skills of students in conducting research

Empowering students to develop research skills

February 8, 2021

This post is republished from   Into Practice ,  a biweekly communication of Harvard’s  Office of the Vice Provost for Advances in Learning

Terence D. Capellini, Richard B Wolf Associate Professor of Human Evolutionary Biology, empowers students to grow as researchers in his Building the Human Body course through a comprehensive, course-long collaborative project that works to understand the changes in the genome that make the human skeleton unique. For instance, of the many types of projects, some focus on the genetic basis of why human beings walk on two legs. This integrative “Evo-Devo” project demands high levels of understanding of biology and genetics that students gain in the first half of class, which is then applied hands-on in the second half of class. Students work in teams of 2-3 to collect their own morphology data by measuring skeletons at the Harvard Museum of Natural History and leverage statistics to understand patterns in their data. They then collect and analyze DNA sequences from humans and other animals to identify the DNA changes that may encode morphology. Throughout this course, students go from sometimes having “limited experience in genetics and/or morphology” to conducting their own independent research. This project culminates in a team presentation and a final research paper.

The benefits: Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges:  Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally.  Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments.  Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line:  Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

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Fostering students’ motivation towards learning research skills: the role of autonomy, competence and relatedness support

Louise maddens.

1 Centre for Instructional Psychology and Technology, Faculty of Psychology and Educational Sciences, KU Leuven and KU Leuven Campus Kulak Kortrijk, Etienne Sabbelaan 51 – bus 7800, 8500 Kortrijk, Belgium

2 Itec, imec Research Group at KU Leuven, imec, Leuven, Belgium

3 Vives University of Applied Sciences, Kortrijk, Belgium

Fien Depaepe

Annelies raes.

In order to design learning environments that foster students’ research skills, one can draw on instructional design models for complex learning, such as the 4C/ID model (in: van Merriënboer and Kirschner, Ten steps to complex learning, Routledge, London, 2018). However, few attempts have been undertaken to foster students’ motivation towards learning complex skills in environments based on the 4C/ID model. This study explores the effects of providing autonomy, competence and relatedness support (in Deci and Ryan, Psychol Inquiry 11(4): 227–268, https://doi.org/10.1207/S15327965PLI1104_01, 2000) in a 4C/ID based online learning environment on upper secondary school behavioral sciences students’ cognitive and motivational outcomes. Students’ cognitive outcomes are measured by means of a research skills test consisting of short multiple choice and short answer items (in order to assess research skills in a broad way), and a research skills task in which students are asked to integrate their skills in writing a research proposal (in order to assess research skills in an integrative manner). Students’ motivational outcomes are measured by means of students’ autonomous and controlled motivation, and students’ amotivation. A pretest-intervention-posttest design was set up in order to compare 233 upper secondary school behavioral sciences students’ outcomes among (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. Both learning environments proved equally effective in improving students’ scores on the research skills test. Students in the need supportive condition scored higher on the research skills task compared to their peers in the baseline condition. Students’ autonomous and controlled motivation were not affected by the intervention. Although, unexpectedly, students’ amotivation increased in both conditions, students’ amotivation was lower in the need supportive condition compared to students in the baseline condition. Theoretical relationships were established between students’ need satisfaction, students’ motivation (autonomous, controlled, and amotivation), and students’ cognitive outcomes. These findings are discussed taking into account the COVID-19 affected setting in which the study took place.

Introduction

Several scholars have argued that the process of learning research skills is often obstructed by motivational problems (Lehti & Lehtinen, 2005 ; Murtonen, 2005 ). Some even describe these issues as students having an aversion towards research (Pietersen, 2002 ). Examples of motivational problems are that students experience research courses as boring, inaccessible, or irrelevant to their daily lives (Braguglia & Jackson, 2012 ). In a research synthesis on teaching and learning research methods, Earley ( 2014 ) argues that students fail to see the relevance of research methods courses, are anxious or nervous about the course, are uninterested and unmotivated to learn the material, and have poor attitudes towards learning research skills. It should be mentioned that the studies mentioned above focused on the field of higher university education. In upper secondary education, to date, students’ motivation towards learning research skills has rarely been studied. As difficulties while learning research seem to relate to problems involving students’ previous experiences regarding learning research skills (Murtonen, 2005 ), we argue that fostering students’ motivation from secondary education onwards is a promising area of research.

The current study combines insights from instructional design theory and self-determination theory (SDT, Deci & Ryan, 2000 ), in order to investigate the cognitive and motivational effects of providing psychological need support (support for the need for autonomy, competence and relatedness) in a 4C/ID based (van Merriënboer & Kirschner, 2018 ) online learning environment fostering upper secondary schools students’ research skills. In the following section, we elaborate on the definition of research skills in the understudied domain of behavioral sciences; on 4C/ID (van Merriënboer & Kirschner, 2018 ) as an instructional design model for complex learning; and on self-determination theory and its related need theory (Deci & Ryan, 2000 ). In addition, the research questions addressed in the current study are outlined.

Conceptual framework

Research skills.

As described by Fischer et al., ( 2014 , p. 29), we define research skills 1 as a broad set of skills used “to understand how scientific knowledge is generated in different scientific disciplines, to evaluate the validity of science-related claims, to assess the relevance of new scientific concepts, methods, and findings, and to generate new knowledge using these concepts and methods”. Furthermore, eight scientific activities learners engage in while performing research are distinguished, namely: (1) problem identification, (2) questioning, (3) hypothesis generation, (4) construction and redesign of artefacts, (5) evidence generation, (6) evidence evaluation, (7) drawing conclusions, and (8) communicating and scrutinizing (Fischer et al., 2014 ). Fischer et al. ( 2014 ) argue that both the nature of, and the weights attributed to each of these activities, differ between domains. Intervention studies aiming to foster research skills are almost exclusively situated in natural sciences domains (Engelmann et al., 2016 ), leaving behavioral sciences domains largely understudied. The current study focuses on research skills in the understudied domain of behavioral sciences. We refer to the domain of behavioral sciences as the study of questions related to how people behave, and why they do so. Human behavior is understood in its broadest sense, and is the study of object in fields of psychology, educational sciences, cultural and social sciences.

The design of the learning environments used in this study is based on an existing instructional design model, namely the 4C/ID model (van Merriënboer & Kirschner, 2018 ). The 4C/ID model has been proven repeatedly effective in fostering complex skills (Costa et al., 2021 ), and thus drew our attention for the case of research skills, as research skills can be considered complex skills (it requires learners to integrate knowledge, skills and attitudes while performing complex learning tasks). Since the 4C/ID model focusses on supporting students’ cognitive outcomes, it might not be considered as relevant from a motivational point of view. However, since we argue that a deliberately designed learning environment from a cognitive point of view is an important prerequisite to provide qualitative motivational support, we briefly sketch the 4C/ID model and its characteristics. The 4C/ID model has a comprehensive character, integrating insights from different theories and models (Merrill, 2002 ), and highlights the relevance of four crucial components: learning tasks, supportive information, part task-practice, and just-in-time information. Central characteristics of these four components are that (a) high variability in authentic learning tasks is needed in order to deal with the complexity of the task; (b) supportive information is provided to the students in order to help them build mental models and strategies for solving the task under study (Cook & McDonald, 2008 ); (c) part-task practice is provided for recurrent skills that need to be automated; and (d) just-in-time (procedural) information is provided for recurrent skills.

Taking into account students’ cognitive struggles regarding research skills, and the existing research on the role of support in fostering research skills (see for example de Jong & van Joolingen, 1998 ), the 4C/ID model was found suitable to design a learning environment for research skills. This is partly because of its inclusion of (almost) all of the support found effective in the literature on research skills, such as providing direct access to domain information at the appropriate moment, providing learners with assignments, including model progression, the importance of students’ involvement in authentic activities, and so on (Chi, 2009 ; de Jong, 2006 ; de Jong & van Joolingen, 1998 ; Engelmann et al., 2016 ). While mainly implemented in vocational oriented programs, the 4C/ID model has been proposed as a good model to design learning environments aiming to foster research skills as well (Bastiaens et al., 2017 ; Maddens et al., 2020b ). Indeed, acquiring research skills requires complex learning processes (such as coordinating different constituent skills). Overall, the 4C/ID model can be considered to be highly suitable for designing learning environments aiming to foster research skills. Given its holistic design approach, it helps “to deal with complexity without losing sight of the interrelationships between the elements taught” (van Merriënboer & Kirschner, 2018 , p. 5).

Although the 4C/ID model has been used widely to construct learning environments enhancing students’ cognitive outcomes (see for example Fischer, 2018 ), research focusing on students’ motivational outcomes related to the 4C/ID model is scarce (van Merriënboer & Kirschner, 2018 ). Van Merriënboer and Kirschner ( 2018 ) suggest self-determination theory (SDT; Deci & Ryan, 2000 ) and its related need theory as a sound theoretical framework to investigate motivation in relation to 4C/ID.

Self-determination theory

Self-determination theory (SDT; Deci & Ryan, 2000 ) provides a broad framework for the study of motivation and distinguishes three types of motivation: amotivation (a lacking ability to self-regulate with respect to a behaviour), extrinsic motivation (extrinsically motivated behaviours, be they self-determined versus controlled), and intrinsic motivation (the ‘highest form’ of self-determined behaviour) (Deci & Ryan, 2000 ). According to Deci and Ryan ( 2000 , p. 237), intrinsic motivation can be considered “a standard against which the qualities of an extrinsically motivated behavior can be compared to determine its degree of self-determination”. Moreover, the authors (Deci & Ryan, 2000 , p. 237) argue that “extrinsic motivation does not typically become intrinsic motivation”. As the current study focuses on research skills in an academic context in which students did not voluntary chose to learn research skills, and thus learning research skills can be considered instrumental (directed to attaining a goal), the current study focuses on students’ amotivation, and students’ extrinsic motivation, realistically striving for the most self-determined types of extrinsic motivation.

Four types of extrinsic motivation are distinguished by SDT (external regulation, introjection, identification, and integration). These types can be categorized in two overarching types of motivation (autonomous and controlled motivation). Autonomous motivation contains the integrated and identified regulation towards a task (be it because the task is considered interesting, or because the task is considered personally relevant respectively). Controlled motivation refers to the external and introjected regulation towards the task (as a consequence of external or internal pressure respectively) (Vansteenkiste et al., 2009 ). More autonomous types of motivation have been found to be related to more positive cognitive and motivational outcomes (Deci & Ryan, 2000 ).

SDT further maintains that one should consider three innate psychological needs related to students’ motivation. These needs are the need for autonomy, the need for competence, and the need for relatedness. The need for autonomy can be described as the need to experience activities as being “concordant with one’s integrated sense of self” (Deci & Ryan, 2000 , p. 231). The need for competence refers to the need to feel effective when dealing with the environment (Deci & Ryan, 2000 ). The need for relatedness contains the need to have close relationships with others, including peers and teachers (Deci & Ryan, 2000 ). The satisfaction of these needs is hypothesized to be related to more internalization, and thus to more autonomous types of motivation (Deci & Ryan, 2000 ). This relationship has been studied frequently (for a recent overview, see Vansteenkiste et al., 2020 ). Indeed, research established the positive relationships between perceived autonomy (see for example Deci et al., 1996 ), perceived competence (see for example Vallerand & Reid, 1984 ), and perceived relatedness (see for example Ryan & Grolnick, 1986 for a self-report based study) with students’ more positive motivational outcomes. Apart from students’ need satisfaction, several scholars also aim to investigate need frustration as a different notion, as “it involves an active threat of the psychological needs (rather than a mere absence of need satisfaction)” (Vansteenkiste et al., 2020 , p. 9). In what follows, possible operationalizations are defined for the three needs.

Possible operationalizations of autonomy need support found in the literature are: teachers accepting irritation or negative feelings related to aspects of a task perceived as “uninteresting” (Reeve, 2006 ; Reeve & Jang, 2006 ; Reeve et al., 2002 ); providing a meaningful rationale in order to explain the value/usefulness of a certain task and stressing why involving in the task is important or why a rule exists (Deci & Ryan, 2000 ); using autonomy-supportive, inviting language (Deci et al., 1996 ); and allowing learners to regulate their own learning and to work at their own pace (Martin et al., 2018 ). Related to competence support, possible operationalizations are: providing a clear task rationale and providing structure (Reeve, 2006 ; Vansteenkiste et al., 2012 ); providing informational positive feedback after a learning activity (Deci et al., 1996 ; Martin et al., 2018 ; Vansteenkiste et al., 2012 ); providing an indication of progress and dividing content into manageable blocks (Martin et al., 2018 ; Schunk, 2003 ); and evaluating performance by means of previously introduced criteria (Ringeisen & Bürgermeister, 2015 ). Possible operationalizations concerning relatedness support are: teacher’s relational supports (Ringeisen & Bürgermeister, 2015 ); encouraging interaction between course participants and providing opportunities for learners to connect with each other (Butz & Stupnisky, 2017 ; van Merriënboer & Kirschner, 2018 ); using a warm and friendly approach or welcoming learners personally into a course (Martin et al., 2018 ); and offering a platform for learners to share ideas and to connect (Butz & Stupnisky, 2017 ; Martin et al., 2018 ).

In the current research, SDT is selected as a theoretical framework to investigate students’ motivation towards learning research skills, as, in contrast to other more purely goal-directed theories, it includes the concept of innate psychological needs or the Basic Psychological Need Theory (Deci & Ryan, 2000 ; Ryan, 1995 ; Vansteenkiste et al., 2020 ), and it describes the relation between these perceived needs and students’ autonomous motivation: higher levels of perceived needs relate to more autonomous forms of motivation. The inclusion of this need theory is considered an advantage in the case of research skills because research revealed problems of students with respect to both their feelings of competence in relation to research skills (Murtonen, 2005 ), as their feelings of autonomy in relation to research skills (Martin et al., 2018 ), as was indicated in the introduction. As such, fostering students’ psychological needs while learning research skills seems a promising way of fostering students’ motivation towards learning research skills.

4C/ID and SDT

One study (Bastiaens et al., 2017 ) was found to implement need support in 4C/ID based learning environments, comparing a traditional module, a 4C/ID based module and an autonomy supportive 4C/ID based module in a vocational undergraduate education context. Autonomy support was operationalized by means of providing choice to the learners. No main effect of the conditions was found on students’ motivation. Surprisingly, providing autonomy support did also not lead to an increase in students’ autonomy satisfaction. Similarly, no effects were found on students’ relatedness and competence satisfaction. Remarkably, students did qualitatively report positive experiences towards the need support, but this did not reflect in their quantitatively reported need experiences. In a previous study performed in the current research trajectory, Maddens et al. ( under review ) investigated the motivational effects of providing autonomy support in a 4C/ID based online learning environment fostering students’ research skills, compared to a learning environment not providing such support. Autonomy support was operationalized as stressing task meaningfulness to the students. Based on insights from self-determination theory, it was hypothesized that students in the autonomy condition would show more positive motivational outcomes compared to students in the baseline condition. However, results showed that students’ motivational outcomes appeared to be unaffected by the autonomy support. One possible explanation for this unexpected finding was that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support (Deci & Ryan, 2000 ; Niemiec & Ryan, 2009 ), and thus, that the intervention was insufficiently powerful for effects to occur. Autonomy support has often been manipulated in experimental research (Deci et al., 1994 ; Reeve et al., 2002 ; Sheldon & Filak, 2008 ). However, the three needs are rarely simultaneously manipulated (Sheldon & Filak, 2008 ).

Integrated need support

Although not making use of 4C/ID based learning environments, some scholars have focused on the impact of integrated (autonomy, competence and relatedness) need support on learners’ motivation. For example, Raes and Schellens ( 2015 ) found differential effects of a need supportive inquiry environment on upper secondary school students’ motivation: positive effects on autonomous motivation were only found in students in a general track, and not in students in a science track. This indicates that motivational effects of need-supportive environments might differ between tracks and disciplines. However, Raes and Schellens ( 2015 ) did not experimentally manipulate need support, as the learning environment was assumed to be need-supportive and was not compared to a non-need supportive learning environment. Pioneers in manipulating competence, relatedness and autonomy support in one study are Sheldon and Filak ( 2008 ), predicting need satisfaction and motivation based on a game-learning experience with introductory psychology students. Relatedness support (mainly operationalized by emphasizing interest in participants’ experiences in a caring way) had a significant effect on intrinsic motivation. Competence support (mainly operationalized by means of explicating positive expectations) had a marginal significant effect on intrinsic motivation. No main effects on intrinsic motivation were found regarding autonomy support (mainly operationalized by means of emphasizing choice, self-direction and participants’ perspective upon the task). However, as is often the case in motivational research based on SDT, the task at hand was quite straight forward (a timed task in which students try to form as many words as possible from a 4 × 4 letter grid), and thus, the applicability of the findings for providing need support in 4C/ID based learning environments for complex learning might be limited.

In the preceding section, several operationalizations of need support were discussed. Deci and Ryan ( 2000 ) argue that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support. However, such integrated need support has rarely been empirically studied (Sheldon & Filak, 2008 ). In addition, research investigating how need support can be implemented in learning environments based on the 4C/ID model is particularly scarce (van Merriënboer & Kirschner, 2018 ). This study aims to combine insights from instructional design theory for complex learning (van Merriënboer & Kirschner, 2018 ) and self-determination theory (Deci & Ryan, 2000 ) in order to investigate the motivational effects of providing need support in a 4C/ID based learning environment for students’ research skills. A pretest-intervention-posttest design is set up in order to compare 233 upper secondary school behavioral sciences students’ cognitive and motivational outcomes among two conditions: (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. The following research questions are answered based on a combination of quantitative and qualitative data (see ‘method’): (1) Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task? ; ( 2) What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes (i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)? ; (3) What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)? ; (4) How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment? .

The first three questions are answered by means of quantitative data. Since the learning environment is constructed in line with existing instructional design principles for complex learning, we hypothesize that both learning environments will succeed in improving students’ research skills (RQ1). Relying on insights from self-determination theory (Deci & Ryan, 2000 ), we hypothesize that providing need support will enhance students’ autonomous motivation (RQ2). In addition, we hypothesize students’ need satisfaction to be positively related to students’ autonomous motivation (RQ3). These hypotheses on the relationship between students’ needs and students’ motivation rely on Vallerands’ ( 1997 ) finding that changes in motivation can be largely explained by students’ perceived competence, autonomy and relatedness (as psychological mediators). More specifically, Vallerand ( 1997 ) argues that environmental factors (in this case the characteristics of a learning environment) influence students’ perceptions of competence, autonomy, and relatedness, which, in turn, influence students’ motivation and other affective outcomes. In addition, based on the self-determination literature (Deci & Ryan, 2000 ), we expect students’ motivation to be positively related to students’ cognitive outcomes. In order to answer the fourth research question, qualitative data (students’ qualitative feedback on the learning environments) is analysed and categorized based on the need satisfaction and need frustration concepts (RQ4) in order to thoroughly capture the meaning of the quantitative results collected in light of RQ1–3. No hypotheses are formulated in this respect.

Methodology

Participants.

The study took place in authentic classroom settings in upper secondary behavioral sciences classes. In total, 233 students from 12 classes from eight schools in Flanders participated in the study. All participants are 11th or 12th grade students in a behavioral sciences track 2 in general upper secondary education in Flanders (Belgium). Classes were randomly assigned to one out of two experimental conditions. Of all 233 students, 105 students (with a mean age of 16.32, SD 0.90) worked in the baseline condition (of which 62% 11th grade students, 36% 12th grade students, and 2% not determined; and of which 31% male, 68% female, and 1% ‘other’), and 128 students (with a mean age of 16.02, SD 0.59) worked in the need supportive condition (of which 80% 11th grade students, and 20% 12th grade students; and of which 19% male, and 81% female). As the current study did not randomly assign students within classes to one out of the two conditions, this study should be considered quasi-experimental. Full randomization was considered but was not feasible as students worked in the learning environments in class, and would potentially notice the experimental differences when observing their peers working in the learning environment. As such, we argued that this would potentially cause bias in the study. By taking into account students’ pretest scores on the relevant variables (cognitive and motivational outcomes) as covariates, we aimed to adjust for inter-conditional differences. No such differences were found for students’ autonomous motivation t (226) =  − 0.115, p  < 0.909, d  = 0.015, and students’ amotivation t (226) =  − 0.658, p  < 0.511, d  =  − 0.088. However, differences were observed for students’ controlled motivation t (226) =  − 2.385, p  < 0.018, d  =  − 0.318, and students’ scores on the LRST pretest t (225) = − 5.200, p  < 0.001, d  =  − 0.695.

Study design and procedure

In a pretest session of maximum two lesson hours, the Leuven Research Skills Test (LRST, Maddens et al., 2020a ), the Academic Self-Regulation Scale (ASRS, Vansteenkiste et al., 2009 ), and four items related to students’ amotivation (Aydin et al., 2014 ) were administered in class via an online questionnaire, under supervision of the teacher. In the subsequent eight weeks, participants worked in the online learning environment, one hour a week. Out of the 233 participating students, 105 students studied in a baseline online learning environment. The baseline online learning environment 3 is systematically designed using existing instructional design principles for complex learning based on the 4C/ID model (van Merriënboer & Kirschner, 2018 ). All four components of the 4C/ID model were taken into account in the design process: regarding the first component, the learning tasks included real-life, authentic cases. More specifically, tasks were selected from the domains of psychology, educational sciences and sociology. As such, there was a large variety in the cases used in the learning tasks. This large variety in learning tasks is expected to facilitate transfer of learners’ research skills in a wide range of contexts. Furthermore, the tasks were ill-structured and required learners to make judgments, in order to provoke deep learning processes. Regarding the second component, supportive information was provided for complex tasks in the learning environment, such as formulating a research question, where students can consult general information on what constitutes a good research question, can consult examples or demonstrations of this general information, and can receive cognitive feedback on their answers (for example by means of example answers). Examples of the implementation of the third component (procedural information) are the provision of information on how to recognize a dependent and an independent variable by means of on-demand (just-in-time) presentation by means of pop-ups; information on how to use Boolean operators; and information on how to read a graph. To avoid split attention, this kind of information was integrated with the task environment itself (van Merriënboer & Kirschner, 2018 ). Finally, the fourth component, part-task-practice (by means of short tests) was implemented for routine aspects of research skills that should be automated, for example the formulation of a search query.

The remaining participating students ( n  = 128) completed an adapted version of the baseline online learning environment, in which autonomy, relatedness and competence support are provided. In total, need support consisted of 12 implementations (four implementations for each need), based on existing research on need support. An overview of these adaptations can be found in Tables ​ Tables1 1 and ​ and2. 2 . Although, ideally, students would work in class, under supervision of their teacher, this was not possible for all classes, due to the COVID-19 restrictions. 4 As a consequence, some students completed the learning environment partly at home. All students were supervised by their teachers (be it virtually or in class), and the researcher kept track of students’ overall activities in order to be able to contact students who did not complete the main activities. During the last two sessions of the intervention, participants submitted a two-pages long research proposal (“two-pager”). One week after the intervention, the LRST (Maddens et al., 2020a ), the ASRS (Vansteenkiste et al., 2009 ), four items related to students’ amotivation (Aydin et al., 2014 ), the value/usefulness scale (Ryan, 1982 ) and the Basic Psychological Need Satisfaction and Frustration Scale (BPNSNF, Chen et al., 2015 ) were administered in a posttest session of maximum two hours. Although most classes succeeded in organizing this posttest session in class, for some classes this posttest was administered at home. However, all classes were supervised by the teacher (be it virtually or in class). These contextual differences at the test moments will be reflected upon in the discussion section.

Adaptations online learning environment

Overview instruments

a When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively

Instruments

In this section, we elaborate on the tests used during the pretest and the posttest. Example items for each scale are presented in Appendix 1.

Motivational outcomes

In the current study, two groups of motivational outcomes are assessed: (1) students’ need satisfaction and frustration, and students’ experiences of value/usefulness; and (2) students’ level of autonomous motivation, controlled motivation, and amotivation. When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively.

The BPNSNF-training scale (The Basic Psychological Need Satisfaction and Frustration Scale, Chen et al., 2015 ; translated version Aelterman et al., 2016 5 ) measured students’ need satisfaction and need frustration while working in the learning environment, and consists of 24 items (four items per scale): (autonomy satisfaction, α  = 0.67; ω = 0.67; autonomy frustration, α  = 0.76; ω = 0.76; relatedness satisfaction, α  = 0.79; ω = 0.79; relatedness frustration, α  = 0.60; ω = 0.61; competence satisfaction, α  = 0.72; ω = 0.73; competence frustration, α  = 0.68; ω = 0.67). The items are Likert-type items ranging from one (not at all true) to five (entirely true). Although the current study focusses mainly on students’ need satisfaction, the scales regarding students’ need frustration are included in order to be able to also detect students’ potential ill-being and in order to detect potential critical issues regarding students’ needs. In addition to the BPNSNF, by means of seven Likert-type items ranging from one (not at all true) to seven (entirely true), the (for the purpose of this research translated) value/usefulness scale of the Intrinsic Motivation Inventory (IMI, Ryan, 1982 ) measured to what extent students valued the activities of the online learning environment ( α  = 0.92; ω = 0.92). Since in the research skills literature problems have been observed related to students’ perceived value/usefulness of research skills (Earley, 2014 ; Murtonen, 2005 ), and this concept is not sufficiently stressed in the BPNSNF-scale, we found it useful to include this value/usefulness scale to the study. The difference in the range of the answer possibilities (one to five vs one to seven) exists because we wanted to keep the range as initially prescribed by the authors of each instrument. All motivational measures are calculated by adding the scores on every item, and dividing this sum score by the number of items on a scale, leading to continuous outcomes. Although the IMI and the BPNSNF targeted students’ experiences while completing the online learning environment, these measures were administered during the posttest. Thus, students had to think retrospectively about their experiences. In order to prevent cognitive overload while completing the online learning environment, these measures were not administered during the intervention itself.

Students’ autonomous and controlled motivation towards learning research skills was measured by means of the Dutch version of the Academic Self-Regulation Scale (ASRS; Vansteenkiste et al., 2009 ), adapted to ‘ research skills ’. The ASRS consists of Likert-type items ranging from one (do not agree at all) to five (totally agree), and contains eight items per subscale (autonomous and controlled motivation). In the autonomous motivation scale, four items are related to identified regulation, and four items are related to intrinsic motivation. 6 In the controlled motivation scale, four items are related to external regulation, and four items are related to introjected regulation. Both scales (autonomous motivation and controlled motivation) indicated good internal consistency for the study’s data (autonomous motivation: α  = 0.91; 0.92; ω = 0.90; 0.92; controlled motivation: α  = 0.83; 0.86; ω = 0.82; 0.85). The items were adapted to the domain under study (motivation to learn about research skills). Based on students’ motivational issues related to research skills, we found it useful to also include a scale to assess students’ amotivation. This was measured with (for the purpose of the current research translated) four items related to students’ amotivation regarding learning research skills, adapted from Academic Motivation Scale for Learning Biology (Aydin et al., 2014 ) ( α  = 0.80; 0.75; ω = 0.81; 0.75). Also this measure consist of Likert-type items ranging from one (do not agree at all) to five (totally agree).

Cognitive outcomes

Students’ research skills proficiency was measured by means of a research skills test (Maddens et al., 2020a ) and a research skills task.

The research skills test used in this study is the LRST (Maddens et al., 2020a ) consisting of a combination of 37 open ended and close ended items ( α  = 0.79; 0.82; ω = 0.78; ω = 0.80 for this data set), administered via an online questionnaire. Each item of the LRST is related to one of the eight epistemic activities regarding research skills as mentioned in the introduction (Fischer et al., 2014 ), and is scored as 0 or 1. The total score on the LRST is calculated by adding the mean subscale scores (related to the eight epistemic activities), and dividing them by eight (the number of scales). In a previous study (Maddens et al., 2020a ), the LRST was checked and found suitable in light of interrater reliability ( κ  = 0.89). As the same researchers assessed the same test with a similar cohort in the current study, the interrater reliability was not calculated for this study.

