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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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Broadening the definition of ‘research skills’ to enhance students’ competence across undergraduate and master’s programs.

1. Introduction

2. statement of the problem, 3. theoretical framework, 4. context and methodology.

• Targeted—research skills that faculty explicitly stated as a goal of their courses or programs, or that groups of experts considered to be important during their research experiences;
• Perceived—research skills that students or faculty believed were developed during the course or program experience;
• Assessed—research skills determined to have been developed during one’s program using objective measurements, such as assessments guided by in-depth rubrics.

5. Seven Core Research Skills Transferable across Disciplines and Degrees

• Critical appraisal—evaluating the methods, data, and conclusions of published research to determine its validity and reliability;
• Information synthesis—combining information from various sources in a logical manner to draw conclusions;
• Decision making—selecting and executing a specific course of action;
• Problem solving—identifying sources of difficulty and finding reasonable and effective solutions to them;
• Data collection—gathering information using structured methods to support the objectives of the study;
• Data analysis—manipulating and modelling data to reveal trends and correlations to make conclusions related to a set of study objectives;
• Communication—the sharing of information with others through either written or verbal means.

6. The Importance of Research Skill Development in Academia and Beyond

7. how can research skills be explicitly addressed in undergraduate and master’s curricula, 8. conclusions and implications, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Vieno, K.; Rogers, K.A.; Campbell, N. Broadening the Definition of ‘Research Skills’ to Enhance Students’ Competence across Undergraduate and Master’s Programs. Educ. Sci. 2022 , 12 , 642. https://doi.org/10.3390/educsci12100642

Vieno K, Rogers KA, Campbell N. Broadening the Definition of ‘Research Skills’ to Enhance Students’ Competence across Undergraduate and Master’s Programs. Education Sciences . 2022; 12(10):642. https://doi.org/10.3390/educsci12100642

Vieno, Kayla, Kem A. Rogers, and Nicole Campbell. 2022. "Broadening the Definition of ‘Research Skills’ to Enhance Students’ Competence across Undergraduate and Master’s Programs" Education Sciences 12, no. 10: 642. https://doi.org/10.3390/educsci12100642

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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.

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.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

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What are Research Skills? How to Improve Your Skills in Research

Learn strategies and techniques to improve your research skills. Avoid common mistakes and implement proven methods for efficient research. This article offers practical tips to enhance your ability to find and evaluate high-quality information.

Are you struggling to find relevant and reliable information for your research? Do you want to avoid getting lost in a sea of sources and needing help knowing where to start? Improving your research skills is essential for academic success and professional growth.

In today's information age, effectively conducting research has become more important than ever. Whether you are a student, a professional, or simply someone who wants to stay informed, knowing how to find and evaluate information is crucial.

Fortunately, some strategies and techniques can help you improve your research skills and become a more efficient and effective researcher. By avoiding common mistakes and implementing proven methods, you can enhance your ability to find high-quality information and make the most of your research endeavors. This article will explore some practical tips and tricks to help you improve your research skills and achieve better results.

What is Research?

Research is a critical part of learning, problem-solving, and decision-making. It is an essential process used in every field for both the individual and collective’s mutual benefit and success. Research involves systematically gathering data from primary or secondary sources, analyzing it, interpreting it, and communicating its findings to researchers and other interested parties.

Research can be divided into two main categories: quantitative research, which uses numerical data to describe phenomena, and qualitative research, which seeks to understand people's beliefs, opinions, values, or behaviors. Quantitative research often involves applying model-based approaches that can predict outcomes based on observations. It is one of the most powerful methods of discovering information about the world, as it allows for testing hypotheses in a systematic manner. Qualitative research is more exploratory in nature by focusing on understanding the motivations behind what people do or think rather than developing models or producing statistics in order to conclude behavior and relationships between variables. This type of research usually relies more on observation and engagement with people instead of using statistical models.

What are Research Skills?

Research skills are the abilities and talents required to focus on an objective, gather the relevant data linked to it, analyze it using appropriate methods, and accurately communicate the results. Taking part in research indicates that you have acquired knowledge of your subject matter, have digested that knowledge, and processed, evaluated, and analyzed it until you can resolve a problem or answer a query. It is highly beneficial for employers to hire people with strong research skills since they can provide valuable insights and add value to the company’s performance. Therefore, researching effectively has become crucial to securing a job in most industries.

Why Do Research Skills Matter?