In the research skills task (“two pager task”), students were asked to write a research proposal of maximum two pages long. The concrete instructions for this research proposal are given in Appendix 1. In this research proposal, students were asked to formulate a research question and its relevance; to explain how they would tackle this research question (method and participants); to explain their hypotheses or expectations; and to explain how they would communicate their results. The two-pager task was analyzed using a pairwise comparison technique, in which four evaluators (i.e. the four authors of this paper) made comparative judgements by comparing two two-pagers at a time, and indicating which two-pager they think is best. All four evaluators are researchers in educational sciences and are familiar with the research project and with assessing students’ texts. This shared understanding and expertise is a prerequisite for obtaining reliable results (Lesterhuis et al., 2018 ). The comparison technique is performed by means of the Comproved tool ( https://comproved.com ). As described by Lesterhuis et al. ( 2018 , p. 18), “the comparative judgement method involves assessing a text on its overall quality. However, instead of requiring an assessor to assign an absolute score to a single text, comparative judgement simplifies the process to a decision about which of two texts is better”. In total, 1635 comparisons were made (each evaluator made 545 comparisons), and this led to a (interrater)reliability score of 0.79. In a next step, these comparative judgements were used to rank the 218 products (15 students did not submit a two-pager) on their quality; and the products were graded based on their ranking. This method was used to grade the two-pagers because it facilitates the holistic evaluation of the tasks, based on the judgement of multiple experts (interrater reliability).

Qualitative feedback

Students’ experiences with the online learning environment were investigated in the online learning environment itself. After completing the learning environment, students were asked how they experienced the tasks, the theory, the opportunity to post answers in the forum and to ask questions via the chat, what they liked or disliked in the online learning environment, and what they disliked in the online learning environment (Fig.  1 ).

Study overview

The first research question (” Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task?” ) is answered by means of a paired samples t -test in order to look for overall improvements in order to detect potential general trends, followed by a full factorial MANCOVA, as this allows us to investigate the effectiveness for both conditions taking into account students’ pretest scores. Hence, the condition is included as an experimental factor, and students’ scores on the LRST and the two-pager task are included as continuous outcome variables. Students’ pretest scores on the LRST are included as a covariate. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariate.

The second research question (“ What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes, i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?”) ;) is answered by means of a full factorial MANCOVA. The condition (need satisfaction condition versus baseline condition) is included as an experimental factor, and students’ responses on the value/usefulness, autonomous and controlled motivation, amotivation, and need satisfaction scales are included as continuous outcome variables. ASRS pretest scores (autonomous and controlled motivation) are included as covariates in order to test the differences between group means, adjusted for students’ a priori motivation. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariates, and assumptions to be met to perform a MANCOVA are checked. 7

The third research question ( “ What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?” ), is initially answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students’ value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables. The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores, and (5) scores on the two-pager task as dependent variables. As a follow-up analysis (see ‘ results ’) two additional regression analyses are performed to look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST (6) and two-pager (7)). As the goal of this analysis is to investigate the relationships between variables as described in SDT research, this analysis focuses on the full sample, rather than distinguishing between the two conditions. An ‘Enter’ method (Field, 2013 ) is used in order to enter the independent variables simultaneously (in line with Sheldon et al., 2008 ).

The fourth research question (“ How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment?” ) is analyzed by means of the knowledge management tool Citavi. Based on the theoretical framework, students’ experiences are labeled by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. For example, students’ quotes referring to the value/usefulness of the learning environment, are labeled as ‘autonomy satisfaction’ or ‘autonomy frustration’. Students’ references towards their feelings of mastery of the learning content are labeled as ‘competence satisfaction’ or ‘competence frustration’. Students’ quotes regarding their relationships with peers and teachers are labeled as ‘relatedness satisfaction’ or ‘relatedness frustration’ (Fig.  2 ).

Overview variables

Does the deliberately designed (4C/ID based) learning environments improve students’ research skills, as measured by a research skills test and a research skills task?

Paired samples t -test. A paired samples t -test reveals that, in general, students ( n  = 210) improved on the LRST-posttest ( M  = 0.57, SD  = 0.16) compared to the pretest ( M  = 0.51, SD  = 0.15) (range 0–1). The difference between the posttest and the pretest is significant t (209) =  − 8.215, p  < 0.001, d 8  =  − 0.567. The correlation between the LRST pretest and posttest is 0.70 ( p  < 0.010).

MANCOVA. A MANCOVA model ( n  = 196) was defined checking for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariate LRST pretest did not show significant interaction effects for the two outcome variables LRST post ( p  = 0.259) and the two-pager task ( p  = 0.702). The correlation between the outcome variables (LRST post and two-pager), is 0.28 ( p  < 0.050).

Of all 233 students, 36 students were excluded from the main analysis because of missing data (for example, because they were absent during a pretest or posttest moment). These students were excluded by means of a listwise deletion method because we found it important to use a complete dataset, since, in a lot of cases, students who did not complete the pretest or posttest, did also not complete the entire learning environment. Including partial data for these students could bias the results. The baseline condition counted 86 students, and the need satisfaction condition counted 111 students. Using Pillai’s Trace [ V  = 0.070, F (2,193) = 7.285, p  ≤ 0.001], there was a significant effect of the condition on the cognitive outcome variables, taking into account students’ LRST pretest scores. Separate univariate ANOVAs on the outcome variables revealed no significant effect of the condition on the LRST posttest measure, F (1,194) = 2.45, p  = 0.120. However, a significant effect of condition was found on the two-pager scores, F (1,194) = 13.69, p  < 0.001 (in the baseline group, the mean score was 6,6/20; in the need condition group, the mean score was 7,6/20). It should be mentioned that both scores are rather low.

What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID based) learning environment fostering students’ research skills, on students’ motivational outcomes (students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?

Paired samples t -tests. The correlations between students’ pretest and posttestscores for the motivational measures are 0.67 ( p  < 0.010) for autonomous motivation; 0.44 ( p  < 0.010) for controlled motivation, and 0.38 for amotivation ( p  < 0.010). Regarding the differences in students’ motivation, three unexpected findings were observed. Overall, students’ ( n  = 215) amotivation was higher on the posttest ( M  = 2.26, SD  = 0.89) compared to the pretest ( M  = 1.77, SD  = 0.79) (based on a score between 1 and 5). The difference between the posttest and the pretest is significant t (214) =  − 7.69, p  < 0.001, d  =  − 0.524. Further analyses learn that the amotivation means in the baseline group increased with 0.65, and the amotivation in the need support group increased with 0.37. In addition, students’ ( n  = 215) autonomous motivation was higher on the pretest ( M  = 2.81, SD  = 0.81) compared to the posttest ( M  = 2.64, SD  = 0.82). The difference between the posttest and the pretest is significant t (214) = 3.72, p  < 0.001, d  = 0.254. Students’ mean scores on autonomous motivation in the baseline condition decreased with 0.19, and students’ autonomous motivation in the need support condition decreased with 0.15. Students’ ( n  = 215) controlled motivation was higher on the posttest ( M  = 2.33, SD  = 0.75) compared to the pretest ( M  = 1.93, SD  = 0.67). The difference between the posttest and the pretest is significant t (214) =  − 07.72, p  < 0.001, d  =  − 0.527. Students’ controlled motivation in the baseline group increased with 0.36, and students’ controlled motivation in the need support group increased with 0.43. However, overall, all mean scores are and stay below neutral score (below 3), indicating robust low autonomous, controlled and amotivation scores (see Table ​ Table3). 3 ). An independent samples T -test on the mean differences between these measures shows that the increases/decreases on autonomous motivation [ t (213) =  − 0.506, p  = 0.613, d  =  − 0.069] and controlled motivation [ t (213) =  − 0.656, p  = 0.513, d  =  − 0.090] did not differ between the two groups. However, the increases in amotivation [ t (213) = 2.196, p  = 0.029, d  = 0.301] does differ significantly between the two conditions. More specifically, the increase was lower in the need supportive condition compared to the baseline condition.

Mean scores and standard deviations motivational variables

a Overall, students’ ( n  = 215) autonomous motivation was significantly higher on the pretest compared to the posttest ( t (214) 3.72, p  ≤ 0.001, d  = 0.254

b Students’ (n = 215) controlled motivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 7.72, p  ≤ 0.001, d  =  − 0.527

c Students’ ( n  = 215) amotivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 07,69, p  ≤ 0.001, d  =  − 0.534)

MANCOVA. Of all 233 students, 18 students were excluded from the analysis because of missing data (for example, because they were absent during a pretest or posttest moment). Compared to the cognitive analyses, the amount of missing data is lower concerning motivational outcomes since, concerning the cognitive outcomes, some students did not complete the two-pager task. However, we found it important to use all relevant data and chose to report this is in a clear way. In total, the baseline condition counted 97 students, and the experimental condition counted 118 students. Similar to the analysis for the cognitive outcomes, a MANCOVA model was defined to check for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariates did not show significant interaction effects for the outcome variables. 9

Using Pillai’s Trace [ V  = 0.113, F (10,201) = 2.558, p  = 0.006], there was a significant effect of condition on the motivational variables, taking into account students’ autonomous and controlled pretest scores, and students’ a priori amotivation. Separate univariate ANOVAs on the outcome variables revealed a significant effect of the condition on the outcome variables amotivation, F (1,210) = 3.98, p  = 0.047; and relatedness satisfaction F (1,210) = 6.41, p  = 0.012. As was hypothesized, students in the need satisfaction group reported less amotivation ( M  = 2.38), compared to students in the baseline group ( M  = 2.18). In contrast to what was hypothesized, students in the need satisfaction group reported less relatedness satisfaction ( M  = 2.43) compared to students in the baseline group ( M  = 2.73), and no significant effects of condition were found on the outcome variables autonomous motivation post, controlled motivation post, value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, and relatedness frustration. Table ​ Table4 4 shows the correlations between the motivational outcome variables.

Correlations motivational outcome variables

AM autonomous motivation, CM controlled motivation, AMOT amotivation, VU value/usefulness, AS autonomy satisfaction, AF autonomy frustration, CS competence satisfaction, CF competence frustration, RS relatedness satisfaction, RF relatedness frustration

**Correlation is significant at the 0.010 level (2-tailed)

*Correlation is significant at the 0.050 level (2-tailed)

What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?

The third research question (investigating the relationships between students’ need satisfaction, students’ motivation and students’ cognitive outcomes), is answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables ( n  = 219). The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores ( n  = 215), and (5) scores on the two-pager task as dependent variables ( n  = 206). Table ​ Table4 4 depicts the correlations for the first three analyses. Table ​ Table5 5 depicts the correlations for the last two analyses.

Correlations motivational and cognitive outcome variables

AM  autonomous motivation, CM  controlled motivation, AMOT  amotivation, LRST  score on LRST, Twopager  score on Twopager

In Table ​ Table3, 3 , we can see that students in both conditions experience average competence and autonomy satisfaction. However, students’ relatedness satisfaction seems low in both conditions. This finding will be further discussed in the discussion section. For autonomous motivation, a significant regression equation was found F (7,211) = 37.453, p  < 0.001. The regression analysis (see Table ​ Table5) 5 ) further reveals that all three satisfaction scores (competence satisfaction, relatedness satisfaction and autonomy satisfaction) contribute positively to students’ autonomous motivation, as does students’ experienced value/usefulness. Also for students’ controlled motivation a significant regression equation was found F (7,211) = 8.236, p  < 0.001, with students’ autonomy frustration and students’ relatedness satisfaction contributing to students’ controlled motivation. The aforementioned relationships are in line with the expectations. However, we noticed that relatedness satisfaction contributed to students’ controlled motivation in the opposite direction of what was expected (the higher students’ relatedness satisfaction, the lower students’ controlled motivation). This finding will be reflected upon in the discussion section. Also for students’ amotivation, a significant regression equation was found F (7,211) = 7.913, p  < 0.001. Students’ autonomy frustration, competence frustration and students’ value/usefulness contributed to students’ amotivation in an expected way. Also for cognitive outcomes related to the research skills test, a significant regression equation was found F (3,211) = 8.351, p  < 0.001. In line with the expectations, the regression analysis revealed that the higher students’ amotivation, the lower students’ scores on the research skills test. No significant regression equation was found for the outcome variable related to the research skills task F (3,202) = 0.954, p  < 0.416. For all regression equations, the R 2 and the exact regression weights are presented in Table ​ Table6 6 .

Linear model of predictors of autonomous motivation, controlled motivation, amotivation, LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

*Significant at .050 level

As a follow-up analysis and in order to better understand the outcomes, we decided to also look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST and two-pager) by means of two additional regression analyses. The motivation behind this decision relates to possible issues regarding the motivational measures used, which might complicate the investigation of indirect relationships (see discussion). The results are provided in Table ​ Table7, 7 , and show that both for the LRST and the two-pager, respectively, a significant [ F (7,207) = 4.252, p  < 0.001] and marginally significant regression weight [ F (7,199) = 2.029, p  = 0.053] was found. More specifically, students’ relatedness satisfaction and students’ perceived value/usefulness contribute to students’ scores on the two-pager and on the research skills test. As one would expect, we see that the higher students’ value/usefulness, the higher students’ scores on both cognitive outcomes. In contrast to one would expect, we found that the higher students’ relatedness satisfaction, the lower students’ scores on the cognitive outcomes. These findings are reflected upon in the discussion section.

Linear model of predictors of LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID based) learning environment?

As was mentioned in the method section, the fourth research question was analysed by labelling students’ qualitative feedback by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. By means of this approach, we could analyse students’ need experiences in a fine grained manner. When students’ quotes were applicable to more than one code, they were labelled with different codes. In what follows, students’ quotes are indicated with the codes “BC” (baseline condition) or “NSC” (need satisfaction condition) in order to indicate which learning environment the student completed. Of all 233 students, 124 students provided qualitative feedback (44 in BC and 80 in NSC). In total, 266 quotes were labeled. Autonomy satisfaction was coded 40 times BC and 41 times in NSC; autonomy frustration was coded 13 times in BC and four times in NSC; competence satisfaction was coded 28 times in BC and 34 times in NSC; competence frustration was coded 31 times in BC and 27 times in NSC; relatedness satisfaction was coded 10 times in BC and 16 times in NSC; and relatedness frustration was coded five times in BC and 17 times in NSC. Several observations could be drawn from the qualitative data.

Related to autonomy satisfaction , in both conditions, several students explicitly mentioned the personal value and usefulness of what they had learned in the learning environment. While in the baseline condition, these references were often vague (“Now I know what people expect from me next year ”; “I think I might use this information in the future ”); some references appeared to be more specific in the need support condition (“I want to study psychology and I think I can use this information!”; “This is a good preparation for higher education and university ”; “I can use this information to write an essay ”; “I think the theory was interesting, because you are sure you will need it once. I don’t always have that feeling during a normal lesson in school”). In addition, students in both conditions mentioned that they found the material interesting, and that they appreciated the online format: “It’s different then just listening to a teacher, I kept interested because of the large variety in exercises and overall, I found it fun” (NSC).

Several comments were coded as ‘ autonomy frustration’ in both conditions. Some students indicated that they found the material “useless” (BC), or that “they did not remember that much” (BC). Others found the material “uninteresting” (BC), “heavy and boring” (NSC) or “not fun” (BC). In addition, some students “did not like to complete the assignments” (NSC), or “prefer a book to learn theory” (NSC).

Related to competence satisfaction , students in both conditions found the material “clear” (BC, NSC). In addition, students’ appreciated the example answers, the difficulty rate (“Some exercises were hard, but that is good. That’s a sign you’re learning something new” (NSC)), and the fact that the theory was segmented in several parts. In addition, students recognized that the material required complex skills: “I learned a lot, you had to think deeper or gain insights in order to solve the exercises” (NSC), “you really had to think to complete the exercises” (NSC). In the need satisfaction group, several quotes were labelled related to the specific need support provided. For example, students indicated that they appreciated the forum option: “If something was not clear, you could check your peer’s answers” (NSC). Students also valued the fact that they could work at their own pace: “I found it very good that we could solve everything at our own pace” (NSC); “good that you could choose your own pace, and if something was not clear to you, you could reread it at your own pace” (NSC). In addition, students appreciated the immediate feedback provided by the researcher “I found it very good that we received personal feedback from xxx (name researcher). That way, I knew whether I understood the theory correctly” (NSC); and the fact that they could indicate their progress “It was good that you could see how far you proceeded in the learning environment” (NSC).

In both the baseline and the need supportive condition, there were also several comments related to competence frustration . For example, students found exercises vague, unclear or too difficult. While students, overall, understood the theory provided, applying the theory to an integrative assignment appears to be very difficult: “I did understand the several parts of the learning environment, but I did not succeed in writing a research proposal myself” (NSC). “I just found it hard to respond to questions. When I had to write my two-pager research proposal, I really struggled. I really felt like I was doing it entirely wrong” (NSC)). In addition, a lot comments related to the fact that the theory was a lot to process in a short time frame, and therefore, students indicated that it was hard to remember all the theory provided. In addition, this led pressure in some students: “Sometimes, I experiences pressure. When you see that your peers are finished, you automatically start working faster.” (BC).

Concerning relatedness satisfaction , in the baseline condition, students appreciated the chat function “you could help each other and it was interesting to hear each other’s opinions about the topics we were working on” (BC). However, most students indicated that they did not make use of the chat or forum options. In the need satisfaction condition, students appreciated the forum and the chat function: “You knew you could always ask questions. This helped to process the learning material” (NSC), “My peers’ answers inspired me” (NSC), “Thanks to the chat function, I felt more connected to my peers” (NSC). In addition, students in the need satisfaction condition appreciated the fact that they could contact the researcher any time.

Several students made comments related to relatedness frustration . In both groups, students missed the ‘live teaching’: “I tried my best, but sometimes I did not like it, because you do not receive the information in ‘real time’, but through videos” (BC). In addition, students missed their peers: “We had to complete the environment individually” (BC). While some students appreciated the opportunity of a forum, other students found this possibility stressful: “I think the forum is very scary. I posted everything I had to, but I found it very scary that everyone can see what you post” (NSC). Others did not like the fact that they needed to work individually: “Sometimes I lost my attention because no one was watching my screen with me” (NSC); “I found it hard because this was new information and we could not discuss it with each other” (NSC); “I felt lonely” (NSC); “It is hard to complete exercises without the help of a teacher. In the future this will happen more often, so I guess I will have to get used to it” (NSC); “When I see the teacher physically, I feel less reluctant to ask questions” (NSC).

The current intervention study aimed at exploring the motivational and cognitive effects of providing need support in an online learning environment fostering upper secondary school students’ research skills. More specifically, we investigated the impact of autonomy, competence and relatedness support in an online learning environment on students’ scores on a research skills test, a research skills task, students’ autonomous motivation, controlled motivation, amotivation, need satisfaction, need frustration, and experienced value/usefulness. Adopting a pretest-intervention-posttest design approach, 233 upper secondary school behavioral sciences students’ motivational outcomes were compared among two conditions: (1) a 4C/ID inspired online learning environment condition (baseline condition), and (2) a condition with an identical online learning environment additively providing support for students’ autonomy, relatedness and competence need satisfaction (need supportive condition). This study aims to contribute to the literature by exploring the integration of need support for all three needs (the need for competence, relatedness and autonomy) in an ecologically valid setting. In what follows, the findings are discussed taking into account the COVID-19 affected circumstances in which the study took place.

As was hypothesized based on existing research (Costa et al., 2021 ), results showed significant learning gains on the LRST cognitive measure in both conditions, pointing out that the learning environments in general succeeded in improving students’ research skills. The current study did not find any significant differences in these learning gains between both conditions. Controlling for a priori differences between the conditions on the LRST pretest measure, students in the need support condition did exceed students in the baseline condition on the two-pager task. However, overall, the scores on the research skills task were quite low, pointing to the fact that students still seem to struggle in writing a research proposal. This task can be considered more complex (van Merriënboer & Kirschner, 2018 ) than the research skills test, as students are required to combine their conceptual and procedural knowledge in one assignment. Indeed, in the qualitative feedback, students indicate that they understand the theory and are able to apply the theory in basic exercises, but that they struggle in integrating their knowledge in a research proposal. Future research could set up more extensive interventions explicitly targeting students’ progress while writing a research proposal, for example using development portfolios (van Merriënboer et al., 2006 ).

The effect of the intervention on the motivational outcome measures was investigated. Since we experimentally manipulated need support, this study hypothesized that students in the need supportive condition would show higher scores for autonomous motivation, value/usefulness and need satisfaction; and lower scores for controlled motivation, amotivation and need frustration compared to students in the baseline condition (Deci & Ryan, 2000 ). However, the analyses showed that students in the conditions did not differ on the value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration and relatedness frustration measures. In contrast to what was hypothesized, students’ in the baseline condition reported higher relatedness satisfaction compared to students in the need supportive condition. No differences were found in students’ autonomous motivation and controlled motivation. However, as was expected, students in the need supportive conditions did report lower levels of amotivation compared to students in the baseline condition. Still, for the current study, one could question the role of the need support in this respect, as the current intervention did not succeed in manipulating students’ need experiences. In what follows, possible explanations for these findings are outlined in light of the existing literature.

Need experiences

A first observation based on the findings as described above is that the intervention did not succeed in manipulating students’ need satisfaction, need frustration and value/usefulness in an expected way. One effect was found of condition on relatedness satisfaction, but in the opposite direction of what was expected. We did not find a conclusive explanation for this unanticipated finding, but we do argue that the COVID-19 related measures at play during the intervention could have impacted this result. This will be reflected upon later in this discussion (limitations). In both conditions, students seem to be averagely satisfied regarding autonomy and competence in the 4C/ID based learning environments. This might be explained by the fact that 4C/ID based learning environments inherently foster students’ perceived competence because of the attention for structure and guidance, and the fact that the use of authentic tasks can be considered autonomy supportive (Bastiaens & Martens, 2007). However, we see that students experience low relatedness satisfaction in both conditions. The fact that the learning environment was organized entirely online might have influenced this result. While one might also partly address this low relatedness satisfaction to the COVID-19 circumstances at play during the study, this hypothetical explanation does not hold entirely since also in a previous non COVID-affected study in this research trajectory (Maddens et al., under review ), students’ relatedness satisfaction was found to be low. This finding, combined with findings from students’ qualitative feedback clearly indicating relatedness frustration, we argue that future research could focus on the question as how to provide need for relatedness support in 4C/ID based learning environments. On a more general level, this raises the question how opportunities for discussions and collaboration can be included in 4C/ID based learning environments. For example, organizing ‘real classroom interactions’ or performing assignments in groups (see also the suggestion of van Merriënboer & Kirschner, 2018 ), might be important in fostering students’ relatedness satisfaction (Salomon, 2002 ) . As argued by Wang et al. ( 2019 ), relatedness support is clearly understudied, for a long time often even ignored, in the SDT literature. Recently, relatedness is beginning to receive more attention, and has been found a strong predictor of autonomous motivation in the classroom (Wang et al., 2019 ).

Possibly, the need support provided in the learning environment was insufficient or inadequate to foster students’ need experiences. However, as the implementations were based on the existing literature (Deci & Ryan, 2000 ), this finding can be considered surprising. In addition, we derive from the qualitative feedback that students seem to value the need support provided in the learning environment. These contradictory observations are in line with previous research (Bastiaens et al., 2017 ), and call for further investigation.

Autonomous motivation, controlled motivation, amotivation

A second observation is that, in both conditions, students seem to hold low autonomous motivation and low controlled motivation towards learning research. On average, also students’ amotivation is low. The fact that students are not amotivated to learn about research can be considered reassuring. However, the fact that students experience low autonomous motivation causes concerns, as we know this might negatively impact their learning behavior and intentions to learn (Deci & Ryan, 2000 ; Wang et al., 2019 ). However, this result is based on mean scores. Future research might look at these results at student level, in order to identify individual motivational profiles (Vansteenkiste et al., 2009 ) and their prevalence in upper secondary behavioral sciences education.

A third observation is that students’ autonomous and controlled motivation were not affected by the intervention. Since the intervention did not succeed in manipulating students’ need experiences, this finding is not surprising. In addition, this is in line with Bastiaens et al.’ ( 2017 ) study, not finding motivational effects of providing need support in 4C/ID based learning environments. However, the current study did confirm that—although still higher than at pretest level, see below—students in the need supportive condition reported lower amotivation compared to students in the baseline condition. As no amotivational differences were observed at pretest level, this might indicate that students’ self-reported motivation (autonomous and controlled motivation) and/or needs do not align with students’ experienced motivation and needs. As was mentioned, this calls for further research.

Theoretical relationships

In line with previous research (Wang et al., 2019 ), multiple regression analyses revealed that students’ need satisfaction (on all three measures) contributed positively to students’ autonomous motivation. In addition, also students’ perceived value/usefulness contributed positively to students’ autonomous motivation. Students’ competence frustration and autonomy frustration contributed positively to students’ amotivation, and students’ value/usefulness contributed negatively to students’ amotivation. Students’ autonomy frustration contributed positively to students’ controlled motivation. While all the aforementioned relationships are in line with the expectations (Deci & Ryan, 2000 ; Wang et al., 2019 ), an unexpected finding is that students’ relatedness satisfaction contributed positively to students’ controlled motivation. This contradicts previous research (Wang et al., 2019 ), reporting that relatedness contributes to controlled motivation negatively. However, previous research (Wang et al., 2019 ) did find controlled motivation to be positively related to pressure . Although we did not find a conclusive explanation for this unanticipated finding, one possible reason thus is that students who contacted their peers in the online learning environment (and thus felt more related to their peers), might have experienced pressure because they felt like their peers worked faster or in a different way. Indeed, in the qualitative feedback, we noticed that some students indicated they ‘rushed’ through the online learning environment because they noticed a peer working faster. This finding calls for further research.

Overall, the results indicate that the observed need variables contributed most to students’ autonomous motivation, compared to (reversed relationships in) students’ amotivation and students’ controlled motivation. As such, when targeting students’ motivation, fostering students’ autonomous motivation based on students’ need experiences seems most promising. This is in line with previous research (Wang et al., 2019 ) reporting high correlations between students’ needs and students’ autonomous motivation, compared to students’ controlled motivation. We also investigated the relationships between students’ motivation and students’ cognitive outcomes. In line with a previously conducted study in this research trajectory (Maddens et al., under review ), but in contrast to what was hypothesized based on the existing literature (Deci & Ryan, 2000 ; Grolnick et al., 1991 ; Reeve, 2006 ) we found that nor students’ autonomous motivation, nor students’ controlled motivation contributed to students’ scores on the research skills test. However, we did find that students’ amotivation contributed negatively to students’ LRST scores. As such, when targeting students’ cognitive outcomes in educational programs, one might pay explicit attention to preventing amotivation. This is in line with previous research conducted in other domains, reporting that amotivation plays an important role in predicting mathematics achievement (Leroy & Bressoux, 2016 ), while this relationship was not found in other motivation types. Related to research skills, the current research suggests that preventing competence frustration and autonomy frustration, and fostering students’ experiences of value/usefulness might be especially promising to reach this goal.

Initially, we did not plan any analyses investigating the direct relationships between students’ needs and students’ cognitive outcomes, partly because previous research (Vallerand & Losier, 1999 ) suggests that the relationships between need satisfaction and (cognitive) outcomes are mediated by the types of motivation. To this end, we investigated the relationships between students’ needs and students’ motivation, separately from the relationships between students’ motivation and students’ cognitive outcomes. However, because of potential issues with the motivational measures (see earlier), which possibly hampers the interpretation of the relationships between students’ needs, students’ motivation, and students’ cognitive outcomes, we decided to also directly assess the regression weights of students’ needs and students’ perceived value/usefulness, on students’ cognitive outcomes. Results revealed that, in line with the expectations, students’ perceived value/usefulness contributed positively to students’ LRST scores and two-pager scores, which potentially stresses the importance of value/usefulness, not only for motivational purposes, but also for cognitive purposes. This is in line with previous research (Assor et al., 2002 ), establishing relationships between fostering relevance and students’ behavioral and cognitive engagement (which potentially leads to better cognitive outcomes). In contrast to the expectations, students’ relatedness satisfaction was found to be negatively related to students’ scores on the LRST and the two-pager. However, again, this surprising finding is best interpreted in light of the COVID-10 pandemic (see earlier).