Research skills are essential if one intends to succeed in today's competitive world. With technology ever-evolving and a need to stay ahead of the competition, employees who possess research skills can prove invaluable to their employers. These skills include researching, analyzing, and interpreting data and making informed decisions based on that information.

Employers value workers who can quickly develop a thorough understanding of any changes or trends in their field of work through accurate research. Knowing how to assess customer needs, recognize competition, write reports, improve productivity, and advise on investments can also benefit any business. With the help of research skills, companies can uncover ways to adapt their services or products that better serve their customers’ needs while helping them save money at the same time. This makes overall operations more efficient as well as helps a company remain ahead of its competitors.

Essential Research Skills :

Here is a list of essential research skills:

Data Collection

Data collection is an important part of comprehending a certain topic and ensuring reliable information is collected while striving to answer complex questions. Every situation differs, but data collection typically includes surveys, interviews, observations, and existing document reviews. The data collected can be quantitative or qualitative, depending on the nature of the problem at hand. As students advance through university and other educational institutions, they will need to read extensively into a particular field and may even need to undertake comprehensive literature reviews to answer fundamental questions.

The skills acquired through data collection during university are invaluable for future roles and jobs. Gaining experience in understanding complex topics, reading widely on a given subject matter, collecting relevant data, and analyzing findings - all these activities are integral when dealing with any type of project within the corporate sector. Therefore, embarking on various research projects enhances a person's education level and brings about significant professional experience.

Goal-Setting

Setting goals is an important skill for any successful research project. It allows you to stay focused and motivated throughout the process. Goals are also essential in helping with direction: they provide a path to organize our thoughts, narrow our focus, and prioritize the tasks we need to undertake to achieve our desired result. The concept of goal-setting is inherent in most research processes, as everything needs to have something to strive for — whether that’s gaining knowledge about a particular topic or testing a theory.

When it comes to creating and setting goals during the research process, you must have clear and specific objectives in mind from the outset. Writing down your thoughts helps define these objectives, which can inform the data collection process; moreover, thinking about short-term and long-term goals can help you create manageable steps toward achieving them. Learning how to break up larger projects into smaller “mini-goals effectively” can make all the difference when tackling complex investigations — allowing researchers to monitor their progress more easily and culminate results further down the line.

Critical Thinking

Critical thinking is an integral part of the modern workplace. To succeed, one must be able to look at a situation objectively and make decisions based on evidence. The information examined needs to come from various sources, such as data collection, personal observation, or analysis. The goal should then be to take all this information and form a logical judgment that informs an action plan or idea.

Someone who displays strong critical thinking skills will not just accept proposed ideas at face value but instead can understand how these ideas can be applied and challenged. Accepting something without consideration means making the wrong decision due to a lack of thought. Critical thinkers understand how brainstorming works, assessing all elements before forming any decision. From negotiating with colleagues or customers in adversarial scenarios to analyzing complex documents such as legal contracts in order to review business agreements - critical dedicated apply their knowledge effectively and are able to back up their evaluation with evidence collected from multiple sources.

Observation Skills

Observation skills are necessary for conducting any form of research, whether it be in the workplace or as part of an investigative process. It is important to be able to pick up on the details that might otherwise pass unnoticed, such as inconsistencies in data or irregularities in how something is presented, and to pay careful attention to regulations and procedures that govern the company or environment. This can help researchers to ensure their processes are accurate and reliable.

As well as analyzing what we see around us directly, many research methodologies often involve calculated statistical analyses and calculations. For this reason, it’s important to develop strong observation skills so that the legitimacy of information can be confirmed and checked before conclusions are formed. Improving this skill requires dedication and practice, which could include keeping a journal reflecting on experiences, posing yourself questions about what you have observed, and seeking out opportunities in unfamiliar settings to test your observations.

Detail Orientation

Detail orientation is an important research skill for any scientific endeavor. It allows one to assess a situation or problem in minute detail and make appropriate judgments based on the information gathered. A detail-oriented thinker can easily spot errors, inconsistencies, and vital pieces of evidence, which can help lead to accurate conclusions from the research. Additionally, this skill allows someone to evaluate the quality and accuracy of data recorded during an experiment or project more efficiently to ensure validity.

Spotting small mistakes that may otherwise have been overlooked is a crucial part of conducting detailed research that must be perfected. Individuals aiming for superior outcomes should strive to develop their skill at detecting details by practicing critical analysis techniques, such as breaking down large bodies of information into smaller tasks to identify finer points quickly. Moreover, encouragement should also be made for elaborate comparison and analysis between different pieces of information when solving a complex problem, as it can help provide better insights into problems accurately.