Limitations

This study faced some reliability issues given the time frame in which the study took place. Due to the COVID-19-restrictions at play at the time of study, the study plan needed to be revised several times in collaboration with teachers in order to be able to complete the interventions. In addition, it is very likely that students’ motivation (and relatedness satisfaction) was influenced by the COVID 19-restrictions. For example, due to the restrictions, in the last phase of the intervention, students could only be present at school halftime, and therefore, some students worked from home while others worked in the classroom. In the qualitative feedback, students reported several COVID-19 related frustrations (it was too cold in class because teachers were obligated to open the windows; students needed to frequently disinfect their computers…). Also the teachers mentioned that students suffered from low well-being during the COVID-19 time frame (see further), and as such, this affected their motivation. Although all efforts were undertaken in order for the study to take place as controlled as possible, results should be interpreted in light of this time frame. The impact of the COVID-19 pandemic on students’ self-reported motivation has been established in recent research (Daniels et al., 2021 ). Overall, one could question to what extent we can expect an intervention at microlevel (manipulating need support in learning environments) to work, when the study takes place in a time frame where students’ need experiences are seriously threatened by the circumstances.

Decreasing motivation

Students’ motivation evolved in a non-desirable way in both conditions. This unexpected finding (decreasing motivation) might be explained by four possible reasons: a first explanation is that asking students to fill out the same questionnaire at posttest and pretest level might lead to frustration and lower reported motivation (Kosovich et al., 2017 ). Indeed, students spent a lot of time working in the online learning environment, so filling out another motivational questionnaire on top of the intervention might have added to the frustration (Kosovich et al., 2017 ). A second explanation is that students’ motivation naturally declines over time (which is a common finding in the motivational literature, Kosovich et al., 2017 ). A third explanation is that students, indeed, felt less motivated towards research skills after having completed the online learning environment. For example, the qualitative data indicated that a lot of students acknowledged the fact that the learning environment was useful, but that personally, they were not interested in learning the material. In addition, students indicated that the learning material was a lot to process in a short time frame, and was new to them, which might have negatively impacted their motivation. The latter (students indicating that the learning material was extensive) might indicate that students experienced high cognitive load (Paas & van Merriënboer, 1994; Sweller et al., 1994 ) while completing the learning environment. A fourth explanation is that, due to the COVID19-restrictions, students lost motivation during the learning process. A post-intervention survey in which we asked teachers about the impact of the COVID-19 restrictions on students’ motivation indicated that some students experienced low well-being during the COVID-19 pandemic, and thus, this might have hampered their motivation to learn. In addition, a teacher mentioned that COVID-19 in general was very demotivating for the students, and that students had troubles concentrating due to the fact they felt isolated. As was mentioned, the impact of COVID-19 on students’ motivation has been well described in the literature (Daniels et al., 2021 ). Although, in the current study, we cannot prove the impact of these measures on students’ motivation specifically towards learning research skills, it is important to take this context into account when interpreting the results.

Students’ learning behavior

Based on students’ qualitative feedback, we have reasons to believe that students did not always work in the learning environment as we would want them to do. Thus, students did not interact with the need support in the intended way (‘instructional disobedient behavior’: Elen, 2020 ). For example, several students reported that they did not always read all the material, did not make use of the forum, or did not notice certain messages from the researcher. However, the current research did not specifically look into students’ learning behavior in the learning environment. In learning environments organized online, future researchers might want to investigate students’ online behavior in order to gain insights in students’ interactions with the learning environment.

This study aims to contribute to theory and practice. Firstly, this study defines the 4C/ID model (van Merriënboer & Kirschner, 2018 ) as a good theoretical framework in order to design learning environments aiming to foster students’ research skills. However, this study also points to students’ struggling in writing a research proposal, which might lead to more specific intervention studies especially focussing on monitoring students’ progress while performing such tasks. Secondly, this study clearly elaborates on the operationalizations of need support used, and as such, might inform instructional designers in order to implement need support in an integrated manner (including competence, relatedness and autonomy support). Future interventions might want to track and monitor students’ learning behavior in order for students to interact with the learning environment as expected (Elen, 2020 ). Thirdly, this study established theoretical relationships between students’ needs, motivation and cognitive outcomes, which might be useful information for researchers aiming to investigate students’ motivation towards learning research skills in the future. Based on the findings, future researchers might especially involve in research fostering students’ autonomous motivation by means of providing need support; and avoiding students’ amotivation in order to enhance students’ cognitive outcomes. Suggestions are made based on the need support and frustration measures relating to these motivational and cognitive outcomes. For example, fostering students’ value/usefulness seems promising for both cognitive and motivational outcomes. Fourthly, although we did not succeed in manipulating students’ need experiences, we did gain insights in students’ experiences with the need support by means of the qualitative data. For example, the irreplaceable role of teachers in motivating students has been exposed. This study can be considered innovative because of its aim to inspect both students’ cognitive and motivational outcomes after completing a 4C/ID based educational program (van Merriënboer & Kirschner, 2018 ). In addition, this study implements integrated need support rather than focusing on a single need (Deci & Ryan, 2000 ; Sheldon & Filak, 2008 ).

Acknowledgements

This study was carried out within imec’s Smart Education research programme, with support from the Flemish government.

Appendix: Overview test instruments

• Instructions 2-pager (Maddens, Depaepe, Raes, & Elen, under review)

Write a research proposal for a fictional study.

In a Word-document of maximum two pages…

• You describe a research question and the importance of this research question
• You explain how you would answer this research question (manner of data collection and target group)
• You explain what your expectations are, and how you will report your results.

To do so, you receive 2 hours.

Post your research proposal here.

Good luck and thank you for your activity in the RISSC-environment!

Declarations

The authors declare that they have no conflict of interest.

All ethical and GDPR-related guidelines were followed as required for conducting human research and were approved by SMEC (Social and Societal Ethics Committee).

1 Fischer et al. ( 2014 ) refer to these research skills as scientific reasoning skills.

2 In Flanders, during the time of study, four different types of education are offered from the second stage of secondary education onwards (EACEA, 2018) (general secondary education, technical secondary education, secondary education in the arts and vocational secondary education). Behavioral sciences is a track in general secondary education.

3 For a complete overview on the design and the evaluation of this learning environment, see Maddens et al ( 2020b ).

4 During the time of study, the COVID-19 restrictions became more strict: students in upper secondary education could only come to school half of the time. Therefore, some students completed the last modules of the learning environment at home.

5 The BPNSNF-training scale is initially constructed to evaluate motivation related to workshops. The phrasing was adjusted slightly in order for the suitability for the current study. For example, we changed the wording ‘during the past workshop…’ to ‘while completing the online learning environment…’.

6 In the current study, we would label the items categorized as ‘intrinsic motivation’ in ASRS (finding something interesting, fun, fascinating or a pleasant activity) as ‘integration’. In SDT (Deci & Ryan, 2000 ; Deci et al., 2017 ), integration is described as being “fully volitional”, or “wholeheartedly engaged”, and it is argued that fully internalized extrinsic motivation does not typically become intrinsic motivation, but rather remains extrinsic even though fully volitional (because it is still instrumental). In the context of the current study, in which students learn about research skills because this is instructed (thus, out of instrumental motivations), we think that the term integration is more applicable than pure intrinsic motivation in self-initiated contexts (which can be observed for example in children’s play or in sports).

7 Levene’s test for homogeneity of variances was significant for the outcome “two-pager”. However, we continued with the analyses since the treatment group sizes are roughly equal, and thus, the assumption of homogeneity of variances does not need to be considered (Field, 2013 ). Levene’s test for homogeneity of variances was non-significant for all the other outcome measures.

8 Cohen’s D is calculated in SPSS by means of the formula: D = M 1 - M 2 Sp

Condition x autonomous motivation pretest Value/usefulness: p  = 0.251; autonomous motivation: p  = 0.269; controlled motivation: p  = 0.457; amotivation: p  = 0.219; autonomy satisfaction: p  = 0.794; autonomy frustration: p  = 0.096; competence satisfaction: p  = 0.682; competence frustration: p  = 0.699; relatedness satisfaction: p  = 0.943; relatedness frustration: p  = 0.870.

Condition x controlled motivation pretest Value/usefulness: p  = 0.882; autonomous motivation: p  = 0.270; controlled motivation: p  = 0.782; amotivation: p  = 0.940; autonomy satisfaction: p  = 0.815; autonomy frustration: p  = 0.737; competence satisfaction: p  = 0.649; competence frustration: p  = 0.505; relatedness satisfaction: p  = 0.625; relatedness frustration: p  = 0.741.

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Educational resources and simple solutions for your research journey

Learning in Research: Importance of Building Research Skills for Students

Learning in research is a fundamental aspect of academic progress, and it plays a vital role in the success of researchers. Science and technology are developing at an unprecedented rate, with new discoveries and advancements being made every day. This makes it crucial for researchers to continuously enhance their research skills and stay ahead of the curve. Lifelong learning , which refers to the ongoing pursuit of knowledge throughout one’s career, is indispensable to thrive in your field. This article explores the importance of learning in research and outlines the benefits of building research skills for students with tailormade courses for researchers .  

Table of Contents

Learning in research and academic progress  

Research is not for the faint of heart. More so when you’re starting out. PhD students need to take care of multiple things in limited time – conducting research, completing their course work, attending classes, and building your network. You also need to keep up with the new research methodologies, technologies, and paradigms as they develop. In this scenario, it’s easy to doubt yourself and wonder if you even belong on academia. Focusing on continued learning in research is one way to deal with these imposter feelings and continue on your path to success. There are many advantages in adding to and polishing research skills for students . We’ve listed the benefits of lifelong learning in research that not only help you build a solid foundation of knowledge but also enables you to explore new avenues and contribute to your specific fields of study.   

Benefits of lifelong learning in research  

Continuously honing research skills offers numerous benefits to researchers, particularly students who are embarking on their academic journeys. Here are some key advantages to restoring your focus on learning in research :  

• Professional growth: Researchers who fail to keep up with the latest trends risk being left behind. Learning in research fosters personal and professional growth, empowering researchers to expand their knowledge base and develop their expertise. By acquiring new research skills for students and researchers, you can undertake more complex projects, produce high-quality work, and gain recognition in your field. Lifelong learning ensures you stay ahead of the race in a highly competitive environment, which allows you to secure better professional opportunities to advance your career.
• Enhanced problem-solving: Research often involves tackling complex problems. Learning in research helps to expand your horizons, explore new areas of interest, and broaden your knowledge base so you can develop pioneering solutions for scientific problems. Lifelong learning also enhances critical thinking and problem-solving abilities, enabling researchers to approach challenges from multiple perspectives. By taking up courses for researchers and acquiring a diverse set of critical skills, researchers can develop innovative solutions to complex problems.

• Adaptability: In a continually evolving research landscape, being adaptable is crucial for success. Continual learning in research equips you to navigate challenges, embrace change, and quickly adapt to new methodologies, technologies, and trends to ensure your research remains relevant and impactful. Moreover, being open to exploring a broader range of resources and tools allows you to widen your options, adopt the best suited options for your research, and keep you moving ahead in your career.
• Networking opportunities: Lifelong learning also creates opportunities for researchers to connect and collaborate with peers, experts, and mentors. Through workshops, conferences, and online platforms, you get to exchange ideas, gain valuable insights, and forge connections with peers around the world. Being seen as an expert, who focuses on learning in research , makes you more sought after for research collaborations than those who lag behind in their understanding of current developments
• Confidence in knowledge : Lifelong learning keeps you aware of the latest developments, allowing you to apply new online tools, innovative technologies, and varied approaches to your own work. Those who keep learning in research are typically more confident about their work and are able to pursue topics even outside their area of expertise. Not only does this give you a sense of personal fulfilment, it increases your chances of faster career growth and advancement.

How to continue learning in research  

Researcher.Life’s R Upskill , with more than 120 courses for researchers , is a great place to start your journey of lifelong learning . You can choose from top researcher skill courses and enhance your expertise in scientific writing, data analysis, project management, peer review, and scientific communication among others. Helmed by industry and academic experts, these courses are designed to help researchers improve existing skills and develop new capabilities that will help them advance in their careers. With simple explanations of complex processes, bite-sized modules, and flexible learning options, the platform allows researchers to learn at their own pace, from anywhere in the world. So commit to lifelong learning – sign up for Researcher.Life now to get free access to 20 handpicked courses for researchers!

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

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Ten tips to help students develop better research skills

• 30 August 2019
• 9 minute read

By Chia Suan Chong for EtonX

The skill of conducting research is an extremely useful life skill that can help students gather and analyse information, build knowledge, think critically and exercise their mind. It is a skill that benefits students beyond their academic life and enables students to understand the world around them better.

The wealth of information available to us and the ease of accessing it via our phones or laptops may make research seem like a straightforward task. But the sheer volume of sources and the dangers of fake news and media misrepresentation require students to develop the right skills to find what they are looking for. By teaching students to plan their research and judiciously consider the information they get, students can become better decision makers and influencers who can convincingly put forward an argument whether at school or in the workforce.

You might not yet offer research skills training for students (if you do, you might want to consider the EtonX Research Skills course ), but here are ten things that you can do in your day-to-day lessons that can foster better research skills.

1. Encourage curiosity

Curiosity is a strong desire to know things and is a powerful driver of learning. Curious students will naturally ask questions that demand answers.

This hunger for knowledge can see students stepping outside their comfort zone and learning about the unknown. It is also said that curious people are better listeners and are more open to listening to other people’s ideas and perspectives, and not just their own.

What you can do

So, encourage questions, allow time for exploration and help students to enjoy the journey, and not just the destination.

2. Prioritise learner autonomy

Instead of presenting students with information on a platter, have students find out for themselves and get them to draw their own conclusions.  This may take a lot more time than simply spoon-feeding them with information, but the process will teach students to think for themselves, especially if you consider the fact that a lot of the information we impart to students may no longer be accurate or relevant by the time they are in the workforce.

So, the next time a student asks you a question, ask them one right back and have them find things out for themselves. The answer might just be a lot more memorable that way.

3. Vary the ways students find out about things

Do your students turn to Professor Googl e every time they need to find out about something? Do they tend to click on the top answers that their favourite search engine presents them with and be satisfied that they’ve done their research on the topic?

Find opportunities to show students why relying on the same research method and resource can produce skewed results. There are a plethora of publications, search engines, online search methods that can inform students about what’s been previously explored.

Encourage students to find out about things via a range of resources, including ones they are less familiar with. Then get them to build upon this existing knowledge by applying it to their context, conducting surveys, experimenting, or speaking in detail to someone of interest.

4. Help students exercise focus and practise goal-setting

  While it might be more straightforward finding out about the circumference of the earth or how food is digested in the human body, larger questions might require a more extensive research plan.

When confronted by the complexity of the different stages needed to piece together information about a topic, students might feel lost and not know where to start. During the process of their research, they might encounter other interesting pieces of information that might distract them and get them sidetracked. Having a main goal and smaller goals along the way can help them to stay focused.

Use the SMART model when helping students to set goals.

Goals, whether big or small, should be:

M easureable

A chievable

T ime-bound

In your day-to-day lessons, encourage your students to practise setting mini goals and encourage them to fulfill them one at a time. And if your students start to get a bit overwhelmed, guide them along each stage and help them to focus on the smaller parts.

5. Have students practise time management

Do your students constantly complain that they have no time? Do they often come to class without having done their homework? Bad time management skills can impact badly when managing projects and doing extensive research.

In addition to goal setting (see above), students can better manage their time by learning to plan and by eliminating time-wasters (How many times have you heard students say ‘ I don’t know ’ when you asked them how they’d spent their weekend?)

Get students talking how they spend their time and ask them to draw a pie-chart or a table depicting how their time is being divided during the week. Have them commit to set deadlines and get students working in teams so that a delay by one individual will impact on the other team members.

It is only with practice can we eliminate those bad habits and work on improving our time management skills.

6. Help students with reading strategies

The idea of research often puts some people off because it suggests ploughing through reams of academic texts and trying to make sense of what’s been written.

But reading can be made easier once we understand that the strategies we employ in reading for research purposes should not be the same as the ones we use to read a novel.

To begin with, we are less likely to read each word on every page. We might skim the text for gist, or scan it for specific information. We might use it to build on our existing knowledge on the topic or look for emerging themes.

The next time your students read in class, set them tasks that ask them to choose a reading strategy and that hone their skimming and scanning ability.

7. Have students experiment with different note-taking methods

Some students choose to highlight chunks of texts in different colours, some choose to summarise chapters that they’ve read, and others copy out only what is relevant to their research question.

Then there are Mind Maps, Sketch Notes, the Cornell Method, the outlining method, the charting method, etc. Whatever the method, a good note-taking strategy can help students better absorb the information and retrieve it when needed.

Watch how your students take notes the next time you’re in class. See if you can persuade them to experiment with a different note-taking method.

8. Use every opportunity to foster critical thinking skills

When conducting research, students need to be able to identify credible sources, understand the differences between opinion and fact, analyse arguments, and know when they are being manipulated.

In other words, students need to be equipped with critical thinking skills.

Find every opportunity for students to practise their critical thinking skills and get students to question the information they get on a day-to-day basis.

Read my previous article here to find out more about how we can help students implement the skills of critical thinking.

9. Cultivate self-awareness

As well as being aware of other people’s subjective opinions, it is important that we help students to also be aware of their own subjectivity. We are all brought up with a certain view of the world, along with certain biases.

In order to analyse information objectively, we need to help students reflect on their beliefs and attitudes and encourage them to open their minds to other perspectives and ways of looking at things.

The next time your students share their opinions or feelings about a topic, ask questions and get them to expand on what they’ve said. Without being confrontational, help them to cultivate an awareness of the foundations upon which they filter the information they receive.

10. Offer opportunities for students to share their findings

You’ve got your students to ask questions and they’ve found some answers. What do they then do with the answers?

Perhaps they share it with their group members or they write it up in a report that only their teacher gets to read. Either way, the long journey seems to end in an anti-climatic fashion with the assumption that the learning achieved from having done the research is enough to satisfy the students.

Giving students the space and platform to present their research and share their findings can be crucial to sustaining the motivation for future research projects. It also gives others the chance of benefitting from the student’s hard work and might inspire them them to do the same.

The next time students come back with answers, consider having them present it in front of the class, share it with the school, record a podcast or write it up for a class blog or a school newsletter. If the extent of the research they’ve done is proportionate to the audience who benefit from those findings, the students are going to be more likely  to embark on future research projects.

There are multiple research skills that can be practised through encouraging students to take on different stages of research in your classroom. And by spotting these opportunities for practice, you’ll be helping your students develop some essential life skills that will enhance their ability to answer those questions that life might throw at them.

If you deem research skills to be of importance to your students, you might also consider getting them to dedicate some time to a course focusing specifically on Research Skills, like this one by EtonX .

Home > Blog > Tips for Online Students > The Best Research Skills For Success

Tips for Online Students , Tips for Students

The Best Research Skills For Success

Updated: June 19, 2024

Published: January 5, 2020

Every student is required to conduct research in their academic careers at one point or another. A good research paper not only requires a great deal of time, but it also requires complex skills. Research skills include the ability to organize, evaluate, locate, and extract relevant information.

Let’s learn how to develop great research skills for academic success.

What is Research?

We’ve all surely heard the term “research” endlessly. But do you really know what it means?

Research is a type of study that focuses on a specific problem and aims to solve it using scientific methods. Research is a highly systematic process that involves both describing, explaining, and predicting something.

Photo by  Startup Stock Photos  from  Pexels

What are research skills.

Research skills are what helps us answer our most burning questions, and they are what assist us in our solving process from A to Z, including searching, finding, collecting, breaking down, and evaluating the relevant information to the phenomenon at hand.

Research is the basis of everything we know — and without it, we’re not sure where we would be today! For starters, without the internet and without cars, that’s for sure.

Why are Research Skills Important?

Research skills come in handy in pretty much everything we do, and especially so when it comes to the workforce. Employers will want to hire you and compensate you better if you demonstrate a knowledge of research skills that can benefit their company.

From knowing how to write reports, how to notice competition, develop new products, identify customer needs, constantly learn new technologies, and improve the company’s productivity, there’s no doubt that research skills are of utter importance. Research also can save a company a great deal of money by first assessing whether making an investment is really worthwhile for them.

How to Get Research Skills

Now that you’re fully convinced about the importance of research skills, you’re surely going to want to know how to get them. And you’ll be delighted to hear that it’s really not so complicated! There are plenty of simple methods out there to gain research skills such as the internet as the most obvious tool.

Gaining new research skills however is not limited to just the internet. There are tons of books, such as Lab Girl by Hope Jahren, journals, articles, studies, interviews and much, much more out there that can teach you how to best conduct your research.

Utilizing Research Skills

Now that you’ve got all the tools you need to get started, let’s utilize these research skills to the fullest. These skills can be used in more ways than you know. Your research skills can be shown off either in interviews that you’re conducting or even in front of the company you’re hoping to get hired at .

It’s also useful to add your list of research skills to your resume, especially if it’s a research-based job that requires skills such as collecting data or writing research-based reports. Many jobs require critical thinking as well as planning ahead.

Career Paths that Require Research Skills

If you’re wondering which jobs actually require these research skills, they are actually needed in a variety of industries. Some examples of the types of work that require a great deal of research skills include any position related to marketing, science , history, report writing, and even the food industry.

Photo by  Abby Chung  from  Pexels

How students can improve research skills.

Perhaps you know what you have to do, but sometimes, knowing how to do it can be more of a challenge. So how can you as a student improve your research skills ?

1. Define your research according to the assignment

By defining your research and understanding how it relates to the specific field of study, it can give more context to the situation.

2. Break down the assignment

The most difficult part of the research process is actually just getting started. By breaking down your research into realistic and achievable parts, it can help you achieve your goals and stay systematic.

3. Evaluate your sources

While there are endless sources out there, it’s important to always evaluate your sources and make sure that they are reliable, based on a variety of factors such as their accuracy and if they are biased, especially if used for research purposes.

4. Avoid plagiarism

Plagiarism is a major issue when it comes to research, and is often misunderstood by students. IAs a student, it’s important that you understand what plagiarism really means, and if you are unclear, be sure to ask your teachers.

5. Consult and collaborate with a librarian

A librarian is always a good person to have around, especially when it comes to research. Most students don’t seek help from their school librarian, however, this person tends to be someone with a vast amount of knowledge when it comes to research skills and where to look for reliable sources.

6. Use library databases

There are tons of online library resources that don’t require approaching anyone. These databases are generally loaded with useful information that has something for every student’s specific needs.

7. Practice effective reading

It’s highly beneficial to practice effective reading, and there are no shortage of ways to do it. One effective way to improve your research skills it to ask yourself questions using a variety of perspectives, putting yourself in the mind of someone else and trying to see things from their point of view.

There are many critical reading strategies that can be useful, such as making summaries from annotations, and highlighting important passages.

Thesis definition

A thesis is a specific theory or statement that is to be either proved or maintained. Generally, the intentions of a thesis are stated, and then throughout, the conclusions are proven to the reader through research. A thesis is crucial for research because it is the basis of what we are trying to prove, and what guides us through our writing.

What Skills Do You Need To Be A Researcher?

One of the most important skills needed for research is independence, meaning that you are capable of managing your own work and time without someone looking over you.

Critical thinking, problem solving, taking initiative, and overall knowing how to work professionally in front of your peers are all crucial for effectively conducting research .

1. Fact check your sources

Knowing how to evaluate information in your sources and determine whether or not it’s accurate, valid or appropriate for the specific purpose is a first on the list of research skills.

2. Ask the right questions

Having the ability to ask the right questions will get you better search results and more specific answers to narrow down your research and make it more concise.

3. Dig deeper: Analyzing

Don’t just go for the first source you find that seems reliable. Always dig further to broaden your knowledge and make sure your research is as thorough as possible.

4. Give credit

Respect the rights of others and avoid plagiarizing by always properly citing your research sources.

5. Utilize tools

There are endless tools out there, such as useful websites, books, online videos, and even on-campus professionals such as librarians that can help. Use all the many social media networks out there to both gain and share more information for your research.

6. Summarizing

Summarizing plays a huge role in research, and once the data is collected, relevant information needs to be arranged accordingly. Otherwise it can be incredibly overwhelming.

7. Categorizing

Not only does information need to be summarized, but also arranged into categories that can help us organize our thoughts and break down our materials and sources of information.

Photo by  Noelle Otto  from  Pexels

What are different types of research, 1. qualitative.

This type of research is exploratory research and its aim is to obtain a better understanding of reasons for things. Qualitative research helps form an idea without any specific fixed pattern. Some examples include face-to-face interviews or group discussions.

2. Quantitative

Quantitative research is based on numbers and statistics. This type of research uses data to prove facts, and is generally taken from a large group of people.

3. Analytical

Analytical research has to always be done from a neutral point of view, and the researcher is intended to break down all perspectives. This type of research involves collecting information from a wide variety of sources.

4. Persuasive

Persuasive research describes an issue from two different perspectives, going through both the pros and cons of both, and then aims to prove their preference towards one side by exploring a variety of logical facts.

5. Cause & Effect

In this type of research, the cause and effects are first presented, and then a conclusion is made. Cause and effect research is for those who are new in the field of research and is mostly conducted by high school or college students.

6. Experimental Research

Experimental research involves very specific steps that must be followed, starting by conducting an experiment. It is then followed by sharing an experience and providing data about it. This research is concluded with data in a highly detailed manner.

7. Survey Research

Survey research includes conducting a survey by asking participants specific questions, and then analyzing those findings. From that, researchers can then draw a conclusion.

8. Problem-Solution Research

Both students and scholars alike carry out this type of research, and it involves solving problems by analyzing the situation and finding the perfect solution to it.

What it Takes to Become a Researcher

• Critical thinking

Research is most valuable when something new is put on the table. Critical thinking is needed to bring something unique to our knowledge and conduct research successfully.

• Analytical thinking

Analytical thinking is one of the most important research skills and requires a great deal of practice. Such a skill can assist researchers in taking apart and understanding a large amount of important information in a short amount of time.

• Explanation skills

When it comes to research skills, it’s not just about finding information, but also about how you explain it. It’s more than just writing it out, but rather, knowing how to clearly and concisely explain your new ideas.

• Patience is key

Just like with anything in life, patience will always take you far. It might be difficult to come by, but by not rushing things and investing the time needed to conduct research properly, your work is bound for success.

• Time management

Time is the most important asset that we have, and it can never be returned back to us. By learning time management skills , we can utilize our time in the best way possible and make sure to always be productive in our research.

What You Need to Sharpen Your Research Skills

Research is one of the most important tasks that students are given in college, and in many cases, it’s almost half of the academic grade that one is given.

As we’ve seen, there are plenty of things that you’ll need to sharpen your research skills — which mainly include knowing how to choose reliable and relevant sources, and knowing how to take them and make it your own. It’s important to always ask the right questions and dig deeper to make sure that you understood the full picture.

In this article

At UoPeople, our blog writers are thinkers, researchers, and experts dedicated to curating articles relevant to our mission: making higher education accessible to everyone. Read More

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Research and innovation menu, research and innovation, research skills development.

Willison & O’Regan (2007) believe that the development of research skills occurs on a continuum of knowledge production, from that which is new to the learner to fringe research that is new to everyone, or to move from “the commonly known, to the commonly not known, to the totally unknown" (p. 394). From this perspective, they developed a  Research Skills Development Framework   that outlines six facets of research and describes how each skill/activity manifests along a continuum based on the level of student autonomy.

Key Terms*:

• Prescribed Research –  highly structured directions and modeling from faculty
• Bounded Research –  boundaries set by and limited directions from faculty
• Scaffolded Research –  scaffolds placed by faculty shape the students’ research
• Student-initiated Research –  student-initiated research under the guidance of a faculty member
• Open Research –  independent student research that is guided by disciplinary standards

* Please see the  RSD Framework for a full description.

The Research Skills Development Framework is useful as both a conceptual and planning tool as well as an assessment mechanism.  It can be used to develop course and program activities that are appropriate for the level of research being conducted, it can help clarify learning outcomes, develop assessment measurements, and track student progress and development.

Equipped with an understanding of student development and research skills development, one can begin to conceptualize and plan undergraduate research activities that best suit the characteristics of the student population you are working with and/or the students that you are targeting to conduct research with – whether it be in a course or extra-curricular activity.

Next – Strategies 

• Willison, J.W. & O’Regan, K. (2007). Commonly known, commonly not known, totally unknown: A framework for students becoming researchers.  Higher Education Research and Development , 26, 393-409.

Mentoring Undergraduate Research Directory

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Empowering students to develop research skills

The benefits

Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges

Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally. Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments. Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line

Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

Related Research

Use of an online social annotation platform to enhance a flipped organic chemistry course, collaborative online annotation: pedagogy, assessment and platform comparisons, use of a social annotation platform for pre-class reading assignments in a flipped introductory physics class, related resources, ask a librarian, interested in hearing from a faculty member using perusall.

Interested in hearing from a faculty member using Perusall? Watch this video of Professor Eric Beerbohm from VPAL’s Debate as Pedagogy event explaining how he used the platform to launch class discussions. 

Introductory video from Perusall’s founders

Discover the nine competencies required to become a researcher

What essential skills do researchers need? For those just starting on the road to research, breaking the process down into achievable and measurable milestones can help

Cynthia López

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When studying education, researchers often face the challenge of trying to figure out what, how and when to research, often believing that if a researcher is not an expert in a specific area, they are unable to carry out research on it. However, certain core competencies can help you effectively research any topic related to your teaching practice, as well as incorporate technological and/or pedagogical trends.

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Several models outline the basic knowledge and competencies that a professional (in this case, a teacher) must have in order to carry out research, including the LART model suggested by Luis Arturo Rivas-Tovar , which lists the key competencies as:

• The ability to state a research problem : start from what is known and move to what is desired to be known.
• Know how to elaborate a contextual framework : analyse how the stated problem occurs within a whole and in the context you want to research.
• Examine the state of the art : review what is already known about the defined problem in the literature in order to aid the search for new knowledge. Each part of the problem must be studied separately.
• Prepare and validate data collection instruments: while considering the objective of the study, define the type of research best suited to it, the instrument(s) to be used, and the individuals who will validate and answer them.
• Build a research model: once you have visualised the problem or event to be researched, establish the process you will follow to analyse it and achieve the study objectives.
• Know how to analyse the data obtained: recognise that different techniques are available to process the results, which are linked to the type of research and the scale used in the data collection instruments.
• Know how to write scientific articles : any professional researcher must learn the citation styles: MLA (for literature), CBE (for basic sciences) and APA (for social sciences). Write briefly and concisely and use the IMRaD structure (introduction, method, results and discussion) to present your work.
• Present your results at a conference:  this ability means the new knowledge will be communicated and, most likely, doors will be opened to exchange experiences with other researchers – in this case, teachers from different disciplines and educational institutions.
• Master a second language : English is the universal language, so it is necessary to learn it to be able to communicate in international journals or at conferences.

These nine skills can help guide professionals interested in researching teaching, although they can also, of course, be applied to almost any field. Even if you do not have a particularly scientific profile, they can help instigate a critical view of any topic or event, even one already defined or being tested.

Indeed, as educational engineers, we often analyse educational models to help gauge the impact of pedagogical innovations.

But for what purpose? To answer, here are three key reasons that can apply to any research:

• To gain in-depth knowledge of a topic, event or situation and visualise the place each of its components occupies.
• To communicate the knowledge obtained to the people involved to help them grasp the scope of their participation in the field studied.
• To help make decisions that favour or produce changes in the object/subject of research.

These three purposes, I think, show the usefulness of the nine competencies. They can help us detect strengths as well as opportunities for improvement – and provide the information needed to adjust or optimise.

Finally, the central argument for mastering these nine competencies is that it demonstrates the commitment and passion that any person, whether they are a researcher or not, must put into a field they want to know better. Only through displaying the correct level of rigour can we prepare to find and then solve those aspects of education (or any other field) that remain to be discovered.

Cynthia López is an educational engineer at Monterrey Institute of Technology, Mexico.

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What are research skills?

Last updated

26 April 2023

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Broadly, it includes a range of talents required to:

Find useful information

Perform critical analysis

Form hypotheses

Solve problems

It also includes processes such as time management, communication, and reporting skills to achieve those ends.

Research requires a blend of conceptual and detail-oriented modes of thinking. It tests one's ability to transition between subjective motivations and objective assessments to ensure only correct data fits into a meaningfully useful framework.

As countless fields increasingly rely on data management and analysis, polishing your research skills is an important, near-universal way to improve your potential of getting hired and advancing in your career.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

What are basic research skills?

Almost any research involves some proportion of the following fundamental skills:

Organization

Decision-making

Investigation and analysis

Creative thinking

What are primary research skills?

The following are some of the most universally important research skills that will help you in a wide range of positions:

Time management — From planning and organization to task prioritization and deadline management, time-management skills are highly in-demand workplace skills.

Problem-solving — Identifying issues, their causes, and key solutions are another essential suite of research skills.

Critical thinking — The ability to make connections between data points with clear reasoning is essential to navigate data and extract what's useful towards the original objective.

Communication — In any collaborative environment, team-building and active listening will help researchers convey findings more effectively through data summarizations and report writing.

What are the most important skills in research?

Detail-oriented procedures are essential to research, which allow researchers and their audience to probe deeper into a subject and make connections they otherwise may have missed with generic overviews.

Maintaining priorities is also essential so that details fit within an overarching strategy. Lastly, decision-making is crucial because that's the only way research is translated into meaningful action.

• Why are research skills important?

Good research skills are crucial to learning more about a subject, then using that knowledge to improve an organization's capabilities. Synthesizing that research and conveying it clearly is also important, as employees seek to share useful insights and inspire effective actions.

Effective research skills are essential for those seeking to:

Analyze their target market

Investigate industry trends

Identify customer needs

Detect obstacles

Find solutions to those obstacles

Develop new products or services

Develop new, adaptive ways to meet demands

Discover more efficient ways of acquiring or using resources

Why do we need research skills?

Businesses and individuals alike need research skills to clarify their role in the marketplace, which of course, requires clarity on the market in which they function in. High-quality research helps people stay better prepared for challenges by identifying key factors involved in their day-to-day operations, along with those that might play a significant role in future goals.

• Benefits of having research skills

Research skills increase the effectiveness of any role that's dependent on information. Both individually and organization-wide, good research simplifies what can otherwise be unwieldy amounts of data. It can help maintain order by organizing information and improving efficiency, both of which set the stage for improved revenue growth.

Those with highly effective research skills can help reveal both:

Opportunities for improvement

Brand-new or previously unseen opportunities

Research skills can then help identify how to best take advantage of available opportunities. With today's increasingly data-driven economy, it will also increase your potential of getting hired and help position organizations as thought leaders in their marketplace.

• Research skills examples

Being necessarily broad, research skills encompass many sub-categories of skillsets required to extrapolate meaning and direction from dense informational resources. Identifying, interpreting, and applying research are several such subcategories—but to be specific, workplaces of almost any type have some need of:

Searching for information

Attention to detail

Taking notes

Problem-solving

Communicating results

Time management

• How to improve your research skills

Whether your research goals are to learn more about a subject or enhance workflows, you can improve research skills with this failsafe, four-step strategy:

Make an outline, and set your intention(s)

Know your sources

Learn to use advanced search techniques

Practice, practice, practice (and don't be afraid to adjust your approach)

These steps could manifest themselves in many ways, but what's most important is that it results in measurable progress toward the original goals that compelled you to research a subject.

• Using research skills at work

Different research skills will be emphasized over others, depending on the nature of your trade. To use research most effectively, concentrate on improving research skills most relevant to your position—or, if working solo, the skills most likely have the strongest impact on your goals.

You might divide the necessary research skills into categories for short, medium, and long-term goals or according to each activity your position requires. That way, when a challenge arises in your workflow, it's clearer which specific research skill requires dedicated attention.

How can I learn research skills?

Learning research skills can be done with a simple three-point framework:

Clarify the objective — Before delving into potentially overwhelming amounts of data, take a moment to define the purpose of your research. If at any point you lose sight of the original objective, take another moment to ask how you could adjust your approach to better fit the original objective.

Scrutinize sources — Cross-reference data with other sources, paying close attention to each author's credentials and motivations.

Organize research — Establish and continually refine a data-organization system that works for you. This could be an index of resources or compiling data under different categories designed for easy access.

Which careers require research skills?

Especially in today's world, most careers require some, if not extensive, research. Developers, marketers, and others dealing in primarily digital properties especially require extensive research skills—but it's just as important in building and manufacturing industries, where research is crucial to construct products correctly and safely.

Engineering, legal, medical, and literally any other specialized field will require excellent research skills. Truly, almost any career path will involve some level of research skills; and even those requiring only minimal research skills will at least require research to find and compare open positions in the first place.

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Teaching Research Skills to K-12 Students in The Classroom

Research is at the core of knowledge. Nobody is born with an innate understanding of quantum physics. But through research , the knowledge can be obtained over time. That’s why teaching research skills to your students is crucial, especially during their early years.

But teaching research skills to students isn’t an easy task. Like a sport, it must be practiced in order to acquire the technique. Using these strategies, you can help your students develop safe and practical research skills to master the craft.

What Is Research?

By definition, it’s a systematic process that involves searching, collecting, and evaluating information to answer a question. Though the term is often associated with a formal method, research is also used informally in everyday life!

Whether you’re using it to write a thesis paper or to make a decision, all research follows a similar pattern.

• Choose a topic : Think about general topics of interest. Do some preliminary research to make sure there’s enough information available for you to work with and to explore subtopics within your subject.
• Develop a research question : Give your research a purpose; what are you hoping to solve or find?
• Collect data : Find sources related to your topic that will help answer your research questions. 
• Evaluate your data : Dissect the sources you found. Determine if they’re credible and which are most relevant.
• Make your conclusion : Use your research to answer your question! 

Why Do We Need It?

Research helps us solve problems. Trying to answer a theoretical question? Research. Looking to buy a new car? Research. Curious about trending fashion items? Research! 

Sometimes it’s a conscious decision, like when writing an academic paper for school. Other times, we use research without even realizing it. If you’re trying to find a new place to eat in the area, your quick Google search of “food places near me” is research!

Whether you realize it or not, we use research multiple times a day, making it one of the most valuable lifelong skills to have. And it’s why — as educators —we should be teaching children research skills in their most primal years. 

Teaching Research Skills to Elementary Students

In elementary school, children are just beginning their academic journeys. They are learning the essentials: reading, writing, and comprehension. But even before they have fully grasped these concepts, you can start framing their minds to practice research.

According to curriculum writer and former elementary school teacher, Amy Lemons , attention to detail is an essential component of research. Doing puzzles, matching games, and other memory exercises can help equip students with this quality before they can read or write. 

Improving their attention to detail helps prepare them for the meticulous nature of research. Then, as their reading abilities develop, teachers can implement reading comprehension activities in their lesson plans to introduce other elements of research. 

One of the best strategies for teaching research skills to elementary students is practicing reading comprehension . It forces them to interact with the text; if they come across a question they can’t answer, they’ll need to go back into the text to find the information they need. 

Some activities could include completing compare/contrast charts, identifying facts or questioning the text, doing background research, and setting reading goals. Here are some ways you can use each activity:

• How it translates : Step 3, collect data; Step 4, evaluate your data
• Questioning the text : If students are unsure which are facts/not facts, encourage them to go back into the text to find their answers. 
• How it translates : Step 3, collect data; Step 4, evaluate your data; Step 5, make your conclusion
• How it translates : Step 1, choose your topic
• How it translates : Step 2, develop a research question; Step 5, make your conclusion

Resources for Elementary Research

If you have access to laptops or tablets in the classroom, there are some free tools available through Pennsylvania’s POWER Kids to help with reading comprehension. Scholastic’s BookFlix and TrueFlix are 2 helpful resources that prompt readers with questions before, after, and while they read. 

• BookFlix : A resource for students who are still new to reading. Students will follow along as a book is read aloud. As they listen or read, they will be prodded to answer questions and play interactive games to test and strengthen their understanding. 

• TrueFlix : A resource for students who are proficient in reading. In TrueFlix, students explore nonfiction topics. It’s less interactive than BookFlix because it doesn’t prompt the reader with games or questions as they read. (There are still options to watch a video or listen to the text if needed!)

Teaching Research Skills to Middle School Students

By middle school, the concept of research should be familiar to students. The focus during this stage should be on credibility . As students begin to conduct research on their own, it’s important that they know how to determine if a source is trustworthy.

Before the internet, encyclopedias were the main tool that people used for research. Now, the internet is our first (and sometimes only) way of looking information up. 

Unlike encyclopedias which can be trusted, students must be wary of pulling information offline. The internet is flooded with unreliable and deceptive information. If they aren’t careful, they could end up using a source that has inaccurate information!

How To Know If A Source Is Credible

In general, credible sources are going to come from online encyclopedias, academic journals, industry journals, and/or an academic database. If you come across an article that isn’t from one of those options, there are details that you can look for to determine if it can be trusted.

• The author: Is the author an expert in their field? Do they write for a respected publication? If the answer is no, it may be good to explore other sources.
• Citations: Does the article list its sources? Are the sources from other credible sites like encyclopedias, databases, or journals? No list of sources (or credible links) within the text is usually a red flag. 
• Date: When was the article published? Is the information fresh or out-of-date? It depends on your topic, but a good rule of thumb is to look for sources that were published no later than 7-10 years ago. (The earlier the better!)
• Bias: Is the author objective? If a source is biased, it loses credibility.

An easy way to remember what to look for is to utilize the CRAAP test . It stands for C urrency (date), R elevance (bias), A uthority (author), A ccuracy (citations), and P urpose (bias). They’re noted differently, but each word in this acronym is one of the details noted above. 

If your students can remember the CRAAP test, they will be able to determine if they’ve found a good source.

Resources for Middle School Research

To help middle school researchers find reliable sources, the database Gale is a good starting point. It has many components, each accessible on POWER Library’s site. Gale Litfinder , Gale E-books , or Gale Middle School are just a few of the many resources within Gale for middle school students.

Teaching Research Skills To High Schoolers

The goal is that research becomes intuitive as students enter high school. With so much exposure and practice over the years, the hope is that they will feel comfortable using it in a formal, academic setting. 

In that case, the emphasis should be on expanding methodology and citing correctly; other facets of a thesis paper that students will have to use in college. Common examples are annotated bibliographies, literature reviews, and works cited/reference pages.

• Annotated bibliography : This is a sheet that lists the sources that were used to conduct research. To qualify as annotated , each source must be accompanied by a short summary or evaluation. 
• Literature review : A literature review takes the sources from the annotated bibliography and synthesizes the information in writing.
• Works cited/reference pages : The page at the end of a research paper that lists the sources that were directly cited or referenced within the paper. 

Resources for High School Research

Many of the Gale resources listed for middle school research can also be used for high school research. The main difference is that there is a resource specific to older students: Gale High School . 

If you’re looking for some more resources to aid in the research process, POWER Library’s e-resources page allows you to browse by grade level and subject. Take a look at our previous blog post to see which additional databases we recommend.

Visit POWER Library’s list of e-resources to start your research!

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The Most Important Research Skills (With Examples)

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Research skills are the ability to find out accurate information on a topic. They include being able to determine the data you need, find and interpret those findings, and then explain that to others. Being able to do effective research is a beneficial skill in any profession, as data and research inform how businesses operate. Whether you’re unsure of your research skills or are looking for ways to further improve them, then this article will cover important research skills and how to become even better at research. Key Takeaways Having strong research skills can help you understand your competitors, develop new processes, and build your professional skills in addition to aiding you in finding new customers and saving your company money. Some of the most valuable research skills you can have include goal setting, data collection, and analyzing information from multiple sources. You can and should put your research skills on your resume and highlight them in your job interviews. In This Article    Skip to section What are research skills? Why are research skills important? 12 of the most important research skills How to improve your research skills Highlighting your research skills in a job interview How to include research skills on your resume Resume examples showcasing research skills Research skills FAQs References Sign Up For More Advice and Jobs Show More What are research skills?

Research skills are the necessary tools to be able to find, compile, and interpret information in order to answer a question. Of course, there are several aspects to this. Researchers typically have to decide how to go about researching a problem — which for most people is internet research.

In addition, you need to be able to interpret the reliability of a source, put the information you find together in an organized and logical way, and be able to present your findings to others. That means that they’re comprised of both hard skills — knowing your subject and what’s true and what isn’t — and soft skills. You need to be able to interpret sources and communicate clearly.

Why are research skills important?

Research skills are useful in any industry, and have applications in innovation, product development, competitor research, and many other areas. In addition, the skills used in researching aren’t only useful for research. Being able to interpret information is a necessary skill, as is being able to clearly explain your reasoning.

Research skills are used to:

Do competitor research. Knowing what your biggest competitors are up to is an essential part of any business. Researching what works for your competitors, what they’re doing better than you, and where you can improve your standing with the lowest resource expenditure are all essential if a company wants to remain functional.

Develop new processes and products. You don’t have to be involved in research and development to make improvements in how your team gets things done. Researching new processes that make your job (and those of your team) more efficient will be valued by any sensible employer.

Foster self-improvement. Folks who have a knack and passion for research are never content with doing things the same way they’ve always been done. Organizations need independent thinkers who will seek out their own answers and improve their skills as a matter of course. These employees will also pick up new technologies more easily.

Manage customer relationships. Being able to conduct research on your customer base is positively vital in virtually every industry. It’s hard to move products or sell services if you don’t know what people are interested in. Researching your customer base’s interests, needs, and pain points is a valuable responsibility.

Save money. Whether your company is launching a new product or just looking for ways to scale back its current spending, research is crucial for finding wasted resources and redirecting them to more deserving ends. Anyone who proactively researches ways that the company can save money will be highly appreciated by their employer.

Solve problems. Problem solving is a major part of a lot of careers, and research skills are instrumental in making sure your solution is effective. Finding out the cause of the problem and determining an effective solution both require accurate information, and research is the best way to obtain that — be it via the internet or by observation.

Determine reliable information. Being able to tell whether or not the information you receive seems accurate is a very valuable skill. While research skills won’t always guarantee that you’ll be able to tell the reliability of the information at first glance, it’ll prevent you from being too trusting. And it’ll give the tools to double-check .

12 of the most important research skills

Experienced researchers know that worthwhile investigation involves a variety of skills. Consider which research skills come naturally to you, and which you could work on more.

Data collection . When thinking about the research process, data collection is often the first thing that comes to mind. It is the nuts and bolts of research. How data is collected can be flexible.

For some purposes, simply gathering facts and information on the internet can fulfill your need. Others may require more direct and crowd-sourced research. Having experience in various methods of data collection can make your resume more impressive to recruiters.

Data collection methods include: Observation Interviews Questionnaires Experimentation Conducting focus groups

Analysis of information from different sources. Putting all your eggs in one source basket usually results in error and disappointment. One of the skills that good researchers always incorporate into their process is an abundance of sources. It’s also best practice to consider the reliability of these sources.

Are you reading about U.S. history on a conspiracy theorist’s blog post? Taking facts for a presentation from an anonymous Twitter account?

If you can’t determine the validity of the sources you’re using, it can compromise all of your research. That doesn’t mean just disregard anything on the internet but double-check your findings. In fact, quadruple-check. You can make your research even stronger by turning to references outside of the internet.

Examples of reliable information sources include: Published books Encyclopedias Magazines Databases Scholarly journals Newspapers Library catalogs

Finding information on the internet. While it can be beneficial to consulate alternative sources, strong internet research skills drive modern-day research.

One of the great things about the internet is how much information it contains, however, this comes with digging through a lot of garbage to get to the facts you need. The ability to efficiently use the vast database of knowledge that is on the internet without getting lost in the junk is very valuable to employers.

Internet research skills include: Source checking Searching relevant questions Exploring deeper than the first options Avoiding distraction Giving credit Organizing findings

Interviewing. Some research endeavors may require a more hands-on approach than just consulting internet sources. Being prepared with strong interviewing skills can be very helpful in the research process.

Interviews can be a useful research tactic to gain first-hand information and being able to manage a successful interview can greatly improve your research skills.

Interviewing skills involves: A plan of action Specific, pointed questions Respectfulness Considering the interview setting Actively Listening Taking notes Gratitude for participation

Report writing. Possessing skills in report writing can assist you in job and scholarly research. The overall purpose of a report in any context is to convey particular information to its audience.

Effective report writing is largely dependent on communication. Your boss, professor , or general reader should walk away completely understanding your findings and conclusions.

Report writing skills involve: Proper format Including a summary Focusing on your initial goal Creating an outline Proofreading Directness

Critical thinking. Critical thinking skills can aid you greatly throughout the research process, and as an employee in general. Critical thinking refers to your data analysis skills. When you’re in the throes of research, you need to be able to analyze your results and make logical decisions about your findings.

Critical thinking skills involve: Observation Analysis Assessing issues Problem-solving Creativity Communication

Planning and scheduling. Research is a work project like any other, and that means it requires a little forethought before starting. Creating a detailed outline map for the points you want to touch on in your research produces more organized results.

It also makes it much easier to manage your time. Planning and scheduling skills are important to employers because they indicate a prepared employee.

Planning and scheduling skills include: Setting objectives Identifying tasks Prioritizing Delegating if needed Vision Communication Clarity Time-management

Note-taking. Research involves sifting through and taking in lots of information. Taking exhaustive notes ensures that you will not neglect any findings later and allows you to communicate these results to your co-workers. Being able to take good notes helps summarize research.

Examples of note-taking skills include: Focus Organization Using short-hand Keeping your objective in mind Neatness Highlighting important points Reviewing notes afterward

Communication skills. Effective research requires being able to understand and process the information you receive, either written or spoken. That means that you need strong reading comprehension and writing skills — two major aspects of communication — as well as excellent listening skills.

Most research also involves showcasing your findings. This can be via a presentation. , report, chart, or Q&A. Whatever the case, you need to be able to communicate your findings in a way that educates your audience.

Communication skills include: Reading comprehension Writing Listening skills Presenting to an audience Creating graphs or charts Explaining in layman’s terms

Time management. We’re, unfortunately, only given 24 measly hours in a day. The ability to effectively manage this time is extremely powerful in a professional context. Hiring managers seek candidates who can accomplish goals in a given timeframe.

Strong time management skills mean that you can organize a plan for how to break down larger tasks in a project and complete them by a deadline. Developing your time management skills can greatly improve the productivity of your research.

Time management skills include: Scheduling Creating task outlines Strategic thinking Stress-management Delegation Communication Utilizing resources Setting realistic expectations Meeting deadlines

Using your network. While this doesn’t seem immediately relevant to research skills, remember that there are a lot of experts out there. Knowing what people’s areas of expertise and asking for help can be tremendously beneficial — especially if it’s a subject you’re unfamiliar with.

Your coworkers are going to have different areas of expertise than you do, and your network of people will as well. You may even know someone who knows someone who’s knowledgeable in the area you’re researching. Most people are happy to share their expertise, as it’s usually also an area of interest to them.

Networking involves: Remembering people’s areas of expertise Being willing to ask for help Communication Returning favors Making use of advice Asking for specific assistance

Attention to detail. Research is inherently precise. That means that you need to be attentive to the details, both in terms of the information you’re gathering, but also in where you got it from. Making errors in statistics can have a major impact on the interpretation of the data, not to mention that it’ll reflect poorly on you.

There are proper procedures for citing sources that you should follow. That means that your sources will be properly credited, preventing accusations of plagiarism. In addition, it means that others can make use of your research by returning to the original sources.

Attention to detail includes: Double checking statistics Taking notes Keeping track of your sources Staying organized Making sure graphs are accurate and representative Properly citing sources

How to improve your research skills

As with many professional skills, research skills serve us in our day to day life. Any time you search for information on the internet, you’re doing research. That means that you’re practicing it outside of work as well. If you want to continue improving your research skills, both for professional and personal use, here are some tips to try.

Differentiate between source quality. A researcher is only as good as their worst source. Start paying attention to the quality of the sources you use, and be suspicious of everything your read until you check out the attributions and works cited.

Be critical and ask yourself about the author’s bias, where the author’s research aligns with the larger body of verified research in the field, and what publication sponsored or published the research.

Use multiple resources. When you can verify information from a multitude of sources, it becomes more and more credible. To bolster your faith in one source, see if you can find another source that agrees with it.

Don’t fall victim to confirmation bias. Confirmation bias is when a researcher expects a certain outcome and then goes to find data that supports this hypothesis. It can even go so far as disregarding anything that challenges the researcher’s initial hunch. Be prepared for surprising answers and keep an open mind.

Be open to the idea that you might not find a definitive answer. It’s best to be honest and say that you found no definitive answer instead of just confirming what you think your boss or coworkers expect or want to hear. Experts and good researchers are willing to say that they don’t know.

Stay organized. Being able to cite sources accurately and present all your findings is just as important as conducting the research itself. Start practicing good organizational skills , both on your devices and for any physical products you’re using.

Get specific as you go. There’s nothing wrong with starting your research in a general way. After all, it’s important to become familiar with the terminology and basic gist of the researcher’s findings before you dig down into all the minutia.

Highlighting your research skills in a job interview

A job interview is itself a test of your research skills. You can expect questions on what you know about the company, the role, and your field or industry more generally. In order to give expert answers on all these topics, research is crucial.

Start by researching the company . Look into how they communicate with the public through social media, what their mission statement is, and how they describe their culture.

Pay close attention to the tone of their website. Is it hyper professional or more casual and fun-loving? All of these elements will help decide how best to sell yourself at the interview.

Next, research the role. Go beyond the job description and reach out to current employees working at your desired company and in your potential department. If you can find out what specific problems your future team is or will be facing, you’re sure to impress hiring managers and recruiters with your ability to research all the facts.

Finally, take time to research the job responsibilities you’re not as comfortable with. If you’re applying for a job that represents increased difficulty or entirely new tasks, it helps to come into the interview with at least a basic knowledge of what you’ll need to learn.

How to include research skills on your resume

Research projects require dedication. Being committed is a valuable skill for hiring managers. Whether you’ve had research experience throughout education or a former job, including it properly can boost the success of your resume .

Consider how extensive your research background is. If you’ve worked on multiple, in-depth research projects, it might be best to include it as its own section. If you have less research experience, include it in the skills section .

Focus on your specific role in the research, as opposed to just the research itself. Try to quantify accomplishments to the best of your abilities. If you were put in charge of competitor research, for example, list that as one of the tasks you had in your career.

If it was a particular project, such as tracking the sale of women’s clothing at a tee-shirt company, you can say that you “directed analysis into women’s clothing sales statistics for a market research project.”

Ascertain how directly research skills relate to the job you’re applying for. How strongly you highlight your research skills should depend on the nature of the job the resume is for. If research looks to be a strong component of it, then showcase all of your experience.

If research looks to be tangential, then be sure to mention it — it’s a valuable skill — but don’t put it front and center.