Investigative Skills

Investigative skills are an essential component when it comes to gathering and analyzing data. In a professional setting, it is important to determine the accuracy and validity of different sources of information before making any decisions or articulating ideas. Generally, effective investigation requires collecting different sets of reliable data, such as surveys and interviews with stakeholders, employees, customers, etc. For example, if a company internally assesses possible challenges within its business operations environment, it would need to conduct more profound research involving talking to relevant stakeholders who could provide critical perspectives about the situation.

Data-gathering techniques such as comparison shopping and regulatory reviews have become more commonplace in the industry as people strive for greater transparency and more accurate results. Knowing how to identify reliable sources of information can give individuals a competitive advantage and allow them to make sound decisions based on accurate data. Investing time in learning different investigative skills can help recruiters spot applicants dedicated to acquiring knowledge in this field. Developing these investigative skills is also valuable for those looking for executive positions or starting their own business. By familiarizing themselves with their application process, people can become adept at collecting high-quality data they may use in their research endeavors.

Time Management

Time management is a key skill for any researcher. It's essential to be able to allocate time between different activities so you can effectively plan and structure your research projects. Without good time management, you may find yourself hastily completing tasks or feeling stressed out as you rush to complete an analysis. Ultimately, managing your time allows you to stay productive and ensure that each project is completed with the highest results.

Good time management requires various skills such as planning ahead, prioritizing tasks, breaking down large projects into smaller steps, and even delegating some activities when possible. It also means setting realistic goals for yourself in terms of the amount of research that can be achieved in certain timestamps and learning how to adjust these goals when needed. Becoming mindful of how you spend the same hours each day will propel your productivity and see positive results from your efforts. Time management becomes especially relevant regarding data collection and analysis – it is crucial to understand precisely what kind of resources are needed for each task before diving into the research itself. Knowing how much time should be dedicated to each step is essential for meeting deadlines while still retaining accuracy in the final outcomes of one’s study.

Tips on How to Improve Your Research Skills

Below are some tips that can help in improving your skills in research:

Initiate your project with a structured outline

When embarking on any research project, creating an outline and scope document must first ensure that you remain on the right track. An outline sets expectations for your project by forming a detailed strategy for researching the topic and gathering the necessary data to conclude. It will help you stay organized and break down large projects into more manageable parts. This can help prevent procrastination as each part of the project has its own timeline, making it easier to prioritize tasks accordingly.

Using an outline and scope document also allows for better structure when conducting research or interviews, as it guides which sources are most relevant, what questions need to be answered, and how information should be collected or presented. This ensures that all information received through research or interviews stays within the confines of the chosen topic of investigation. Additionally, it ensures that no important details are overlooked while minimizing the chance that extraneous information gets included in your results. Taking this time upfront prevents potential problems during analysis or reporting of findings later.

Acquire expertise in advanced data collection methods

When it comes to collecting data for research purposes, a range of advanced data collection techniques can be used to maximize your efficiency and accuracy. One such technique is customizing your online search results with advanced search settings. By adding quotation marks and wildcard characters to the terms you are searching for, you are more likely to find the information you need from reliable sources. This can be especially useful if, for instance, you are looking for exact quotes or phrases. Different search engines require different advanced techniques and tactics, so learning these can help you get more specific results from your research endeavors.

Aside from using online searches, another standard methodology when conducting research is accessing primary information through libraries or other public sources. A specific classification system will likely be in place that can help researchers locate the materials needed quickly and easily. Knowing and understanding this system allows one to access information much more efficiently while also giving them ample opportunity to increase their knowledge of various topics by browsing related content in the same category groups. Thus, by learning about advanced data collection techniques for both online and offline sources, researchers can make substantial progress in their studies more efficiently.

Validate and examine the reliability of your data sources

Collecting reliable information for research can be a challenge, especially when relying on online sources. It is essential to remember that not all sources are created equal, and some sites may contain false or inaccurate data. It is, therefore important to verify and analyze the data before using it as part of your research.

One way to start verifying and analyzing your sources is to cross-reference material from one source with another. This may help you determine if particular facts or claims are accurate and, therefore, more valid than others. Additionally, trace where the data is coming from by looking at the author or organization behind it so that you can assess their expertise in a particular field and authority on the topic at hand. Once these steps have been completed, you can confidently use this trusted information for your project.