Resume examples showcasing research skills

Example #1: Academic Research

Simon Marks 767 Brighton Blvd. | Brooklyn, NY, 27368 | (683)-262-8883 | [email protected] Diligent and hardworking recent graduate seeking a position to develop professional experience and utilize research skills. B.A. in Biological Sciences from New York University. PROFESSIONAL EXPERIENCE Lixus Publishing , Brooklyn, NY Office Assistant- September 2018-present Scheduling and updating meetings Managing emails and phone calls Reading entries Worked on a science fiction campaign by researching target demographic Organizing calendars Promoted to office assistant after one year internship Mitch’s Burgers and Fries , Brooklyn, NY Restaurant Manager , June 2014-June 2018 Managed a team of five employees Responsible for coordinating the weekly schedule Hired and trained two employees Kept track of inventory Dealt with vendors Provided customer service Promoted to restaurant manager after two years as a waiter Awarded a $2.00/hr wage increase SKILLS Writing Scientific Research Data analysis Critical thinking Planning Communication RESEARCH Worked on an ecosystem biology project with responsibilities for algae collection and research (2019) Lead a group of freshmen in a research project looking into cell biology (2018) EDUCATION New York University Bachelors in Biological Sciences, September 2016-May 2020

Example #2: Professional Research

Angela Nichols 1111 Keller Dr. | San Francisco, CA | (663)-124-8827 |[email protected] Experienced and enthusiastic marketer with 7 years of professional experience. Seeking a position to apply my marketing and research knowledge. Skills in working on a team and flexibility. EXPERIENCE Apples amp; Oranges Marketing, San Francisco, CA Associate Marketer – April 2017-May 2020 Discuss marketing goals with clients Provide customer service Lead campaigns associated with women’s health Coordinating with a marketing team Quickly solving issues in service and managing conflict Awarded with two raises totaling $10,000 over three years Prestigious Marketing Company, San Francisco, CA Marketer – May 2014-April 2017 Working directly with clients Conducting market research into television streaming preferences Developing marketing campaigns related to television streaming services Report writing Analyzing campaign success statistics Promoted to Marketer from Junior Marketer after the first year Timberlake Public Relations, San Francisco, CA Public Relations Intern – September 2013–May 2014 Working cohesively with a large group of co-workers and supervisors Note-taking during meetings Running errands Managing email accounts Assisting in brainstorming Meeting work deadlines EDUCATION Golden Gate University, San Francisco, CA Bachelor of Arts in Marketing with a minor in Communications – September 2009 – May 2013 SKILLS Marketing Market research Record-keeping Teamwork Presentation. Flexibility

Research skills FAQs

What research skills are important?

Goal-setting and data collection are important research skills. Additional important research skills include:

Using different sources to analyze information.

Finding information on the internet.

Interviewing sources.

Writing reports.

Critical thinking.

Planning and scheduling.

Note-taking.

Managing time.

How do you develop good research skills?

You develop good research skills by learning how to find information from multiple high-quality sources, by being wary of confirmation bias, and by starting broad and getting more specific as you go.

When you learn how to tell a reliable source from an unreliable one and get in the habit of finding multiple sources that back up a claim, you’ll have better quality research.

In addition, when you learn how to keep an open mind about what you’ll find, you’ll avoid falling into the trap of confirmation bias, and by staying organized and narrowing your focus as you go (rather than before you start), you’ll be able to gather quality information more efficiently.

What is the importance of research?

The importance of research is that it informs most decisions and strategies in a business. Whether it’s deciding which products to offer or creating a marketing strategy, research should be used in every part of a company.

Because of this, employers want employees who have strong research skills. They know that you’ll be able to put them to work bettering yourself and the organization as a whole.

Should you put research skills on your resume?

Yes, you should include research skills on your resume as they are an important professional skill. Where you include your research skills on your resume will depend on whether you have a lot of experience in research from a previous job or as part of getting your degree, or if you’ve just cultivated them on your own.

If your research skills are based on experience, you could put them down under the tasks you were expected to perform at the job in question. If not, then you should likely list it in your skills section.

University of the People – The Best Research Skills for Success

Association of Internet Research Specialists — What are Research Skills and Why Are They Important?

MasterClass — How to Improve Your Research Skills: 6 Research Tips

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Sky Ariella is a professional freelance writer, originally from New York. She has been featured on websites and online magazines covering topics in career, travel, and lifestyle. She received her BA in psychology from Hunter College.

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Getting First Graders Started With Research

Teaching academically honest research skills helps first graders learn how to collect, organize, and interpret information.

Earlier in my career, I was told two facts that I thought to be false: First graders can’t do research, because they aren’t old enough; and if facts are needed for a nonfiction text, the students can just make them up. Teachers I knew went along with this misinformation, as it seemed to make teaching and learning easier. I always felt differently, and now—having returned to teaching first grade 14 years after beginning my career with that age group—I wanted to prove that first graders can and should learn how to research. 

A lot has changed over the years. Not only has the science of reading given teachers a much better understanding of how to teach reading skills , but we now exist in a culture abundant in information and misinformation. It’s imperative that we teach academically honest research skills to students as early as possible. 

Use a Familiar Resource, and Pair it with a Planned Unit

How soon do you start research in first grade? Certainly not at the start of the year with the summer lapse in skills and knowledge and when new students aren’t yet able to read. By December of this school year, skills had either been recovered or established sufficiently that I thought we could launch into research. This also purposely coincided with a unit of writing on nonfiction—the perfect pairing.

The research needed an age-related focus to make it manageable, so I chose animals. I thought about taking an even safer route and have one whole class topic that we researched together, so that students could compare notes and skills. I referred back to my days working in inquiry-based curriculums (like the International Baccalaureate Primary Years Program) and had students choose which animal to study. Our school librarian recommended that we use Epic because the service has an abundance of excellent nonfiction animal texts of different levels.

Teach the Basics for Organized Research 

I began with a conversation about academic honesty and why we don’t just copy information from books. We can’t say this is our knowledge if we do this; it belongs to the author. Instead, we read and learn. Then, we state what we learned in our own words. Once this concept is understood, I model how to do this by creating a basic step-by-step flowchart taught to me by my wife—a longtime first-grade and kindergarten teacher and firm believer in research skills.

• Read one sentence at a time.
• Turn the book over or the iPad around.
• Think about what you have learned. Can you remember the fact? Is the fact useful? Is it even a fact?
• If the answer is no, reread the sentence or move onto the next one.
• If the answer is yes, write the fact in your own words. Don’t worry about spelling. There are new, complex vocabulary words, so use your sounding-out/stretching-out strategies just like you would any other word. Write a whole sentence on a sticky note.
• Place the sticky note in your graphic organizer. Think about which section it goes in. If you aren’t sure, place it in the “other facts” section.

The key to collecting notes is the challenging skill of categorizing them. I created a graphic organizer that reflected the length and sections of the exemplar nonfiction text from our assessment materials for the writing unit. This meant it had five pages: an introduction, “what” the animal looks like, “where” the animal lives, “how” the animal behaved, and a last page for “other facts” that could become a general conclusion.

Our district’s literacy expert advised me not to hand out my premade graphic organizer too soon in this process because writing notes and categorizing are two different skills. This was my intention, but I forgot the good advice and handed out the organizer right away. This meant dedicating time for examining and organizing notes in each combined writing and reading lesson. A lot of one-on-one feedback was needed for some students, while others flourished and could do this work independently. The result was that the research had a built-in extension for those students who were already confident readers.

Focus on What Students Need to Practice 

Research is an essential academic skill but one that needs to be tackled gradually. I insisted that my students use whole sentences rather than words or phrases because they’re at the stage of understanding what a complete sentence is and need regular practice. In this work, there’s no mention of citation language and vetting sources; in the past, I’ve introduced those concepts to students in fourth grade and used them regularly with my fifth-grade students. Finding texts that span the reading skill range of a first-grade class is a big enough task. 

For some of the key shared scientific vocabulary around science concepts, such as animal groups (mammals, etc.) or eating habits (carnivore, etc.), I created class word lists, having first sounded out the words with the class and then asked students to attempt spelling them in their writing.

The Power of Research Can Facilitate Student Growth 

I was delighted with the results of the research project. In one and a half weeks, every student had a graphic organizer with relevant notes, and many students had numerous notes. With my fourth- and fifth-grade students, I noticed that one of the biggest difficulties for them was taking notes and writing them in a way that showed a logical sequence. Therefore, we concluded our research by numbering the notes in each section to create a sequential order. 

This activity took three lessons and also worked for my first graders. These organized notes created an internal structure that made the next step in the writing process, creating a first draft of their nonfiction teaching books, so much easier. 

The overall result was that first graders were able to truly grasp the power of research and gathering accurate facts. I proved that young children can do this, especially when they work with topics that already fascinate them. Their love of learning motivated them to read higher-level and more sophisticated texts than they or I would normally pick, further proving how interest motivates readers to embrace complexity.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research

• Elham Nasiri   ORCID: orcid.org/0000-0002-0644-1646 1 &
• Laleh Khojasteh   ORCID: orcid.org/0000-0002-6393-2759 1  

BMC Medical Education volume  24 , Article number:  925 ( 2024 ) Cite this article

Metrics details

This study investigates the effectiveness of panel discussions, a specific interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, in English for Specific Purposes (ESP) courses for international medical students. This approach aims to simulate professional conference discussions, preparing students for future academic and clinical environments where such skills are crucial. While traditional group presentations foster critical thinking and communication, a gap exists in understanding how medical students perceive the complexities of preparing for and participating in panel discussions within an ESP setting. This qualitative study investigates the perceived advantages and disadvantages of these discussions from the perspectives of both panelists (medical students) and the audience (peers). Additionally, the study explores potential improvements based on insights from ESP instructors. Utilizing a two-phase design involving reflection papers and focus group discussions, data were collected from 46 medical students and three ESP instructors. Thematic analysis revealed that panel discussions offer unique benefits compared to traditional presentations, including enhanced engagement and more dynamic skill development for both panelists and the audience. Panelists reported gains in personal and professional development, including honing critical thinking, communication, and presentation skills. The audience perceived these discussions as engaging learning experiences that fostered critical analysis and information synthesis. However, challenges such as academic workload and concerns about discussion quality were also identified. The study concludes that panel discussions, when implemented effectively, can be a valuable tool for enhancing critical thinking, communication skills, and subject matter knowledge in ESP courses for medical students. These skills are transferable and can benefit students in various academic and professional settings, including future participation in medical conferences. This research provides valuable insights for ESP instructors seeking to integrate panel discussions into their curriculum, ultimately improving student learning outcomes and preparing them for future success in professional communication.

Peer Review reports

Introduction

In the field of medical education, the acquisition and application of effective communication skills are crucial for medical students in today’s global healthcare environment [ 1 ]. This necessitates not only strong English language proficiency but also the ability to present complex medical information clearly and concisely to diverse audiences.

Language courses, especially English for Specific Purposes (ESP) courses for medical students, are highly relevant in today’s globalized healthcare environment [ 2 ]. In non-English speaking countries like Iran, these courses are particularly important as they go beyond mere language instruction to include the development of critical thinking, cultural competence, and professional communication skills [ 3 ]. Proficiency in English is crucial for accessing up-to-date research, participating in international conferences, and communicating with patients and colleagues from diverse backgrounds [ 4 ]. Additionally, ESP courses help medical students understand and use medical terminologies accurately, which is essential for reading technical articles, listening to audio presentations, and giving spoken presentations [ 5 ]. In countries where English is not the primary language, ESP courses ensure that medical professionals can stay current with global advancements and collaborate effectively on an international scale [ 6 ]. Furthermore, these courses support students who may seek to practice medicine abroad, enhancing their career opportunities and professional growth [ 7 ].

Moreover, ESP courses enable medical professionals to communicate effectively with international patients, which is crucial in multicultural societies and for medical tourism, ensuring that patient care is not compromised due to language barriers [ 8 ]. Many medical textbooks, journals, and online resources are available primarily in English, and ESP courses equip medical students with the necessary language skills to access and comprehend these resources, ensuring they are well-informed about the latest medical research and practices [ 9 ].

Additionally, many medical professionals from non-English speaking countries aim to take international certification exams, such as the USMLE or PLAB, which are conducted in English, and ESP courses prepare students for these exams by familiarizing them with the medical terminology and language used in these assessments [ 10 ]. ESP courses also contribute to the professional development of medical students by improving their ability to write research papers, case reports, and other academic documents in English, which is essential for publishing in international journals and contributing to global medical knowledge [ 11 ]. In the increasingly interdisciplinary field of healthcare, collaboration with professionals from other countries is common, and ESP courses facilitate effective communication and collaboration with international colleagues, fostering innovation and the exchange of ideas [ 12 ].

With the rise of telemedicine and online medical consultations, proficiency in English is essential for non-English speaking medical professionals to provide remote healthcare services to international patients, and ESP courses prepare students for these modern medical practices [ 13 ].

Finally, ESP courses often include training on cultural competence, which is crucial for understanding and respecting the cultural backgrounds of patients and colleagues, leading to more empathetic and effective patient care and professional interactions [ 14 ]. Many ESP programs for medical students incorporate group presentations as a vital component of their curriculum, recognizing the positive impact on developing these essential skills [ 15 ].

Group projects in language courses, particularly in ESP for medical students, are highly relevant for several reasons. They provide a collaborative environment that mimics real-world professional settings, where healthcare professionals often work in multidisciplinary teams [ 16 ]. These group activities foster not only language skills but also crucial soft skills such as teamwork, leadership, and interpersonal communication, which are essential in medical practice [ 17 ].

The benefits of group projects over individual projects in language learning are significant. Hartono, Mujiyanto [ 18 ] found that group presentation tasks in ESP courses led to higher self-efficacy development compared to individual tasks. Group projects encourage peer learning, where students can learn from each other’s strengths and compensate for individual weaknesses [ 19 ]. They also provide a supportive environment that can reduce anxiety and increase willingness to communicate in the target language [ 20 ]. However, it is important to note that group projects also come with challenges, such as social loafing and unequal contribution, which need to be managed effectively [ 21 ].

Traditional lecture-based teaching methods, while valuable for knowledge acquisition, may not effectively prepare medical students for the interactive and collaborative nature of real-world healthcare settings [ 22 ]. Panel discussions (hereafter PDs), an interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, are particularly relevant in this context. They simulate professional conference discussions and interdisciplinary team meetings, preparing students for future academic and clinical environments where such skills are crucial [ 23 ].

PDs, also known as moderated discussions or moderated panels, are a specific type of interactive format where a group of experts or stakeholders engage in a facilitated conversation on a particular topic or issue [ 22 ]. In this format, a moderator guides the discussion, encourages active participation from all panelists, and fosters a collaborative environment that promotes constructive dialogue and critical thinking [ 24 ]. The goal is to encourage audience engagement and participation, which can be achieved through various strategies such as asking open-ended questions, encouraging counterpoints and counterarguments, and providing opportunities for audience members to pose questions or share their own experiences [ 25 ]. These discussions can take place in-person or online, and can be designed to accommodate diverse audiences and settings [ 26 ].

In this study, PD is considered a speaking activity where medical students are assigned specific roles to play during the simulation, such as a physician, quality improvement specialist, policymaker, or patient advocate. By taking on these roles, students can gain a better understanding of the diverse perspectives and considerations that come into play in real-world healthcare discussions [ 23 ]. Simulating PDs within ESP courses can be a powerful tool for enhancing medical students’ learning outcomes in multiple areas. This approach improves language proficiency, academic skills, and critical thinking abilities, while also enabling students to communicate effectively with diverse stakeholders in the medical field [ 27 , 28 ].

Theoretical framework

The panel discussions in our study are grounded in the concept of authentic assessment (outlined by Villarroel, Bloxham [ 29 ]), which involves designing tasks that mirror real-life situations and problems. In the context of medical education, this approach is particularly relevant as it prepares students for the complex, multidisciplinary nature of healthcare communication. Realism can be achieved through two means: providing a realistic context that describes and delivers a frame for the problem to be solved and creating tasks that are similar to those faced in real and/or professional life [ 30 ]. In our study, the PDs provide a realistic context by simulating scenarios where medical students are required to discuss and present complex medical topics in a professional setting, mirroring the types of interactions they will encounter in their future careers.

The task of participating in PDs also involves cognitive challenge, as students are required to think critically about complex medical topics, analyze information, and communicate their findings effectively. This type of task aims to generate processes of problem-solving, application of knowledge, and decision-making that correspond to the development of cognitive and metacognitive skills [ 23 ]. For medical students, these skills are crucial in developing clinical reasoning and effective patient communication. The PDs encourage students to go beyond the textual reproduction of fragmented and low-order content and move towards understanding, establishing relationships between new ideas and previous knowledge, linking theoretical concepts with everyday experience, deriving conclusions from the analysis of data, and examining both the logic of the arguments present in the theory and its practical scope [ 24 , 25 , 27 ].

Furthermore, the evaluative judgment aspect of our study is critical in helping students develop criteria and standards about what a good performance means in medical communication. This involves students judging their own performance and regulating their own learning [ 31 ]. In the context of panel discussions, students reflect on their own work, compare it with desired standards, and seek feedback from peers and instructors. By doing so, students can develop a sense of what constitutes good performance in medical communication and what areas need improvement [ 32 ]. Boud, Lawson and Thompson [ 33 ] argue that students need to build a precise judgment about the quality of their work and calibrate these judgments in the light of evidence. This skill is particularly important for future medical professionals who will need to continually assess and improve their communication skills throughout their careers.

The theoretical framework presented above highlights the importance of authentic learning experiences in medical education. By drawing on the benefits of group work and panel discussions, university instructor-researchers aimed to provide medical students with a unique opportunity to engage with complex cases and develop their communication and collaboration skills. As noted by Suryanarayana [ 34 ], authentic learning experiences can lead to deeper learning and improved retention. Considering the advantages of group work in promoting collaborative problem-solving and language development, the instructor-researchers designed a panel discussion task that simulates real-world scenarios, where students can work together to analyze complex cases, share knowledge, and present their findings to a simulated audience.

While previous studies have highlighted the benefits of interactive learning experiences and critical thinking skills in medical education, a research gap remains in understanding how medical students perceive the relevance of PDs in ESP courses. This study aims to address this gap by investigating medical students’ perceptions of PD tasks in ESP courses and how these perceptions relate to their language proficiency, critical thinking skills, and ability to communicate effectively with diverse stakeholders in the medical field. This understanding can inform best practices in medical education, contributing to the development of more effective communication skills for future healthcare professionals worldwide [ 23 ]. The research questions guiding this study are:

What are the perceived advantages of PDs from the perspectives of panelists and the audience?

What are the perceived disadvantages of PDs from the perspectives of panelists and the audience?

How can PDs be improved for panelists and the audience based on the insights of ESP instructors?

Methodology

Aim and design.

For this study, a two-phase qualitative design was employed to gain an understanding of the advantages and disadvantages of PDs from the perspectives of both student panelists and the audience (Phase 1) and to acquire an in-depth understanding of the suggested strategies provided by experts to enhance PPs for future students (Phase 2).

Participants and context of the study

This study was conducted in two phases (Fig.  1 ) at Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.

Participants of the study in two phases

In the first phase, the student participants were 46 non-native speakers of English and international students who studied medicine at SUMS. Their demographic characteristics can be seen in Table  1 .

These students were purposefully selected because they were the only SUMS international students who had taken the ESP (English for Specific Purposes) course. The number of international students attending SUMS is indeed limited. Each year, a different batch of international students joins the university. They progress through a sequence of English courses, starting with General English 1 and 2, followed by the ESP course, and concluding with academic writing. At the time of data collection, the students included in the study were the only international students enrolled in the ESP course. This mandatory 3-unit course is designed to enhance their language and communication skills specifically tailored to their profession. As a part of the Medicine major curriculum, this course aims to improve their English language proficiency in areas relevant to medicine, such as understanding medical terminology, comprehending original medicine texts, discussing clinical cases, and communicating with patients, colleagues, and other healthcare professionals.

Throughout the course, students engage in various interactive activities, such as group discussions, role-playing exercises, and case studies, to develop their practical communication skills. In this course, medical students receive four marks out of 20 for their oral presentations, while the remaining marks are allocated to their written midterm and final exams. From the beginning of the course, they are briefed about PDs, and they are shown two YouTube-downloaded videos about PDs at medical conferences, a popular format for discussing and sharing knowledge, research findings, and expert opinions on various medical topics.

For the second phase of the study, a specific group of participants was purposefully selected. This group consisted of three faculty members from SUMS English department who had extensive experience attending numerous conferences at national and international levels, particularly in the medical field, as well as working as translators and interpreters in medical congresses. Over the course of ten years, they also gained considerable experience in PDs. They were invited to discuss strategies helpful for medical students with PDs.

Panel discussion activity design and implementation

When preparing for a PD session, medical students received comprehensive guidance on understanding the roles and responsibilities of each panel member. This guidance was aimed at ensuring that each participant was well-prepared and understood their specific role in the discussion.

Moderators should play a crucial role in steering the conversation. They are responsible for ensuring that all panelists have an opportunity to contribute and that the audience is engaged effectively. Specific tasks include preparing opening remarks, introducing panelists, and crafting transition questions to facilitate smooth topic transitions. The moderators should also manage the time to ensure balanced participation and encourage active audience involvement.

Panelists are expected to be subject matter experts who bring valuable insights and opinions to the discussion. They are advised to conduct thorough research on the topic and prepare concise talking points. Panelists are encouraged to draw from their medical knowledge and relevant experiences, share evidence-based information, and engage with other panelists’ points through active listening and thoughtful responses.

The audience plays an active role in the PDs. They are encouraged to participate by asking questions, sharing relevant experiences, and contributing to the dialogue. To facilitate this, students are advised to take notes during the discussion and think of questions or comments they can contribute during the Q&A segment.

For this special course, medical students were advised to choose topics either from their ESP textbook or consider current medical trends, emerging research, and pressing issues in their field. Examples included breast cancer, COVID-19, and controversies in gene therapy. The selection process involved brainstorming sessions and consultation with the course instructor to ensure relevance and appropriateness.

To accommodate the PD sessions within the course structure, students were allowed to start their PD sessions voluntarily from the second week. However, to maintain a balance between peer-led discussions and regular course content, only one PD was held weekly. This approach enabled the ESP lecturer to deliver comprehensive content while also allowing students to engage in these interactive sessions.

A basic time structure was suggested for each PD (Fig.  2 ):

Time allocation for panel discussion stages in minutes

To ensure the smooth running of the course and maintain momentum, students were informed that they could cancel their PD session only once. In such cases, they were required to notify the lecturer and other students via the class Telegram channel to facilitate rescheduling and minimize disruptions. This provision was essential in promoting a sense of community among students and maintaining the course’s continuity.

Research tools and data collection

The study utilized various tools to gather and analyze data from participants and experts, ensuring a comprehensive understanding of the research topic.

Reflection papers

In Phase 1 of the study, 46 medical students detailed their perceptions of the advantages and disadvantages of panel discussions from dual perspectives: as panelists (presenters) and as audience members (peers).

Participants were given clear instructions and a 45-minute time frame to complete the reflection task. With approximately 80% of the international language students being native English speakers and the rest fluent in English, the researchers deemed this time allocation reasonable. The questions and instructions were straightforward, facilitating quick comprehension. It was estimated that native English speakers would need about 30 min to complete the task, while non-native speakers might require an extra 15 min for clarity and expression. This time frame aimed to allow students to respond thoughtfully without feeling rushed. Additionally, students could request more time if needed.

Focus group discussion

In phase 2 of the study, a focus group discussion was conducted with three expert participants. The purpose of the focus group was to gather insights from expert participants, specifically ESP (English for Specific Purposes) instructors, on how presentation dynamics can be improved for both panelists and the audience.

According to Colton and Covert [ 35 ], focus groups are useful for obtaining detailed input from experts. The appropriate size of a focus group is determined by the study’s scope and available resources [ 36 ]. Morgan [ 37 ] suggests that small focus groups are suitable for complex topics where specialist participants might feel frustrated if not allowed to express themselves fully.

The choice of a focus group over individual interviews was based on several factors. First, the exploratory nature of the study made focus groups ideal for interactive discussions, generating new ideas and in-depth insights [ 36 ]. Second, while focus groups usually involve larger groups, they can effectively accommodate a limited number of experts with extensive knowledge [ 37 ]. Third, the focus group format fostered a more open environment for idea exchange, allowing participants to engage dynamically [ 36 ]. Lastly, conducting a focus group was more time- and resource-efficient than scheduling three separate interviews [ 36 ].

Data analysis

The first phase of the study involved a thorough examination of the data related to the research inquiries using thematic analysis. This method was chosen for its effectiveness in uncovering latent patterns from a bottom-up perspective, facilitating a comprehensive understanding of complex educational phenomena [ 38 ]. The researchers first familiarized themselves with the data by repeatedly reviewing the reflection papers written by the medical students. Next, an initial round of coding was independently conducted to identify significant data segments and generate preliminary codes that reflected the students’ perceptions of the advantages and disadvantages of presentation dynamics PDs from both the presenter and audience viewpoints [ 38 ].

The analysis of the reflection papers began with the two researchers coding a subset of five papers independently, adhering to a structured qualitative coding protocol [ 39 ]. They convened afterward to compare their initial codes and address any discrepancies. Through discussion, they reached an agreement on the codes, which were then analyzed, organized into categories and themes, and the frequency of each code was recorded [ 38 ].

After coding the initial five papers, the researchers continued to code the remaining 41 reflection paper transcripts in batches of ten, meeting after each batch to review their coding, resolve any inconsistencies, and refine the coding framework as needed. This iterative process, characterized by independent coding, joint reviews, and consensus-building, helped the researchers establish a robust and reliable coding approach consistently applied to the complete dataset [ 40 ]. Once all 46 reflection paper transcripts were coded, the researchers conducted a final review and discussion to ensure accurate analysis. They extracted relevant excerpts corresponding to the identified themes and sub-themes from the transcripts to provide detailed explanations and support for their findings [ 38 ]. This multi-step approach of separate initial coding, collaborative review, and frequency analysis enhanced the credibility and transparency of the qualitative data analysis.

To ensure the trustworthiness of the data collected in this study, the researchers adhered to the Guba and Lincoln standards of scientific accuracy in qualitative research, which encompass credibility, confirmability, dependability, and transferability [ 41 ] (Table  2 ).

The analysis of the focus group data obtained from experts followed the same rigorous procedure applied to the student participants’ data. Thematic analysis was employed to examine the experts’ perspectives, maintaining consistency in the analytical approach across both phases of the study. The researchers familiarized themselves with the focus group transcript, conducted independent preliminary coding, and then collaboratively refined the codes. These codes were subsequently organized into categories and themes, with the frequency of each code recorded. The researchers engaged in thorough discussions to ensure agreement on the final themes and sub-themes. Relevant excerpts from the focus group transcript were extracted to provide rich, detailed explanations of each theme, thereby ensuring a comprehensive and accurate analysis of the experts’ insights.

1. What are the advantages of PDs from the perspective of panelists and the audience?

The analysis of the advantages of PDs from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  2 and 3 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  3 ), the overarching theme was “Personal and Professional Development.” The most frequently reported advantage was knowledge sharing (93.5%), followed closely by increased confidence (91.3%) and the importance of interaction in presentations (91.3%).

Notably, all categories within this theme had at least one code mentioned by over 80% of participants, indicating a broad range of perceived benefits. The category of “Effective teamwork and communication” was particularly prominent, with collaboration (89.1%) and knowledge sharing (93.5%) being among the most frequently cited advantages. This suggests that PDs are perceived as valuable tools for fostering interpersonal skills and collective learning. In the “Language mastery” category, increased confidence (91.3%) and better retention of key concepts (87.0%) were highlighted, indicating that PDs are seen as effective for both language and content learning.

The audience perspective (Table  4 ), encapsulated under the theme “Enriching Learning Experience,” showed similarly high frequencies across all categories.

The most frequently mentioned advantage was exposure to diverse speakers (93.5%), closely followed by the range of topics covered (91.3%) and increased audience interest (91.3%). The “Broadening perspectives” category was particularly rich, with all codes mentioned by over 70% of participants. This suggests that audience members perceive PDs as valuable opportunities for expanding their knowledge and viewpoints. In the “Language practice” category, the opportunity to practice language skills (89.1%) was the most frequently cited advantage, indicating that even as audience members, students perceive significant language learning benefits.

Comparing the two perspectives reveals several interesting patterns:

High overall engagement: Both panelists and audience members reported high frequencies across all categories, suggesting that PDs are perceived as beneficial regardless of the role played.

Language benefits: While panelists emphasized increased confidence (91.3%) and better retention of concepts (87.0%), audience members highlighted opportunities for language practice (89.1%). This indicates that PDs offer complementary language learning benefits for both roles.

Interactive learning: The importance of interaction was highly rated by panelists (91.3%), while increased audience interest was similarly valued by the audience (91.3%). This suggests that PDs are perceived as an engaging, interactive learning method from both perspectives.

Professional development: Panelists uniquely emphasized professional growth aspects such as experiential learning (84.8%) and real-world application (80.4%). These were not directly mirrored in the audience perspective, suggesting that active participation in PDs may offer additional professional development benefits.

Broadening horizons: Both groups highly valued the diversity aspect of PDs. Panelists appreciated diversity and open-mindedness (80.4%), while audience members valued diverse speakers (93.5%) and a range of topics (91.3%).

2. What are the disadvantages of PDs from the perspective of panelists and the audience?

The analysis of the disadvantages of panel discussions (PDs) from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  4 and 5 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  5 ), the theme “Drawbacks of PDs” was divided into two main categories: “Academic Workload Challenges” and “Coordination Challenges.” The most frequently reported disadvantage was long preparation (87.0%), followed by significant practice needed (82.6%) and the time-consuming nature of PDs (80.4%). These findings suggest that the primary concern for panelists is the additional workload that PDs impose on their already demanding academic schedules. The “Coordination Challenges” category, while less prominent than workload issues, still presented significant concerns. Diverse panel skills (78.3%) and finding suitable panelists (73.9%) were the most frequently cited issues in this category, indicating that team dynamics and composition are notable challenges for panelists.

The audience perspective (Table  6 ), encapsulated under the theme “Drawbacks of PDs,” was divided into two main categories: “Time-related Issues” and “Interaction and Engagement Issues.” In the “Time-related Issues” category, the most frequently mentioned disadvantage was the inefficient use of time (65.2%), followed by the perception of PDs as too long and boring (60.9%). Notably, 56.5% of respondents found PDs stressful due to overwhelming workload from other studies, and 52.2% considered them not very useful during exam time. The “Interaction and Engagement Issues” category revealed more diverse concerns. The most frequently mentioned disadvantage was the repetitive format (82.6%), followed by limited engagement with the audience (78.3%) and the perception of PDs as boring (73.9%). The audience also noted issues related to the panelists’ preparation and coordination, such as “Not practiced and natural” (67.4%) and “Coordination and Interaction Issues” (71.7%), suggesting that the challenges faced by panelists directly impact the audience’s experience.

Workload concerns: Both panelists and audience members highlighted time-related issues. For panelists, this manifested as long preparation times (87.0%) and difficulty balancing with other studies (76.1%). For the audience, it appeared as perceptions of inefficient use of time (65.2%) and stress due to overwhelming workload from other studies (56.5%).

Engagement issues: While panelists focused on preparation and coordination challenges, the audience emphasized the quality of the discussion and engagement. This suggests a potential mismatch between the efforts of panelists and the expectations of the audience.

Boredom and repetition: The audience frequently mentioned boredom (73.9%) and repetitive format (82.6%) as issues, which weren’t directly mirrored in the panelists’ responses. This indicates that while panelists may be focused on content preparation, the audience is more concerned with the delivery and variety of the presentation format.

Coordination challenges: Both groups noted coordination issues, but from different perspectives. Panelists struggled with team dynamics and finding suitable co-presenters, while the audience observed these challenges manifesting as unnatural or unpracticed presentations.

Academic pressure: Both groups acknowledged the strain PDs put on their academic lives, with panelists viewing it as a burden (65.2%) and the audience finding it less useful during exam times (52.2%).

3. How can PDs be improved for panelists and the audience from the experts’ point of view?

The presentation of data for this research question differs from the previous two due to the unique nature of the information gathered. Unlike the quantifiable student responses in earlier questions, this data stems from expert opinions and a reflection discussion session, focusing on qualitative recommendations for improvement rather than frequency of responses (Braun & Clarke, 2006). The complexity and interconnectedness of expert suggestions, coupled with the integration of supporting literature, necessitate a more narrative approach (Creswell & Poth, 2018). This format allows for a richer exploration of the context behind each recommendation and its potential implications (Patton, 2015). Furthermore, the exploratory nature of this question, aimed at generating ideas for improvement rather than measuring prevalence of opinions, is better served by a detailed, descriptive presentation (Merriam & Tisdell, 2016). This approach enables a more nuanced understanding of how PDs can be enhanced, aligning closely with the “how” nature of the research question and providing valuable insights for potential implementation (Yin, 2018).

The experts provided several suggestions to address the challenges faced by students in panel discussions (PDs) and improve the experience for both panelists and the audience. Their recommendations focused on six key areas: time management and workload, preparation and skill development, engagement and interactivity, technological integration, collaboration and communication, and institutional support.

To address the issue of time management and heavy workload, one expert suggested teaching students to “ break down the task to tackle the time-consuming nature of panel discussions and balance it with other studies .” This approach aims to help students manage the extensive preparation time required for PDs without compromising their other academic responsibilities. Another expert emphasized “ enhancing medical students’ abilities to prioritize tasks , allocate resources efficiently , and optimize their workflow to achieve their goals effectively .” These skills were seen as crucial not only for PD preparation but also for overall academic success and future professional practice.

Recognizing the challenges of long preparation times and the perception of PDs being burdensome, an expert proposed “ the implementation of interactive training sessions for panelists .” These sessions were suggested to enhance coordination skills and improve the ability of group presenters to engage with the audience effectively. The expert emphasized that such training could help students view PDs as valuable learning experiences rather than additional burdens, potentially increasing their motivation and engagement in the process.

To combat issues of limited engagement and perceived boredom, experts recommended increasing engagement opportunities for the audience through interactive elements like audience participation and group discussions. They suggested that this could transform PDs from passive listening experiences to active learning opportunities. One expert suggested “ optimizing time management and restructuring the format of panel discussions ” to address inefficiency during sessions. This restructuring could involve shorter presentation segments interspersed with interactive elements to maintain audience attention and engagement.

An innovative solution proposed by one expert was “ using ChatGPT to prepare for PDs by streamlining scenario presentation preparation and role allocation. ” The experts collectively discussed the potential of AI to assist medical students in reducing their workload and saving time in preparing scenario presentations and allocating roles in panel discussions. They noted that AI could help generate initial content drafts, suggest role distributions based on individual strengths, and even provide practice questions for panelists, significantly reducing preparation time while maintaining quality.

Two experts emphasized the importance of enhancing collaboration and communication among panelists to address issues related to diverse panel skills and coordination challenges. They suggested establishing clear communication channels and guidelines to improve coordination and ensure a cohesive presentation. This could involve creating structured team roles, setting clear expectations for each panelist, and implementing regular check-ins during the preparation process to ensure all team members are aligned and progressing.

All experts were in agreement that improving PDs would not be possible “ if nothing is done by the university administration to reduce the ESP class size for international students .” They believed that large class sizes in ESP or EFL classes could negatively influence group oral presentations, hindering language development and leading to uneven participation. The experts suggested that smaller class sizes would allow for more individualized attention, increased speaking opportunities for each student, and more effective feedback mechanisms, all of which are crucial for developing strong presentation skills in a second language.

Research question 1: what are the advantages of PDs from the perspective of panelists and the audience?

The results of this study reveal significant advantages of PDs for both panelists and audience members in the context of medical education. These findings align with and expand upon previous research in the field of educational presentations and language learning.

Personal and professional development for panelists

The high frequency of reported benefits in the “Personal and Professional Development” theme for panelists aligns with several previous studies. The emphasis on language mastery, particularly increased confidence (91.3%) and better retention of key concepts (87.0%), supports the findings of Hartono, Mujiyanto [ 42 ], Gedamu and Gezahegn [ 15 ], Li [ 43 ], who all highlighted the importance of language practice in English oral presentations. However, our results show a more comprehensive range of benefits, including professional growth aspects like experiential learning (84.8%) and real-world application (80.4%), which were not as prominently featured in these earlier studies.

Interestingly, our findings partially contrast with Chou [ 44 ] study, which found that while group oral presentations had the greatest influence on improving students’ speaking ability, individual presentations led to more frequent use of metacognitive, retrieval, and rehearsal strategies. Our results suggest that PDs, despite being group activities, still provide significant benefits in these areas, possibly due to the collaborative nature of preparation and the individual responsibility each panelist bears. The high frequency of knowledge sharing (93.5%) and collaboration (89.1%) in our study supports Harris, Jones and Huffman [ 45 ] emphasis on the importance of group dynamics and varied perspectives in educational settings. However, our study provides more quantitative evidence for these benefits in the specific context of PDs.

Enriching learning experience for the audience

The audience perspective in our study reveals a rich learning experience, with high frequencies across all categories. This aligns with Agustina [ 46 ] findings in business English classes, where presentations led to improvements in all four language skills. However, our study extends these findings by demonstrating that even passive participation as an audience member can lead to significant perceived benefits in language practice (89.1%) and broadening perspectives (93.5% for diverse speakers). The high value placed on diverse speakers (93.5%) and range of topics (91.3%) by the audience supports the notion of PDs as a tool for expanding knowledge and viewpoints. This aligns with the concept of situated learning experiences leading to deeper understanding in EFL classes, as suggested by Li [ 43 ] and others [ 18 , 31 ]. However, our study provides more specific evidence for how this occurs in the context of PDs.

Interactive learning and engagement

Both panelists and audience members in our study highly valued the interactive aspects of PDs, with the importance of interaction rated at 91.3% by panelists and increased audience interest at 91.3% by the audience. This strong emphasis on interactivity aligns with Azizi and Farid Khafaga [ 19 ] study on the benefits of dynamic assessment and dialogic learning contexts. However, our study provides more detailed insights into how this interactivity is perceived and valued by both presenters and audience members in PDs.

Professional growth and real-world application

The emphasis on professional growth through PDs, particularly for panelists, supports Li’s [ 43 ] assertion about the power of oral presentations as situated learning experiences. Our findings provide more specific evidence for how PDs contribute to professional development, with high frequencies reported for experiential learning (84.8%) and real-world application (80.4%). This suggests that PDs may be particularly effective in bridging the gap between academic learning and professional practice in medical education.

Research question 2: what are the disadvantages of pds from the perspective of panelists and the audience?

Academic workload challenges for panelists.

The high frequency of reported challenges in the “Academic Workload Challenges” category for panelists aligns with several previous studies in medical education [ 47 , 48 , 49 ]. The emphasis on long preparation (87.0%), significant practice needed (82.6%), and the time-consuming nature of PDs (80.4%) supports the findings of Johnson et al. [ 24 ], who noted that while learners appreciate debate-style journal clubs in health professional education, they require additional time commitment. This is further corroborated by Nowak, Speed and Vuk [ 50 ], who found that intensive learning activities in medical education, while beneficial, can be time-consuming for students.

Perceived value of pds relative to time investment

While a significant portion of the audience (65.2%) perceived PDs as an inefficient use of time, the high frequency of engagement-related concerns (82.6% for repetitive format, 78.3% for limited engagement) suggests that the perceived lack of value may be more closely tied to the quality of the experience rather than just the time investment. This aligns with Dyhrberg O’Neill [ 27 ] findings on debate-based oral exams, where students perceived value despite the time-intensive nature of the activity. However, our results indicate a more pronounced concern about the return on time investment in PDs. This discrepancy might be addressed through innovative approaches to PD design and implementation, such as those proposed by Almazyad et al. [ 22 ], who suggested using AI tools to enhance expert panel discussions and potentially improve efficiency.

Coordination challenges for panelists

The challenges related to coordination in medical education, such as diverse panel skills (78.3%) and finding suitable panelists (73.9%), align with previous research on teamwork in higher education [ 21 ]. Our findings support the concept of the free-rider effect discussed by Hall and Buzwell [ 21 ], who explored reasons for non-contribution in group projects beyond social loafing. This is further elaborated by Mehmood, Memon and Ali [ 51 ], who proposed that individuals may not contribute their fair share due to various factors including poor communication skills or language barriers, which is particularly relevant in medical education where clear communication is crucial [ 52 ]. Comparing our results to other collaborative learning contexts in medical education, Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] measured teamwork competence development in a multidisciplinary project-based learning environment. They found that while teamwork skills improved over time, initial coordination challenges were significant. This aligns with our findings on the difficulties of coordinating diverse panel skills and opinions in medical education settings.

Our results also resonate with Chou’s [ 44 ] study comparing group and individual oral presentations, which found that group presenters often had a limited understanding of the overall content. This is supported by Wilson, Ho and Brookes [ 54 ], who examined student perceptions of teamwork in undergraduate science degrees, highlighting the challenges and benefits of collaborative work, which are equally applicable in medical education [ 52 ].

Quality of discussions and perception for the audience

The audience perspective in our study reveals significant concerns about the quality and engagement of PDs in medical education. The high frequency of issues such as repetitive format (82.6%) and limited engagement with the audience (78.3%) aligns with Parmar and Bickmore [ 55 ] findings on the importance of addressing individual audience members and gathering feedback. This is further supported by Nurakhir et al. [ 25 ], who explored students’ views on classroom debates as a strategy to enhance critical thinking and oral communication skills in nursing education, which shares similarities with medical education. Comparing our results to other interactive learning methods in medical education, Jones et al. [ 26 ] reviewed the use of journal clubs and book clubs in pharmacy education. They found that while these methods enhanced engagement, they also faced challenges in maintaining student interest over time, similar to the boredom issues reported in our study of PDs in medical education. The perception of PDs as boring (73.9%) and not very useful during exam time (52.2%) supports previous research on the stress and pressure experienced by medical students [ 48 , 49 ]. Grieve et al. [ 20 ] specifically examined student fears of oral presentations and public speaking in higher education, which provides context for the anxiety and disengagement observed in our study of medical education. Interestingly, Bhuvaneshwari et al. [ 23 ] found positive impacts of panel discussions in educating medical students on specific modules. This contrasts with our findings and suggests that the effectiveness of PDs in medical education may vary depending on the specific context and implementation.

Comparative analysis and future directions

Our study provides a unique comparative analysis of the challenges faced by both panelists and audience members in medical education. The alignment of concerns around workload and time management between the two groups suggests that these are overarching issues in the implementation of PDs in medical curricula. This is consistent with the findings of Pasandín et al. [ 56 ], who examined cooperative oral presentations in higher education and their impact on both technical and soft skills, which are crucial in medical education [ 52 ]. The mismatch between panelist efforts and audience expectations revealed in our study is a novel finding that warrants further investigation in medical education. This disparity could be related to the self-efficacy beliefs of presenters, as explored by Gedamu and Gezahegn [ 15 ] in their study of TEFL trainees’ attitudes towards academic oral presentations, which may have parallels in medical education. Looking forward, innovative approaches could address some of the challenges identified in medical education. Almazyad et al. [ 22 ] proposed using AI tools like ChatGPT to enhance expert panel discussions in pediatric palliative care, which could potentially address some of the preparation and engagement issues identified in our study of medical education. Additionally, Ragupathi and Lee [ 57 ] discussed the role of rubrics in higher education, which could provide clearer expectations and feedback for both panelists and audience members in PDs within medical education.

Research question 3: how can PDs be improved for panelists and the audience from the experts’ point of view?

The expert suggestions for improving PDs address several key challenges identified in previous research on academic presentations and student workload management. These recommendations align with current trends in educational technology and pedagogical approaches, while also considering the unique needs of medical students.

The emphasis on time management and workload reduction strategies echoes findings from previous studies on medical student stress and academic performance. Nowak, Speed and Vuk [ 50 ] found that medical students often struggle with the fast-paced nature of their courses, which can lead to reduced motivation and superficial learning approaches. The experts’ suggestions for task breakdown and prioritization align with Rabbi and Islam [ 58 ] recommendations for reducing workload stress through effective assignment prioritization. Additionally, Popa et al. [ 59 ] highlight the importance of acceptance and planning in stress management for medical students, supporting the experts’ focus on these areas.

The proposed implementation of interactive training sessions for panelists addresses the need for enhanced presentation skills in professional contexts, a concern highlighted by several researchers [ 17 , 60 ]. This aligns with Grieve et al. [ 20 ] findings on student fears of oral presentations and public speaking in higher education, emphasizing the need for targeted training. The focus on interactive elements and audience engagement also reflects current trends in active learning pedagogies, as demonstrated by Pasandín et al. [ 56 ] in their study on cooperative oral presentations in engineering education.

The innovative suggestion to use AI tools like ChatGPT for PD preparation represents a novel approach to leveraging technology in education. This aligns with recent research on the potential of AI in scientific research, such as the study by Almazyad et al. [ 22 ], which highlighted the benefits of AI in supporting various educational tasks. However, it is important to consider potential ethical implications and ensure that AI use complements rather than replaces critical thinking and creativity.

The experts’ emphasis on enhancing collaboration and communication among panelists addresses issues identified in previous research on teamwork in higher education. Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] noted the importance of measuring teamwork competence development in project-based learning environments. The suggested strategies for improving coordination align with best practices in collaborative learning, as demonstrated by Romero-Yesa et al. [ 61 ] in their qualitative assessment of challenge-based learning and teamwork in electronics programs.

The unanimous agreement on the need to reduce ESP class sizes for international students reflects ongoing concerns about the impact of large classes on language learning and student engagement. This aligns with research by Li [ 3 ] on issues in developing EFL learners’ oral English communication skills. Bosco et al. [ 62 ] further highlight the challenges of teaching and learning ESP in mixed classes, supporting the experts’ recommendation for smaller class sizes. Qiao, Xu and bin Ahmad [ 63 ] also emphasize the implementation challenges for ESP formative assessment in large classes, further justifying the need for reduced class sizes.

These expert recommendations provide a comprehensive approach to improving PDs, addressing not only the immediate challenges of preparation and delivery but also broader issues of student engagement, workload management, and institutional support. By implementing these suggestions, universities could potentially transform PDs from perceived burdens into valuable learning experiences that enhance both academic and professional skills. This aligns with Kho and Ting [ 64 ] systematic review on overcoming oral presentation anxiety among tertiary ESL/EFL students, which emphasizes the importance of addressing both challenges and strategies in improving presentation skills.

This study has shed light on the complex challenges associated with PDs in medical education, revealing a nuanced interplay between the experiences of panelists and audience members. The findings underscore the need for a holistic approach to implementing PDs that addresses both the academic workload concerns and the quality of engagement.

Our findings both support and extend previous research on the challenges of oral presentations and group work in medical education settings. The high frequencies of perceived challenges across multiple categories for both panelists and audience members suggest that while PDs may offer benefits, they also present significant obstacles that need to be addressed in medical education. These results highlight the need for careful consideration in the implementation of PDs in medical education, with particular attention to workload management, coordination strategies, and audience engagement techniques. Future research could focus on developing and testing interventions to mitigate these challenges while preserving the potential benefits of PDs in medical education.

Moving forward, medical educators should consider innovative approaches to mitigate these challenges. This may include:

Integrating time management and stress coping strategies into the PD preparation process [ 59 ].

Exploring the use of AI tools to streamline preparation and enhance engagement [ 22 ].

Developing clear rubrics and expectations for both panelists and audience members [ 57 ].

Incorporating interactive elements to maintain audience interest and participation [ 25 ].

Limitations and future research

One limitation of this study is that it focused on a specific population of medical students, which may limit the generalizability of the findings to other student populations. Additionally, the study relied on self-report data from panelists and audience members, which may introduce bias and affect the validity of the results. Future research could explore the effectiveness of PDs in different educational contexts and student populations to provide a more comprehensive understanding of the benefits and challenges of panel discussions.

Future research should focus on evaluating the effectiveness of these interventions and exploring how PDs can be tailored to the unique demands of medical education. By addressing the identified challenges, PDs have the potential to become a more valuable and engaging component of medical curricula, fostering both academic and professional development. Ultimately, the goal should be to transform PDs from perceived burdens into opportunities for meaningful learning and skill development, aligning with the evolving needs of medical education in the 21st century.

Future research could also examine the long-term impact of PDs on panelists’ language skills, teamwork, and communication abilities. Additionally, exploring the effectiveness of different training methods and tools, such as AI technology, in improving coordination skills and reducing workload stress for panelists could provide valuable insights for educators and administrators. Further research could also investigate the role of class size and audience engagement in enhancing the overall effectiveness of PDs in higher education settings. By addressing these gaps in the literature, future research can contribute to the ongoing development and improvement of PDs as a valuable learning tool for students in higher education.

However, it is important to note that implementing these changes may require significant institutional resources and a shift in pedagogical approaches. Future research could focus on piloting these recommendations and evaluating their effectiveness in improving student outcomes and experiences with PDs.

Data availability

We confirm that the data supporting the findings are available within this article. Raw data supporting this study’s findings are available from the corresponding author, upon request.

Abbreviations

Artificial Intelligence

English as a Foreign Language

English for Specific Purposes

Panel Discussion

Shiraz University of Medical Sciences

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Nasiri, E., Khojasteh, L. Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research. BMC Med Educ 24 , 925 (2024). https://doi.org/10.1186/s12909-024-05911-3

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Strategies for Getting Involved

Identify your interests Take advantage of undergraduate research initiatives Identify faculty members who share your interests Stop! Prepare before contacting faculty members Contact a faculty member Prepare for your initial meeting with a faculty member Ensure a successful first meeting with a faculty member Keep in mind. . .

Identify your interests.

• What topics interest you?
• What relevant coursework have you taken or do you plan to take?
• Is there something you are passionate about and would like to explore?
• What volunteer or work experience do you have?
• What organizations, sports, or activities do you participate in?
• What are your favorite courses?

Take advantage of undergraduate research initiatives. A variety of programs exist to support undergraduate research on campus.

• The Office for Undergraduate Research in the College of Natural Sciences provides access to a range of resources, from the Freshman Research Initiative to student groups to a database of summer research opportunities.
• Various undergraduate research opportunities exist within the Cockrell School of Engineering , including paid and volunteer research positions and for-credit research programs.
• The College of Liberal Arts provides comprehensive information on events and resources in the College. In addition, there are many undergraduate research scholarship opportunities through the College of Liberal Arts.
• Students in any college can participate in the Bridging Disciplines Programs . BDP s allow you to develop an interdisciplinary specialization that complements your major and combines coursework, internships, and research. The BDP Research Coordinators can help you identify research experiences and connect you with a faculty research mentor.
• Organizations around campus provide opportunities for students with similar interests to discuss their work and promote involvement in undergraduate research. The Science Undergraduate Research Group ( SURG e) and Liberal Arts Undergraduate Research Chapter ( LAUNCH ) include students who work to foster a cohesive undergraduate research community.

Identify faculty members who share your interests.

• Search EUREKA for potential research projects and faculty who work on your preferred topics.
• Read faculty profiles on departmental web pages.
• Browse organized research units on campus.

Stop! Prepare before contacting faculty members.

• Approach faculty with respect. Before you approach a professor, do your homework. Read her or his web site, find out about recent publications, and have a good understanding of that person’s research.
• Make sure you can clearly articulate your interests. An Advisor at the Office of Undergraduate Research can help.

Contact a faculty member.

• Be professional. Write an email in a formal, respectful tone. Address the faculty member as ‘Prof. X’ or ‘Dr. Y.’
• Include a sentence or two stating your interests and topics you would like to explore (primarily those that the faculty member shares with you).
• Show your enthusiasm and commitment.

Prepare for your initial meeting with a faculty member.

• Review what made you interested in this topic (courses, college experiences, family/background, media).
• Look over the faculty member’s curriculum vitae and/or publications.
• Think about skills and qualities you can bring to the project.

Ensure a successful first meeting with a faculty member.

• Be confident, excited, and relaxed.
• Introduce yourself and address the faculty member as ‘Prof. X’ or ‘Dr. Y.’
• Look presentable. This isn’t a job interview, but you should dress neatly.
• Ask the professor what will be expected of you.

Keep in mind . . .

• Be willing to start “at the bottom.” Just remember, eventually you will reach the top.
• Be yourself. The faculty member will want to get to know your interests and ideas.
• Think about your availability. How much time do you have to commit to this project? (Remember that your workload shifts during midterms and finals. Don’t overestimate how much time you can contribute.)
• Be reliable. Faculty members are looking for students who are not just interested in their work, but who are reliable and can be counted on.
• Don’t get discouraged. If this faculty member does not have an undergraduate position available, she or he might be able to recommend another project or faculty member for you.

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User Research and the Paperwork Reduction Act

Highlighting case studies across the government.

In 2021, the Biden-Harris Administration issued an Executive Order to improve customer experience and make service delivery simple, seamless, and secure for Americans. Since then, government agencies have been hard at work making their services, tools, and content more effective in addressing the needs of the people they serve.

User research is at the heart of transforming customer experience. The best way to ensure services are effective, efficient, easy to use, fair, and safe is to include the people they are intended for when building it.

In this piece, we’ll demonstrate:

The importance of user research and how it helps mitigate risk in agency projects.

How and when to integrate user research into a project.

Examples of agencies successfully incorporating and conducting certain types of user research without needing Paperwork Reduction Act approval.

The basics of a customer-centered approach

Before we dive into the examples, let’s discuss the basics of a customer-centered approach, and why it’s important to ensure an agency is meeting the needs of the people using its products or services.

Creating a new digital tool or making changes to a website or service can affect the experience of millions. If working in isolation, agencies can miss important details that could slow down or prevent people from using your services.

The key to any successful project is to observe how real people interact with a government service or product . Failing to prioritize user research is risky and can lead to poorly designed products and services that are difficult to access.

Conducting user research by the rules

The Paperwork Reduction Act (PRA) ensures that the Federal Government collects information in a way that reduces unnecessary burden on the public. The PRA process generally involves notifying the public about each information collection and giving them a chance to provide feedback. It also includes review of a proposed information collection by the Office of Information and Regulatory Affairs Management (OIRA) within the Office of Management and Budget. However, as discussed in regulation 5 CFR 1320.3(h) , information collection does not include items in categories that are common during user research. Agencies might not need PRA approval for most user research done on projects, depending on how the project is structured.

Specifically, there are activities common in user research that do not require PRA clearance because they do not qualify as an information collection under PRA regulations (5 C.F.R. § 1320.3). Those activities include:

Directly observing someone using a product (i.e., observing someone filling out a form or finding information on a website) or

Asking people non-standardized questions as part of a one-on-one research session (i.e., asking people questions orally, in an unstructured way, about navigating government benefits or signing up for an appointment). “Non-standardized,” in this context, means the questions asked vary from person to person, and are not drawn from a list of identical questions posed in every research session.

Unlike methods that constitute information collection and require PRA clearance (for example, surveys or large focus groups with a set of the same questions or tasks), these types of research conversations (direct observations or one-on-one sessions with non-standardized questions) do not require PRA approval, regardless of the number of users ultimately involved in the research.

Direct observations and one-on-one feedback sessions can be more valuable than focus groups or surveys , since they may provide a deeper understanding of people’s behavior rather than feelings and opinions and showcase what’s working well, what isn’t, and why. You can learn more about talking to people with tips from the Department of Homeland Security.

Incorporating user research into your project

Incorporating user research results in a better product and reduces risks. It helps address usability concerns, reduces the potential for major backlogs on call centers, increases customer satisfaction, and ensures agencies are prioritizing the right changes. Research is a continuous process, and certain project stages benefit from research, including:

Before a project

Look at comparable systems and services to learn lessons.

Gain a better understanding of the pain points people experience with this system or service.

Get to know stakeholders’ business needs or concerns, possibly using methods suggested by 18F, the digital services agency within the General Services Administration.

Do desk research, such as reading threads on social media websites and discussions on online forums. Find out where people are talking about the topic, go there, and read as much as you can.

Test high-level ideas or potential future solutions.

Start of a project

Observe people using the existing system or service.

Work with local groups, advocates, or nonprofits to identify people to talk to.

Review any comments or letters submitted by the public.

See what people who use the system or service are saying publicly online about their experience.

Read research conducted on this topic.

Middle of a project

Place the thing that your agency team is building in front of someone who will use it, and observe them using it.

• Create a few different product versions and solutions to each problem.
• Gather feedback on what people found easiest to use.

In operations

Conduct content testing to understand what people think the message means and make updates until it’s in plain language. (For example, the agency can write improvements to system messages and try to understand what people think the messages mean until the messages are plain, understandable, and straightforward. This approach could be used for emails, text messages, policy guidance, or webpage updates.)

Hold one-on-one listening sessions with people to learn about existing pain points and then prioritize new features based on feedback.

Now let’s take a look at seven government projects that have successfully incorporated user research and improved the customer experience without conducting an information collection requiring PRA clearance.

By exploring these projects, you can start thinking of ways to conduct more research or advocate for more research to enhance the customer experience. This list can generate new ideas and help you find ways to integrate user research more effectively.

Highlighting seven government projects

Case study 1: conducting user research for an informative website launch.

Reporting Unemployment Identity Theft, Department of Labor (DOL)- March 2021

In 2022, thousands of Americans received a fraudulent unemployment insurance tax form (government Form 1099-G) in the mail despite never applying for unemployment insurance (UI). These individuals were victims of unemployment identity theft, and fraudsters used their information to illegally receive unemployment benefits. For most victims, understanding what steps to take next was confusing.

The DOL met with victims of UI theft and developed a website to guide them through reporting the fraud. After the site went live, the DOL collaborated with other government agencies and organizations to incorporate the same tested language for reporting UI fraud on their sites. This created a consistent and reliable standard across all websites, fostering trustworthiness among victims.

User research

Ten unstructured, one-on-one user research sessions with victims helped the DOL learn more about the current process and what victims did when they received the fraudulent unemployment insurance form. Participants walked through their unique scenarios and researchers took detailed notes.

During the second round of research, the main focus was observing individuals as they navigated through the newly drafted website content. Participants were instructed to vocalize their thoughts as they started at the top of the page and explained what they saw. Any aspects of the website that people found confusing were revised and improved following the sessions.

Why did the PRA not cover this research?

In this project, the DOL directly observed the experiences of program applicants and participants and asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Case study 2: Conducting user research for a new application launch

A New Digital Application for VA Health Care, Department of Veterans Affairs (VA) - July 2016

Many Veterans found the health care application process at the VA frustrating. Most weren’t able to open the fillable PDF online application because it required a certain software that wasn’t common in most browsers. As a result, over 70 percent of visitors had trouble accessing the health care application, according to USDS research.

The team developed a new, user-friendly online application that doesn’t require a certain software to use. For more details, check out the USDS blog post , “Introducing a new digital application for healthcare at VA.”

The team observed Veterans using the existing application to identify pain points and then worked on a new version. Then, they did user research sessions with the new form again to ensure it was easy and removed any previous pain points. Watch a real-life user research session, conducted by a VA employee with a Veteran at this video link .

In this project, the VA directly observed the experiences of program applicants and participants and engaged in unstructured one-on-one interactions. They asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Case study 3: Conducting user research to inform policy and strategy

Welcome Corps, Department of State - February 2024

The Department of State launched a new program to allow Americans to sponsor refugees. The Welcome Corps program involves forming a sponsor group, completing pre-application steps, and then submitting an application. The process was burdensome to Americans seeking to sponsor a refugee, causing frustration and incomplete applications.

The team did user research to inform which steps in the process could be improved in order to reduce unnecessary burden on sponsors and increase successful application submissions. This research helped ensure any policy changes under consideration would actually support program goals. It also helped inform the agency’s roadmap.

The team met one-on-one with current sponsors to learn about their experience and met one-on-one with potential sponsors to understand what steps of the process were challenging. These research sessions used non-standardized questions. The research findings were presented to program leadership, and policy, tech, and operations teams to inform improvements to the application process.

Why the PRA did not cover this research?

In this project, the team observed the experiences of sponsors and potential sponsors and engaged in unstructured one-on-one interactions. They asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Case study 4: Conducting user research to streamline digital experiences

My VA Dashboard for Veterans, Department of Veterans Affairs (VA) - November 2020

Many services are available to Veterans on the VA websites but it can be challenging to locate them and take action. Veterans asked for a centralized location that was relevant to their needs.

The team worked with a vendor to create My VA, a personalized dashboard for Veterans to access tools and information.

A vendor conducted user research to identify the information that Veterans expect to find in the My VA Dashboard tool and the best way to navigate it.

The contract required vendors to conduct user research to determine people’s goals, needs, and behaviors. The vendor conducted one-on-one, non-structured conversations with Veterans to inform how the agency should build the dashboard.

The contractor collected information and observed program applicants and participants by asking non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3), (h)(6).

Case study 5: Conducting user research to inform outreach strategies via text messages

Child Tax Credit Outreach, Department of Treasury and the White House - June 2021

When the American Rescue Plan Act became law in March 2021, millions of Americans were suddenly eligible for unprecedented tax relief by expanding both earned income and child tax credits. Americans who don’t make enough income to require a tax filing would benefit most from the expansion, but first, they needed to know about the credits and include them when they filed a tax return.

Outreach was key to reaching families in most need. The team learned throughout the year that:

Messages from official government entities work well, specifically, government benefits agencies.

Emails and text messages have worked when encouraging state and local governments to send messages directly to beneficiaries.

The team performed user research throughout the year by working with non-profits on the ground to identify research participants and understand what was working and not working. They partnered with Code for America to test several text messages to ensure clear language. They also joined research sessions to observe SNAP applicants engaging with a third-party app that helps them manage their SNAP benefits electronically.

In this project, the groups directly observed the experiences of program applicants and participants. They asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Case study 6: Conducting user research with internal users of a system

Updates to the Unaccompanied Children Case Management System, Office of Refugee Resettlement (ORR), Administration for Children and Families, Department of Health and Human Services – August 2022

Case managers were updating both paper and digital forms when assessing a potential sponsor. Many duplications existed across the two forms, and filling out both caused inefficiencies.

The team changed the online form, eliminating the need for the paper form and transferred them to the digital experience, reducing burden for the sponsor and case manager. The updated digital form also included design improvements to enhance the flow of the questions.

ORR contacted case workers nationwide and observed them filling out both versions of the form to identify pain points. Another round of research was conducted later on, once they had an updated digital form. These were one-on-one conversations to confirm that the form was easy to use. Any areas where the case managers had questions or trouble were good indicators that the digital form needed tweaking. Finally, they arrived at a version that was easy to use and improved the previous safety and efficiency concerns.

In this project, the ORR directly observed case workers (not federal employees) using a form via one-on-one interviews to understand any usability concerns. They asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Case study 7: Conducting user research with students and families

College Scorecard, Department of Education - August 2015

Deciding on a college can be an overwhelming task with limited access to reliable information on student outcomes like student earnings, graduates’ student debt, and borrowers’ repayment rates.

In September 2015, the Department of Education launched the College Scorecard, which made data transparent for the public about colleges by leveraging existing data on costs, graduation, etc. and providing new data points on earnings after attendance, student debt, and borrower repayment rates. As these data sets were published through an open application programming interface (API), researchers, policymakers, and the public could customize their analysis of college performance easily. For more information on this project, check out the Obama White House blog post , “Under the Hood: Building a College Scorecard with Students.”

The team conducted user research at every single step in the project. This user research involved one-on-one conversations with high school students in Washington, D.C.’s Anacostia neighborhood, guidance counselors, 4-H kids, parents, college advisors, and data journalists. They also conducted research and met one-on-one with a diverse set of stakeholders across the higher education community to learn about their concerns, ideas, and hopes for how they could help students and families make a more informed decision.

Based on this research, the team developed a College Scorecard prototype and then turned it into a website. The prototype was put in front of students during one-on-one sessions, to observe if the tool was easy to use. The research revealed that students were unlikely to use a mobile app and were hesitant to use government websites, so the team ensured other sites that were actually frequented by students had access to the same data.

Why did the PRA not cover this research ?

In this project, the Department of Education directly observed the program applicants and participants engaging with the College Scorecard. The Department of Education also asked non-standardized questions on a particular process, theme, or issue without any specification of the information being sought. See 5 CFR 1320.3(h)(3).

Ready to conduct user research?

Throughout this piece, we emphasized the significance of user research and described some ways to incorporate it into agency projects without conducting a PRA information collection. These examples showcase how an agency can successfully conduct user research to improve delivery of services.

Agencies should incorporate user research into their work to make well-informed decisions, minimize risk, and save time and money. These case studies are a reminder that by applying user research best practices, agencies can build trust in government and improve customer experience for all Americans.

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Designing a Context-Driven Problem-Solving Method with Metacognitive Scaffolding Experience Intervention for Biology Instruction

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• Published: 27 August 2024

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• Merga Dinssa Eticha   ORCID: orcid.org/0009-0008-9263-3273 1 , 2 ,
• Adula Bekele Hunde 3 &
• Tsige Ketema 1  

Learner-centered instructional practices, such as the metacognitive strategies scaffolding the problem-solving method for Biology instruction, have been shown to promote students’ autonomy and self-direction, significantly enhancing their understanding of scientific concepts. Thus, this study aimed to elucidate the importance and procedures of context analysis in the development of a context-driven problem-solving method with a metacognitive scaffolding instructional approach, which enhances students’ learning effectiveness in Biology. Therefore, the study was conducted in the Biology departments of secondary schools in Shambu Town, Oromia Region, Ethiopia. The study employed mixed-methods research to collect and analyze data, involving 12 teachers and 80 students. The data collection tools used were interviews, observations, and a questionnaire. The study revealed that conducting a context analysis that involves teachers, students, and learning contexts is essential in designing a context-driven problem-solving method with metacognitive scaffolding for Biology instruction, which provides authentic examples, instructional content, and engaging scenarios for teachers and students. As a result, the findings of this study provide a practical instructional strategy that can be applied to studies aimed at designing a context-driven problem-solving method with metacognitive scaffolding with the potential to influence instructional practices.

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Introduction

Biology is a vital subject in the Natural Sciences and enables learners to understand the mechanisms of living organisms and their practical applications for humans (Agaba, 2013 ). Therefore, Biology instruction requires interactive, learner-centered instructional methods like the problem-solving method with metacognitive scaffolding (PSMMS), which foster students to develop critical thinking, problem-solving, metacognitive, and scientific process skills (Al Azmy & Alebous, 2020 ; Inel & Balim, 2010 ) and help them make informed decisions regarding health and the environment, thereby advancing scientific knowledge (Aurah et al., 2011 ).

Although the focus is on students acquiring scientific knowledge and higher-order thinking skills (Senyigit, 2021 ), research revealed gaps in implementing the PSMMS in Biology, mainly due to the teachers’ limited experience in learner-centered methods (Agena, 2010 ; Beyessa, 2014 ), poor enhancement practices (MoE, 2019 ), tendency to use conventional problem-solving approaches (Aurah et al., 2011 ), and limited understanding of the roles of metacognition in instructional processes (Cimer, 2012 ). On the other hand, there is limited study on the importance of metacognitive instruction in scaffolding the problem-solving method in Biology, although it has a significant impact on students’ performance in mathematics and logical reasoning (Guner & Erbay, 2021 ).

In addition, metacognitive instructional strategies in primary school sciences and the contributions of metacognitive instructional intervention in developing countries are other areas where limited research has been done (Sbhatu, 2006). These challenges offer a study ground for investigating the intervention of metacognitive instructional methods in secondary schools, focusing on the problem-solving method in Biology. This study, therefore, aims to answer the research question, “How can context analysis be used to design a context-driven PSMMS and suggest PSMMS instructional guidelines to enhance students’ effective Biology learning?”

Theoretical Background

The problem-solving method.

The problem-solving method is a learner-centered approach that focuses on identifying, investigating, and solving problems (Ahmady & Nakhostin-Ruhi, 2014 ). The problem-solving method in Biology promotes advanced and critical thinking skills, enhancing students’ attitudes, academic performance, and subject understanding (Albay, 2019 ; Khaparde, 2019 ). Research has shown that students who learn using the problem-solving method outperform those who are taught conventionally (Nnorom, 2019 ). Studies have discussed that the problem-solving method encourages experimentation or learning through trial-and-error and also facilitates a constructivist learning environment by encouraging brainstorming and inquiry (e.g., Ishaku, 2015).

Metacognition

Metacognition, introduced by John Flavell in 1976, refers to an individual’s awareness, critical thinking, reflective judgment, and control of cognitive processes and strategies (Tachie, 2019 ). It consists of two main components, namely metacognitive knowledge and metacognitive regulation (Lai, 2011 ). Metacognitive knowledge involves understanding one’s own thinking, influencing performance, and effective use of methods through declarative, procedural, and conditional knowledge (Schraw et al., 2006 ; Sperling et al., 2004 ), while metacognitive regulation is about controlling thought processes and monitoring cognition, which involves planning, implementing, monitoring, and evaluating strategies (Aaltonen & Ikavalko, 2002 ; Zumbrunn et al., 2011 ).

Metacognitive instructional strategies are used to enhance learners’ effectiveness and support their learning process during the stages of forethought, performance, and self-reflection (Okoro & Chukwudi, 2011 ; Zimmerman, 2008 ). Therefore, metacognitive scaffolding, as described by Zimmerman ( 2008 ), is important in classroom interventions because it promotes problem-solving processes and supports metacognitive activities. According to Sbhatu (2006), understanding metacognitive processes and methods is fundamental for complex problem-solving tasks. Metacognitive functions are categorized based on the phases of the problem-solving method, including problem recognition, presentation, planning, execution, and evaluation (Kapa, 2001 ).

PSMMS in the Face of Globalization and Twenty-First Century Advancements

In the twenty-first century, societies rely on scientific and technological advances, and promoting scientific literacy is crucial for their integration into interactive learning environments (Chu et al., 2017 ). Studies suggest that science, technology, engineering, and mathematics (STEM) education promotes critical thinking, creativity, and problem-solving skills (Widya et al., 2019 ). Therefore, teachers should adopt a learning science and learner-centered approach and focus on higher-order thinking skills and problem-based tasks (Darling-Hammond et al., 2020 ; Nariman, 2014).

The implementation of metacognitive strategies as a scaffold system for the problem-solving method, which simultaneously fosters the development of higher-order skills in their Biology learning, helps students advance in the age of globalization and the twenty-first century. According to Chu et al. ( 2017 ), twenty-first century skills are classified into four categories, such as ways of thinking, ways of working, tools for working, and ways of living in an advanced world. Therefore, studies suggest that teachers can help students develop twenty-first century skills and influence learning through metacognition, thereby promoting self-directed learning (Stehle & Peters-Burton, 2019 ; Tosun & Senocak, 2013 ).

The Problem-Solving Method and Metacognition in Biology Instruction in Ethiopia

The National Education and Training Policy emphasizes the importance of education, particularly in science and technology, in improving problem-solving skills, cultural development, and environmental conservation for holistic development (ETP, 1994 ). Similarly, the 2009 Ethiopian Education Curriculum Framework Document highlights higher-order skills as key competencies and promotes the application, analysis, synthesis, evaluation, and innovation of knowledge for the twenty-first century (MoE, 2009 ). Whereas, a third revision of the curriculum is needed to promote science and technology studies with an emphasis on advanced cognitive skills and a shift from teacher-centered to learner-centered instructional methods (MoE, 2020 ).

The 2009 curriculum framework also places a strong emphasis on Biology as a life science, promoting understanding of self and living things while encouraging critical thinking and problem-solving. Biology lessons that integrate the problem-solving method can enhance students’ academic performance and understanding of the subject (Agaba, 2013 ). However, the Ethiopian education system faces challenges due to limited instructional resources, poor instructional methods, and a lack of experience in practical (hands-on) activities (Eshete, 2001; ETP, 1994 ; MoE, 2005 ; Negash, 2006 ). On the other hand, teachers’ inability to demonstrate effective instructional practices may contribute to low academic performance (Ganyaupfu, 2013 ; Umar, 2011 ).

Challenges in Implementing the PSMMS in Biology Instruction

Metacognitive processes are crucial for guiding learners in problem-solving activities (Sbhatu, 2006), but assessing them can be challenging due to their covert nature (Georghiades, 2000 ). Just like other areas of study, implementing metacognitive scaffolding of the problem-solving method in Biology instruction faces challenges such as complex learning, outdated skills, self-study, overloaded curricula, and limited resources, as shown in Table  1 .

Context Analysis in the Design of the PSMMS for Biology Instruction

Biology lessons are designed for different contexts and consider factors such as the learning environment, prior knowledge, background information, and cultural orientation (Reich et al., 2006 ). For this study, the three domains of context analysis (learners, learning, and learning task contexts) of Smith and Ragan’s (2005) instructional design model (as cited in Getenet, 2020 ) are adapted to design a context-based PSMMS method to generate authentic examples, strong scenarios, and instructional content, as shown in Table  2 .

Research Design

The study analyzed the learning context, including the available instructional resources and facilities in selected schools in Shambu Town, considering teachers’ and students’ perspectives using a mixed-methods research design (Creswell, 2009 ; Creswell & Creswell, 2018 ).

Study Participants

The study was conducted in public secondary schools in Shambu Town. Two schools, namely Shambu Secondary and Preparatory School (ShSPS) and Shambu Secondary School (ShSS), were selected using purposive sampling. Additionally, two Natural Sciences grade 11 sections, one from each school, were selected for instructional intervention based on feedback from context analysis to design an instructional approach, specifically the PSMMS in this study. Thus, all 12 Biology teachers and 80 eleventh-grade students participated in this study (see Table  4 ).

Data Collection Instruments and Procedure

To analyze the contexts to design a context-driven PSMMS for Biology instruction, data were collected using interviews, observations, and a questionnaire. Interviews were conducted to get insights from teachers, while observations were used to assess classroom instructions and instructional resources. Likewise, a questionnaire was administered to students to collect quantitative data on their opinions about the use of PSMMS in Biology instruction. The questionnaire, which was adapted from existing literature (Kallio et al., 2017 ; Rahmawati et al., 2018 ), was initially produced in English and subsequently translated into local language (Afan Oromo) with the help of both software (English to Oromo translator software) and experts. The questionnaire was pilot-tested on a sample of 40 students (22 males and 18 females) to identify any deficiencies in the measuring instrument, and responses were rated on a five-point Likert scale ranging from strongly agree ( N  = 5) to strongly disagree ( N  = 1). The reliability score of the questionnaire was determined to be 0.895, which is at a good level of acceptability.

In this design-based research (DBR) to design an instructional approach for context-driven PSMMS, the data collection process follows a context analysis procedure. Subsequently, the quantitative data collection method is based on the qualitative approach. Accordingly, assessing the context and literature was the first step in the research process. The qualitative approach used interviews and observations for data collection and was also used to identify instructional deficiencies and formulate questions for quantitative data collection.

Data Analysis

This context-based study used both qualitative and quantitative methods to analyze the data collected. In this context-based study, data analysis was conducted on the complex networks of contextual components (Wang & Hannafin, 2005 ). According to Table  2 , the domains of context analysis and key themes that emerged and were applied in this study are listed in Table  3 .

Qualitative data included interviews and notes recorded on the observation checklist. These were analyzed through thematic categorization. Each record was first transcribed, imported into Excel for filtering, and then sent back to Microsoft Word for highlighting. The transcripts were read several times to get a feel for the whole thing. The observation checklist was assessed by watching video recordings and taking notes. However, SPSS software version 24.0 was used to analyze quantitative data using descriptive and inferential statistics, including frequency, percentage, mean, standard deviation, and one-sample t-test.

Results and Discussions

In the study, a total of 12 Biology teachers participated, with 11 males and one female. As displayed in Table  4 , 41.67% of the teacher participants were from ShSPS, while 58.33% were from ShSS. The majority of these teachers had master’s degrees and had over ten years of teaching experience. As for the students involved, 52.5% were from ShSS and 47.5% were from ShSPS. The sex ratio among the students was 51.25% males and 48.75% females (Table  4 ).

Teachers’ Context Analysis

Beliefs about the practices of using the psmms in biology instruction.

The study analyzed teachers’ beliefs about the importance of the PSMMS in Biology instruction. Accordingly, most teachers interviewed (10 out of 12) stated that PSMMS improves students’ learning by enhancing their thinking skills, subject understanding, self-directed learning techniques, and behavior change, suggesting that it has a significant impact on students’ learning. About this, the study participant gave the following illustrative response:

In my opinion, using PSMMS in Biology classes improves students’ higher-order thinking skills by allowing them to understand and articulate problems in their context, stimulate reflection, and promote practical application knowledge (Teacher 4, ShSPS).

Concerning supportive learning, most of the teachers (nine out of 12) believed that it could enhance students’ engagement despite challenges in understanding and learning. About this, research participants said the following:

The PSMMS provides an engaging approach to Biology learning that promotes students’ active engagement and strengthens their awareness and understanding of the objectives and concepts they are expected to understand (Teacher 1, ShSS). Despite the challenge, I believe that using metacognitive scaffolding in the problem-solving method will help students develop their critical thinking skills. In addition, both teachers and students enjoy participating in the teaching-learning process in a classroom environment that is conducive to learning (Teacher 4, ShSPS).

The majority of teachers (eight out of 12) interviewed about PSMMS in Biology instruction argued that it is not commonly used in classrooms and instead relies on established methods like group discussions, pre-learning questions, projects, and quizzes. Some sample responses from teachers are:

The problem-solving method augmented by metacognition is crucial to learning Biology, although students and teachers have limited experience. However, motivated students using this strategy can make the Biology learning experience attractive (Teacher 2, ShSPS). Most students find learning Biology through the PSMMS a tiresome activity and believe that it is too challenging to achieve their learning goals (Teacher 1, ShSPS). The inability to implement the PSMMS in Biology learning experiences is attributed to inadequate laboratory equipment, teaching aids, and school facilities (Teacher 7, ShSS). On some occasions, I provide students with classwork, plans for implementing teaching strategies, arrange group discussions, and assist them in practicing subject-related skills. I then provide background information, promote class engagement, guide responses to questions, assess students’ existing knowledge and goals, provide relevant comments, and guide their thinking (Teacher 4, ShSPS).

Based on the results of the data analysis, it was found that teachers’ perceptions of the importance of the PSMMS to students’ Biology learning contributed significantly to the analysis of the learning context. Accordingly, the contribution of the PSMMS was to enhance students’ Biology learning by improving their critical thinking and learning experiences. Consistent with these findings, teachers’ positive beliefs about classroom problem-solving processes influence their approach to effective Biology teaching (Ishaku, 2015), and integrating metacognitive classroom interventions improves student learning, as evidenced by changes in conceptual learning and problem-solving skills (Guterman, 2002 ; Howard et al., 2001 ).

Observation of Teachers’ Classroom Instruction

The classroom instructional situation was observed to examine the effectiveness of PSMMS for Biology instruction. Consequently, teachers’ use of the PSMMS in Biology lessons was observed. According to the observation checklist, a total of 12 lessons, each lasting 40 minutes, were audited. The first step was to examine teachers’ daily lesson plans. Objectives were found to center predominantly on cognitive domains, neglecting higher-order problem-solving and metacognitive skills. This was evident from the use of terms such as “understand,” “know,” “write,” “explain,” and “describe” in the lesson plan objectives, which hold little significance for teaching Biology using the PSMMS. This finding is consistent with previous research (Chandio et al., 2016 ; Hyder & Bhamani, 2016 ) showing that the objectives of classroom lesson plans often focus on the lower cognitive domain, indicating lower-level knowledge acquisition.

Observing how teachers deliver lessons in the classroom revealed that they often require students to participate in group discussions, which they believe is a learner-centered approach. However, student engagement was limited, and the details of the tasks that students were expected to discuss were not outlined. Additionally, in the lessons observed, teachers failed to engage students, connect theory with practical applications, or support activity-based learning. On the other hand, teachers still have limited opportunities to assess understanding through targeted questions and encourage the use of critical thinking skills. Only oral questions, tests, or quizzes are used as an assessment method. These results were contradictory to the findings of other researchers’ studies, such as Ahmady and Nakhostin-Ruhi ( 2014 ) and Ishaku (2015), where teachers’ classroom lesson delivery is based on students’ constructivist and learner-centered environment acquiring advanced and critical thinking skills from Biology lessons.

The observation raised further questions regarding multimodal lesson delivery, revealing the use of visual representations of figures and diagrams in addition to the usual lecture style (auditory), raising additional concerns about multimodal instructional delivery. Therefore, there was no way to verify whether students had acquired the required higher-order skills, such as problem-solving and metacognitive skills, during their Biology learning. This finding contradicts the findings of Syofyan and Siwi’s ( 2018 ) research, which claims that students’ learning approaches are influenced by their sensory experiences. Consequently, students employ all their senses to capture information when teachers employ visual, auditory, and kinesthetic learning styles.

Students’ Context Analysis

The section presents the results of students’ responses collected using survey questions. Using a questionnaire with a five-point Likert scale ranging from strongly agree to strongly disagree (5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, and 1 = strongly disagree), the impact of using PSMMS in Biology learning practices on students’ problem-solving and metacognitive skills was examined. The questionnaire had a response rate of 80 out of 98 (81.63%), indicating satisfactory status and acceptable use of the instrument. Therefore, in students’ responses to the survey questions on Biology learning practices using the PSMMS, there is significant ( p  < 0.05) variation across all dimensions of the items (M = 4.32, SD = 1.30), with mean scores above 4 indicating general students’ agreement with most items listed in Table  5 .

Regarding the problem-solving skills (Items 1–5) that students would acquire in their Biology learning practices using the PSMMS in Biology lessons, the strongest agreement was to investigate and identify the most effective problem-solving strategies (Item 4, M = 4.25, SD = 1.11), followed by creating the framework and design of the problem-solving activities (Item 2, M = 4.05, SD = 1.16), appropriately evaluating the results and providing alternative solutions to the problems (Item 5, M = 3.91, SD = 1.21), and identifying the problem in the problem sketch and interpreting the final result (Item 1, M = 3.90, SD = 1.28). On the other hand, students typically expressed less positive views about the PSMMS’s use of Biology instruction to enhance laboratory knowledge and problem-solving skills (Item 3, M = 3.25, SD = 1.57), despite significant differences in response patterns (Table  5 ).

Concerning students’ responses to the questionnaire items on metacognitive skills (Items 6–15) acquired in their Biology learning practices using the PSMMS, Table  5 shows that the most positive item states that the use of the PSMMS helps set clear learning objectives (Item 7, M = 4.36, SD = 1.09) and evaluates success by asking how well they did (Item 15, M = 4.29, SD = 1.10). Students tended to be less positive about learning Biology using the PSMMS, which is used to create examples and diagrams to make information more meaningful (Item 9, M = 3.83, SD = 1.21), despite the wide range of response patterns (Table  5 ). As a result, using PSMMS in Biology instruction helps students learn essential planning (Items 6–8), implementing (Items 9 and 10), monitoring (Items 11 and 12), and evaluating (Items 13–15) strategies for practice and to learn real-world applications of Biology (Table  5 ).

After data analysis of students’ responses to the survey questions, it was found that the PSMMS instructional approach is effective in helping students acquire problem-solving and metacognitive skills in their Biology learning practices. However, teachers’ responses, classroom observations, and resource availability indicated that the PSMMS approach was not effectively used to improve students’ problem-solving skills and strategies in Biology learning. The study highlights the disadvantages of shortages of laboratory facilities and large class sizes when implementing learner-centered practices in schools. These issues are supported by Kawishe’s (2016) study. Additionally, the PSMMS was not effectively applied in Biology instruction, resulting in students’ inability to develop metacognitive strategies and skills. Therefore, as studies have shown, students face challenges in acquiring metacognitive knowledge and regulation, which are crucial for the development of higher-order thinking skills in Biology learning (Aaltonen & Ikavalko, 2002 ; Lai, 2011 ).

Learning Context Analysis

This section presents the learning context analysis of PSMMS-based Biology instruction for two aspects, namely the availability of instructional resources in laboratories and pedagogical centers and the challenges in implementing the PSMMS in Biology instruction at Shambu Secondary and Preparatory School (ShSPS) and Shambu Secondary School (ShSS). Each is described below.

Availability of Instructional Resources in the Laboratories and Pedagogical Centers

In this section, a physical observation was conducted to assess the availability of instructional resources in Biology laboratories and pedagogical centers. The observation checklists were used to examine the impacts of their availability on Biology instruction using PSMMS.

Concerning the observations of the laboratory resources, it was noted that the two schools have independent Biology laboratories, but their functioning is hindered by poor organization, display tables, and a lack of water supply and waste disposal systems, as shown in Table  6 . Some basic laboratory equipment and chemicals, including dissecting kits, centrifuges, measuring cylinders, protein foods, sodium hydroxide solution, 1% copper (II) sulfate solution, gas syringes, and hydrogen peroxide, are missing. One school, ShSS, has only seven resources out of 20 identified for observation, making it difficult to conduct laboratory activities (Table  6 ).

Regarding the observations of instructional or teaching resources in the pedagogical centers, the results are shown in Table  7 . The results showed that there were no independent or autonomous pedagogical centers in the two schools; instead, they used the Biology department offices as a pedagogical center and kept some teaching and learning aids there. On the other hand, only DNA and RNA models were accessible in ShSPS, while models of DNA and RNA as well as illustrations depicting the organization of animal cell structures were available in ShSS (Table  7 ).

Challenges of Using the PSMMS in Biology Instruction

In this case, the results of interviews with teachers and survey results from students about the challenges they encountered when using the PSMMS in Biology instruction were used. The results of teachers’ and students’ responses are described below.

Teachers’ interview responses regarding the challenges they encountered in implementing the PSMMS in Biology instruction served as the basis for teachers’ perspectives . With the exception of two teachers who gave insignificant responses, the other teachers’ responses were categorized thematically. Therefore, Table  8 contains the response categories by themes, the number of respondents (N), and examples of responses. According to most teachers ( N  = 10), there is a lack of the required up-to-date knowledge, skills, and experience, and for other teachers ( N  = 7), there are shortages of equipment and chemicals (in Biology laboratories) as well as instructional aids (in pedagogical centers), which are challenges of using the PSMMS in Biology instruction. They also mentioned that challenging factors, such as the high student-teacher ratio and time constraints ( N  = 4), students’ deficiency of knowledge and attitudes towards learning ( N  = 3), and problems with school administrative functions ( N  = 1), have an impact on how well students learn Biology while using the PSMMS instructional approach (Table  8 ).

Students’ perspectives , however, were based on their responses to survey questions concerning the challenges of using the PSMMS in Biology lessons, as shown in Table  9 below. The study found statistically significant ( p  < 0.05) differences across the five-item dimensions, with an average mean of 3.62 and a standard deviation of 1.36. Consequently, mean scores above 3 indicated that students agreed with the challenges of implementing the PSMMS in Biology instruction (Table  9 ).

As shown in Table  9 , the majority of students identified two key challenges to successfully implementing the PSMMS in their learning. These are shortages of instructional resources (Item 2, M = 3.56, SD = 1.39) and student difficulty in connecting their prior knowledge with Biological concepts (Item 1, M = 3.44, SD = 1.42). On the other hand, students responded that their teachers had the knowledge and awareness to conduct instructional processes using the PSMMS (Item 4, M = 3.95, SD = 1.22) and had the skills and competence to conduct instructional processes using the PSMMS (Item 5, M = 3.98, SD = 1.35). Table  9 also shows that, despite significant differences in response patterns, students generally had a negative opinion about the dominance of some students in collaborative work (Item 3, M = 3.16, SD = 1.43).

According to the analyzed data, one of the challenging factors was that teachers often lack the required knowledge and skills to facilitate learning, scaffold it, and successfully implement PSMMS in Biology instruction. In contrast, Belland et al. ( 2013 ) suggested that instructional scaffolds increase students’ autonomy, competence, and intimacy, which improves their motivation and enables them to identify appropriate challenges. The other challenging factor that influenced the use of the PSMMS in Biology instruction was the shortage of instructional resources and facilities. Consistent with the studies of Daganaso et al. ( 2020 ) and Kawishe (2016), the use of the PSMMS for Biology instruction faces challenges due to inadequate instructional resources, time constraints, and large class sizes. However, as Eshete (2001) describes, students lack the importance of instructional resources, as instructional resources are necessary for students to learn Biology effectively as they are essential for a deeper understanding of science.

Generally, the important findings from the analyses of the teachers, learners, and learning contexts and their implications for design principles are summarized in Table  10 .

Conclusions

In this study, contexts (teachers, students, and learning) were analyzed with the aim of designing a context-driven problem-solving method with metacognitive scaffolding (PSMMS) for Biology instruction. Despite the potential benefits of the PSMMS, the findings of the current study indicate that the use of the PSMMS instructional approach faces challenges. These challenges include teachers’ lack of the required up-to-date knowledge and skills, students’ lack of awareness and positive attitude towards learning, an overloaded curriculum, scarcity of resources, large class sizes, and problems with school administrative functions. The study emphasizes the significance of context analysis in the design of an effective PSMMS instructional method for enhancing students’ learning in Biology. This analysis provides useful information for providing pertinent examples, practical content, and context-driven instruction.

The context-driven instructional design approach, using the PSMMS, addresses problems in teachers’ effectiveness, students’ effective learning, and the establishment of supportive teaching and learning environments. This approach considers the performance of both teachers and students, as well as the learning environment, including the availability of instructional resources. Consequently, this study concludes that understanding the needs of teachers in relation to the PSMMS can help both teachers and educational policymakers design a system that is well-suited to their specific requirements. Additionally, it can help students use their practical skills as well as establish connections between their prior knowledge and the Biology concepts they are learning. This process has the potential to generate innovative systems for applying the PSMMS instructional approach, with teachers serving as facilitators and students actively engaging and taking responsibility for their own learning progress.

The study investigated the importance of incorporating target groups into the design of the PSMMS for Biology instruction. The study’s empirical findings support the notion that the PSMMS should provide regular learning opportunities and foster the active engagement of teachers. The study also emphasizes the need to consider learning contexts while designing the PSMMS for Biology instruction that is deeply rooted in its particular context, as effective principles applied in one context could not yield the same results in another context. The study suggests that this strategy is particularly useful in developing countries like Ethiopia, where there is limited experience with metacognitive strategies to scaffold the problem-solving method in Biology instruction. As a result, the authors recommend expanding the target audience, considering the national context, and incorporating metacognitive knowledge and regulation strategies in designing context-driven PSMMS for secondary school Biology instruction.

Data Availability

The authors confirm that the results of this study are available in the article and its supplementary material, and raw data can be obtained from the corresponding author upon reasonable request.

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Eticha, M.D., Hunde, A.B. & Ketema, T. Designing a Context-Driven Problem-Solving Method with Metacognitive Scaffolding Experience Intervention for Biology Instruction. J Sci Educ Technol (2024). https://doi.org/10.1007/s10956-024-10107-x

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Bridging the divide: supporting and mentoring trainees to conceptualize, plan, and integrate engagement of people with lived experience in health research

• Soo Chan Carusone 1 , 2 ,
• Cassandra D’Amore 1 ,
• Subhash Dighe 1 ,
• Lance Dingman 1 ,
• A. Tina Falbo 1 ,
• Michael Kirk 1 ,
• Joyce Luyckx 1 ,
• Mark McNeil 1 ,
• Kim Nolan 1 ,
• Penelope Petrie 1 ,
• Donna Weldon 1 ,
• Rebecca Ganann 1 , 3 &
• Brenda Vrkljan 1 , 4  

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Health researchers are encouraged by governments, funders, and journals to conduct research in partnership with people with lived experience. However, conducting research with authentic engagement and partnership with those who are experts by experience, but may not have research methods training, requires resources and specialized skills. The McMaster Collaborative for Health and Aging developed a fellowship program for trainees that builds their capacity to conduct research in partnership with older adults with relevant lived experience. We share this case example, with its successes and challenges, to encourage creative reformation of traditional research training.

The Collaborative used an iterative design process, involving researchers, trainees and older adult and caregiver partners, who, together, developed a fellowship program for trainees that provides support and mentorship to plan and conduct health research in partnership with people with lived experience.

Since 2022, the Partnership in Research Fellowship has been offered biannually. The application process was purposefully designed to be both constructive and supportive. Opportunities for one-on-one consultations; key resources, including a guide for developing a plan to involve people with relevant lived experience; and feedback from older adult and researcher reviewers are provided to all applicants. Successful trainees engage with older adult and caregiver partners from the Collaborative to advance and enhance a range of skills from facilitating partner meetings to forming advisory committees. Trainees are awarded $1500 CAD to foster reciprocal partnerships. Ten graduate students from various disciplines have participated. Trainees reported positive impacts on their knowledge, comfort, and approach to partnered research. However, the time required for undertaking partnered research activities and involving diverse partners remain obstacles to meaningful engagement.

Partnering with people with lived experience in the design of educational programs embeds the principles of partnership and can increase the value and reward for all involved. We share the Partnership in Research Fellowship as a case example to inspire new and transformative approaches in research training and mentorship that will move the field forward from engagement theory to meaningful enactment.

Plain English summary

Health researchers are encouraged by governments, funders, and journals to conduct research in partnership with individuals with relevant health conditions or experience. However, conducting research with individuals who are experts by experience, but may not have research training, requires resources and specialized skills. The McMaster Collaborative for Health and Aging developed a fellowship program to support and mentor trainees to conduct their research in partnership with people with lived experience and turn engagement theory into action.

The Collaborative involved researchers, trainees, and older adults in the development of the fellowship program. Since 2022, the Partnership in Research Fellowship has been offered twice a year. The application process was designed to be both supportive and informative. Opportunities for one-on-one consultations; key resources, including guiding questions to consider when planning to involve people with relevant lived experience; and feedback from older adults and researchers, are provided to all applicants. Each trainee receives $1500 CAD to support building strong, two-way partnerships. Since the fellowship’s launch, 10 graduate students from different fields have participated. Trainees reported improvements in their knowledge and comfort to partner with people with lived experience in research. However, challenges, such as the extra time needed for conducting partnered research as well as locating and involving those from diverse backgrounds, were identified.

Involving people with lived experience in the design of research training incorporates partnership principles and may enhance the benefits and satisfaction for everyone involved. We share the Partnership in Research Fellowship, as an example, to inspire new approaches in research training and mentorship.

Peer Review reports

Health research is increasingly embracing the inclusion of persons with lived experience to advance health and healthcare systems [ 1 , 2 ]. Governments, research funders, and academic journals [ 3 , 4 , 5 , 6 ] are increasingly encouraging and mandating that researchers collaborate with individuals and communities whose expertise stems from lived experience rather than formal education or credentials. In Canada, the involvement of experts by experience in health research is broadly referred to as patient-oriented research which the Canadian Institutes of Health Research (CIHR) describes as “… a continuum of research that engages patients as partners, focusses on patient-identified priorities and improves patient outcomes. This research, conducted by multidisciplinary teams in partnership with relevant stakeholders, aims to apply the knowledge generated to improve healthcare systems and practices” [ 3 ]. With the ultimate goal of improving patient outcomes, CIHR partnered with the provinces, territories, academic institutions, charities and others, to develop and implement a Strategy for Patient-Oriented Research (SPOR) to transform how research is conducted [ 3 ]. As part of this strategy, provincial and territorial SUPPORT (Support for People and Patient-Oriented Research and Trials) Units were established to champion and provide infrastructure for patient-oriented research in the provinces and territories [ 4 ].

Some have referred to the involvement of people with lived experience as a paradigm shift in health research; reflecting an increasing commitment to foster more equitable and inclusive research practices that amplify the voices of those who have direct experience and personal knowledge of the health-related issues under investigation [ 7 ]. The justification behind this shift in health research can be categorized into three distinct yet complementary rationales, namely: (1) a moral duty of researchers and a right of “patients” to be involved in research related to their medical condition; (2) to improve the relevance, feasibility, value, and impact of health research; and (3) to increase the transparency and accountability across the research process to improve public trust in science and research [ 8 , 9 ]. This paradigm shift has the potential to enhance the relevance and applicability of research findings [ 10 ] and may empower individuals and communities whose voices have been historically marginalized [ 11 ].

Setting and context

The McMaster Collaborative for Health and Aging is a part of the O ntario S POR S UPPORT U nit (OSSU) network of research centers that champions patient-oriented research in the province [ 4 ]. OSSU is jointly funded by the Canadian Institutes of Health Research, the Government of Ontario and partner Ontario hospital foundations and institutes. As one of the 11 SPOR SUPPORT Units across the country, and as part of the Strategy for Patient Oriented Research, capacity development is one of OSSU’s four pillars for providing infrastructure, expertise and support to people engaged in patient-oriented research. The McMaster Collaborative for Health and Aging (herein, ‘the Collaborative’) is a coalition of researchers, trainees, older adults, and caregivers working together to improve the health equity and well-being of older Canadians by advancing patient-oriented health research on aging.

Conducting research in partnership with individuals and/or communities who are experts by experience, but may not have formal education in research methods, necessitates a nuanced set of skills, ethical considerations, and methodological approaches that extend beyond conventional research paradigms and training [ 12 , 13 , 14 ]. While the potential benefits of this approach are strong, the risks of engagement gone wrong should not be ignored [ 7 , 15 , 16 , 17 ].

In this paper, we introduce a training fellowship that has been co-developed by the Collaborative’s researchers, trainees, and older adult and caregiver partners. This fellowship aims to provide trainees with the support and mentorship to gain experiential knowledge in meaningful and ethical engagement of experts by experience in research on aging. We share this program as a case example, with its successes and challenges, to encourage the development of creative training and mentorship programs. Our goal is to go beyond acknowledging the value of inclusion and contribute to structures that bridge the divide between theory and action.

McMaster Collaborative for Health and Aging Partnership in Research Fellowship

Planning and objectives.

The Collaborative identified a critical gap for specialized training to foster authentic partnerships and facilitate patient-oriented research on aging. Supported by a mandate and funding for capacity development, the Collaborative involved researchers, trainees and older adult and caregiver partners in an iterative design process to bridge this training gap. The first step was brainstorming sessions where priorities and existing barriers for trainees to conduct their research in partnership with older adults were identified. Trainees were interested in formal training related to patient-oriented research and opportunities to interact and learn from researchers with experience engaging people with lived experience in their research. Specific questions they identified, related to gaps in their training, included how to engage marginalized groups of older people and how to communicate with partners with lived experience to keep them engaged as meaningful research contributors throughout the research process. From these sessions and corresponding discussions with Collaborative leadership members and the team of older adult and caregiver partners, the Managing Director drafted a training and mentorship proposal, including potential goals, eligibility, and program components, for further rounds of review, feedback, and refinement. This proposal was presented to the team of older adult and caregiver partners for discussion and feedback, with specific questions about their interest and comfort with potential roles and activities (e.g., as members of the application review committee). Collaborative leadership members and trainees who had expressed an interest in initiatives that addressed their identified needs were also sent documents at two stages and invited to provide feedback electronically or through individual discussions. From this process, a training fellowship that facilitates health research conducted in partnership with older adults with relevant lived experience was developed.

The Partnership in Research Fellowship was designed as an activity to contribute to the Collaborative’s goal to build capacity in patient-oriented research specific to health and aging, including building awareness of resources that support the implementation of Canada’s Strategy for Patient Oriented Research (SPOR). More specifically, the fellowship aims to provide trainees with the support and mentorship to conduct meaningful and ethical engagement of experts by experience in their graduate or post-doctoral aging research. The secondary objective, informed by the interests and positive experiences of our members, is to provide opportunities for trainees and the Collaborative’s older adult and caregiver partners to exchange knowledge and expertise.

The Collaborative’s core principles for partnership (clear communication; information exchange; empowerment; transparency; mutual respect; and responsiveness) [ 18 ] and our commitment to improving health equity and fairness in research [ 19 ] provide the foundation for this trainee fellowship and informed the design process.

Program design and structure

This training opportunity was designed to be informative and constructive for trainees regardless of their eligibility or the success of their application (see Table  1 for key components of the fellowship). For example, before they apply, interested trainees must participate in a one-on-one consultation meeting to help develop their partnership plan. These consultations provide applicants with the opportunity to brainstorm various ways they can involve experts by experience in their research. Approaches discussed vary based on the stage of the project, the research question and methods, and the population most affected. Recommended partnership activities may include, but are not limited to, partners being involved in data collection (e.g., peer interviewers), the creation of an advisory committee, a community consultation event to inform the research question, and/or the recruitment of co-investigators with lived experience. Strategic advice offered to applicants can include frank discussions of potential challenges and the feasibility of various timelines. In addition, all trainees (whether their application is successful or not) receive written feedback on their applications, including constructive feedback from older adult and caregiver partners. All applicants are offered the opportunity to have a one-on-one meeting with the Managing Director of the Collaborative to discuss their feedback, potential next steps, and ask general questions about partnering in research.

Successful trainees are awarded $1500 CAD to support their work in engaging people with relevant lived experience in their research. After meeting with the Managing Director to discuss the reviewer feedback, trainees are provided with materials to support their planning, execution, and follow-up of meetings or activities with people with lived experience. For each fellowship trainee, we organize a meeting with the Collaborative older adult and caregiver partner team for the trainees to present their research and revised engagement plan (based on the reviewer feedback) for discussion and further guidance. Trainees identify goals for the meeting and relevant questions for the older adult and caregiver partner team, which vary according to the trainee’s project and timeline. For example, one trainee sought feedback on their community advisory committee recruitment materials, another had questions about her plan for onboarding new partners (with lived experience). In addition to providing trainees with further feedback and advice on their engagement plans, these meetings offer a supported opportunity to facilitate a meeting with older adults and practice communicating about their research in accessible ways. After the meetings, Collaborative partners anonymously submit feedback and suggestions for the trainees specific to their communication and facilitation of the meeting.

Over the remaining six to 18 months of their fellowship, trainees implement their engagement plans, identifying and working with people with relevant lived experience to advise on their ongoing research. Fellowship trainees meet as a group three times per year to discuss their successes and challenges and share relevant information and resources. Every six months they are required to submit an activity report. We created these reports to encourage reflection, support the advancement of trainees’ engagement efforts, and assess the program’s effectiveness. They also provide important information to refine and enhance the program. In addition to reporting on the engagement activities conducted, trainees are asked their greatest challenges and most valuable learning, to reflect on their successes and challenges related to the equity, diversity and inclusion of their engagement activities, the impact of the fellowship, and their recommendations for improvement. Fellowship trainees are also encouraged to engage in other Collaborative activities (e.g., Journal Club, seminar series) and to reach out for advice, mentorship, or support related to their partnered research, at any time.

The removal of financial barriers was not a primary goal of this initiative. However, we included financial support to increase the diversity of applicants and to ensure that the partnerships that are supported were reciprocal, ethical, and aligned with our values. We wanted this training opportunity to be accessible to trainees, regardless of their research funding, research environment, and level of patient-oriented research experience of their supervisor(s). Financial support ensured trainees could recognize the value of lived expertise through honorariums and co-production and provides the opportunity for trainees to develop, and receive feedback on, a budget to support their engagement plans. Applicants are required to submit a budget (maximum $1500 CAD) with at least 25% allocated to directly support people with lived experience (e.g., honourariums, travel reimbursement, training).

Implementation

To date, the program has been coordinated online. Meetings with the fellowship review team and the trainees’ meetings with Collaborative staff and older adult and caregiver partners have occurred via Zoom. However, funded trainees are encouraged to choose their location and means of partner engagement based on the preferences of the experts by experience they plan to engage and the feasibility for their project. Virtual meetings have both advantages (e.g., flexibility, reduced transportation burden) and disadvantages (e.g., digital literacy and access requirements, reduced non-verbal communication) when compared to in-person meetings.

All 10 of the Collaborative older adult and caregiver partners have been actively engaged in the implementation of this program. Partner team meetings with fellowship trainees have had a minimum of five partners. Seven of the 10 partners have served at least once as a member of the application review committee (with two partners per round).

We have integrated a quality improvement framework into the program and encourage informal feedback and suggestions throughout the year. We also collect information from all stakeholder groups: through the fellowship trainee 6-month reports; through supervisor support forms at the application phase; and after partner team meetings with fellowship trainees. At the end of review committee meetings, we reflect on the latest round of applications and discuss potential improvements for the next program offering based on our individual and collective experiences with the program operations.

Fellowship outcomes

At the time of writing, the fellowship has been offered a total of four times in two years (every six months). We have supported and mentored 10 graduate students, from various disciplines, in conceptualizing, planning, and implementing engagement of people with lived experience, as experts, in their research. Examples of trainee projects include a systematic review, a qualitative case study, and analysis of secondary data. From evaluations, the Collaborative’s older adult and caregiver partners have unanimously rated the value of this training opportunity as “excellent” in terms of supporting meaningful engagement of older adults and caregivers in aging-focused research. Funded trainees have similarly reported being appreciative of the personalized support and mentorship by the Managing Director and the Collaborative’s older adult and caregiver partners. Trainees have shared their perspectives on the value of the program and a range of “a-ha” moments they have experienced – from valuing the peer component (as a means to share challenges and successes with other fellowship trainees) to learning from the Collaborative’s older adult partners that their research may not be of interest to all audiences. All trainees agreed or strongly agreed that their knowledge about patient-oriented research had increased because of the fellowship program. The impact on other outcomes, such as their comfort with engaging people with lived experience in research, planned or completed engagement activities, and future research plans, were less consistent although such outcomes were identified to be strongly impacted by at least one trainee. Notably, one trainee indicated that the fellowship had highlighted potential risks of engaging people with lived experience, if not done well, which initially affected their comfort of involving those with lived experience in their research project.

Time constraints were the most prevalent challenge identified by trainees: time pressures of competing activities (with their program of study more broadly); difficulty finding mutually agreeable times for meetings with partners with lived experience; and research and engagement steps (e.g., research ethics board approval, connecting with community-based organizations, recruitment of partners with lived experience) taking longer than anticipated. Another challenge identified by trainees was connecting and engaging people within their target population and ensuring that a range of perspectives were represented. Based on conversations, this often resulted in research partners who were less diverse, in terms of important characteristics such as ethnicity, gender, and education, than the study’s target population and what was desired. When asked to reflect on these challenges, trainees noted the need to tailor approaches to specific partner organizations and the importance of reflecting on “who was not at the table.” These challenges are not unique to trainees but may be accentuated in their context where many have limited power and control over external requirements and may also have fewer existing relationships (with community organizations and individuals with relevant lived experience) on which to build and may lack stability for fostering ongoing relationships.

Promising practices for sustainability and scale-up

Reflecting on our experience to date, we believe the co-development of this training opportunity was critical to its success. We continue to both revise the program and celebrate its achievement in supporting our mandate through various knowledge translation activities (e.g., conference presentations, newsletters). The commitment and enthusiasm of the older adult and caregiver partners in reviewing applications, meeting with successful trainees, and offering their guidance on and across their projects, creates a sense of connection and energy for the initiative. The lived experiences of the Collaborative’s older adult and caregiver partners are diverse and extensive in many areas – including culture, education, and health. This broad range of lived experiences benefits the trainees when planning who and how to engage experts by experience in their research and enriches the training for all involved, including trainee (faculty) supervisors. The one-on-one discussions and mentorship components of our fellowship are fundamental to the spirit and goals of the program but could limit scalability of the initiative. However, as more experts by experience, trainees, and researchers develop expertise with research done in partnership, there will be opportunities for additional mentors, peer mentorship, and train-the-trainer models. There are also knowledge syntheses, reflective papers, and resources that are being developed and disseminated, which can support the creation and implementation of training opportunities similar to this training fellowship. Building on existing relationships and structures, we created this program to help us strengthen our institutional mandate. Our approach was largely informed by our collective experiences (as educators, trainees, research partners with lived experience, and researchers with extensive experience working with community), our principles of partnership, and a quality improvement approach (of plan-do-study-act). However, other teams may find resources such as Engage for Equity’s Tools and Resources for Evaluation and Collective-Reflection of Community Based Participatory Research (CBPR) and Community Engaged Research (CEnR) [ 21 ] useful as a framework for planning, reflecting on, and improving their own training programs created in partnership.

As previously mentioned, one trainee in the program shared their increased concern for causing unintentional harm while working in partnership (e.g., through choice of language). The Collaborative promotes reflection and awareness of potential risks of engagement, which have the potential for ripple effects on community trust and relationships between others from the same academic institution. This fellowship program creates an environment for trainees to explore and discuss such risks, allowing them to plan ahead and try to mitigate potential sources of harm. Understanding these risks is a critical part of their learning. Trainees (vs. established researchers) may face additional barriers if conducting their research in partnership and also operate under conditions that may increase the risk of what Richards and colleagues [ 15 ] refer to as “how it can go wrong.” Research conducted by trainees plays a crucial role in the advancement of knowledge. However, mentorship is critical for trainees to develop complex research skills, such as those necessary for conducting research through authentic partnerships, and to conduct ethical and impactful research [ 22 , 23 , 24 ]. As such, mentorship programs may be critical to mitigating these risks and can facilitate trust and reciprocal relationships with community organizations and people with lived experience. Risks that may be especially relevant in trainees’ research are tokenism and the sense of loss at the end of the engagement. A trainee may move on with their career, possibly shifting research focus and changing institutions, without continuing to pursue the broader objectives and subsequent phases of their research project. This situation can leave partners and advisors without the chance to apply their knowledge and insights in activities that build upon the research project. To mitigate this risk, we encourage fellowship trainees to embed reciprocity in their engagement plans throughout their project and to consider the post-project transition for people who have partnered with them: what were their partners’ goals for engaging in the project and are there opportunities or people the trainees can connect them to that may help them achieve their goals? As an organization, we can also help to bridge this gap, by acting, in part, as a central hub for researchers and partnership opportunities.

We share the Partnership in Research Fellowship as a case example with the aim of inspiring other innovations in research training and mentorship that support moving from engagement theory to meaningful enactment. To support a paradigm shift in what and how research is conducted and how it is used to improve health, health care, and equity – we must support the researchers of tomorrow to engage with people and communities with relevant lived experience from the outset, not as an afterthought. Mentorship programs can provide the opportunity for learning, experiencing, and conducting research in partnership with the values foundational to its success. Partnering with people with lived experience in the design of educational opportunities for trainees embeds the principles of partnership and can increase the value for all involved.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We would like to acknowledge the members of the McMaster Collaborative for Health and Aging who have supported this initiative including Alison Finney, Karina Tavernese, and members of the fellowship review committee.

Funding for the McMaster Collaborative for Health and Aging is provided by the Ontario SPOR SUPPORT Unit, which is supported by the Canadian Institutes of Health Research, the Province of Ontario, and partner Ontario hospital foundations and institutes.

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Soo Chan Carusone, Cassandra D’Amore, Subhash Dighe, Lance Dingman, A. Tina Falbo, Michael Kirk, Joyce Luyckx, Mark McNeil, Kim Nolan, Penelope Petrie, Donna Weldon, Rebecca Ganann & Brenda Vrkljan

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School of Nursing, McMaster University, HSC2J20 1280 Main St. W, Hamilton, ON, L8S 4L8, Canada

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SCC, CD’A, SD, LD, ATF, MK, JL, MM, KN, PP, DW, RG and BV contributed to the design of the fellowship program and analytical discussions of the program and lessons learned. SCC led the writing of the manuscript. All authors contributed to the writing and revising of the manuscript and read and approved the final manuscript.

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Correspondence to Soo Chan Carusone .

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Chan Carusone, S., D’Amore, C., Dighe, S. et al. Bridging the divide: supporting and mentoring trainees to conceptualize, plan, and integrate engagement of people with lived experience in health research. Res Involv Engagem 10 , 89 (2024). https://doi.org/10.1186/s40900-024-00625-8

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Received : 27 May 2024

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DOI : https://doi.org/10.1186/s40900-024-00625-8

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