Structure your research materials

Organizing your research materials is an integral part of any research process. When you’re conducting a project or study and trying to find the most relevant information, you can become overwhelmed with all the data available. It’s important to separate valid from invalid materials and to categorize research materials by subject for easy access later on. Bookmarking websites on a computer or using a digital asset management tool are two effective methods for organizing research information.

When researching, it’s critical to remember that some sources have limited value and may be outside the scope of your topic. Recognizing reliable material versus trustworthy resources can be complex in this sea of information. However, sorting data into appropriate categories can help narrow down what is necessary for producing valid conclusions. This method of classifying information helps ensure that vital documents aren't overlooked during the organization process as they are placed in folders shortcutted for quick access within one centralized source whenever needed. Separating valuable sources also makes it easier to reference later on when writing reports or giving presentations - material won't get lost among irrelevant data, and conclusions will be backed by sound evidence.

Enhance your research and communication capabilities

Developing research and communication skills is essential for succeeding academically and professionally in the modern world. The key to improving these skills lies in rigorous practice, which can begin with small projects such as resolving common issues or completing a research task that can be made into a personal project. One way to do this is to volunteer for research projects at work and gain experience under the guidance of experienced researchers. This will improve your research skills and help you develop communication skills when working with others on the project. Another option is to turn a personal project into a research task. For example, if you plan on taking a holiday soon, you could create an objective method to select the best destination by conducting online research on destinations and making informed decisions based on thorough analysis. Practicing in this way enables you to complete any research task confidently and communicate efficiently with ease.

How to Articulate Research Skills on Your Resume

Research projects require commitment and perseverance, making it an important skill to include on a resume. Even if you have had limited research experience throughout your education or previous job, including this in your resume assesses these qualities to potential employers. It's important to consider the extent of your research experience when deciding how to add this part of your background to your resume. If you have been involved with multiple in-depth research projects, it might be best to highlight this by including it as its own section. On the other hand, if the amount of research you have completed is more limited, then try including it in the skills section instead.

When adding research experience and accomplishments into either section of your resume, be sure to emphasize any specific roles or contributions you made during the process instead of just describing the project itself. Furthermore, remember to quantify any successes where possible - this showcases both communication and technical proficiency strengths, which can help make your resume stand out even more. By properly articulating research skills within a resume, employers will likely be more interested in what job seekers have accomplished in their careers.

How to Apply Research Skills Effectively in Your Workplace

Research skills are an invaluable set of abilities to bring to your workplace. To make sure you use them properly, a good place to start is by taking time to plan the project you have been assigned. Whether it’s writing a report or analyzing data, mapping out what tasks you need to do and how long they should take helps to understand the project timeline better. This also makes setting aside dedicated time for research easier too.

To ensure that the decisions made are sound and informed, reading up on the subject area related to the project remains one of the premier ways of doing this. This will help to ensure that any problems arising can be solved quickly and effectively, as well as provide answers before any decisions are actually put into practice. By arming yourself with knowledge gathered through reading about a particular topic, it can give you more confidence when formulating plans or strategies in which direction to take your work in.

Final Thoughts

Research skills are increasingly important in the modern world, and gaining proficiency in this area can significantly benefit a person's career. Research skills are essential for success in many different roles and fields, including those within business and industry, education, science, and medicine. Developing a deep understanding of research allows us to identify problems better and critically evaluate potential solutions. It also bolsters our problem-solving abilities as we work to find creative solutions that meet our efforts' objectives.

By improving your research capabilities, you can impress employers during an application process or when joining a team at work. Research skills are considered soft skills by potential employers since they signal that you have attention to detail while simultaneously demonstrating your ability to learn new things quickly. Employers regard these skills highly, making them one of the key graduate career skills recruiters seek. Furthermore, being able to add ‘research skills’ to your CV will be looked upon favorably by employers and help drive up your employability significantly. Demonstrating that you possess these sought-after traits makes it easier for recruiters to give you the opportunity you've been looking for, so it's worth investing the time into developing these life-long learning tools today.

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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.

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.

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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.

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 .

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

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.

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.

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.

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

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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Unpacking the science of reading – what the research says

There has been a lot of media coverage recently on the science of reading. But what does the evidence say? In their new paper on the topic, ACER Senior Research Fellow, Greta Rollo, and ACER Research Fellow Dr Kellie Picker, from the Effective Practice in Education team synthesise evidence reviews conducted by ACER researchers that unpack the science of reading.

In this 3-part series, Greta and Kellie will explain each of the components that make up the science of reading and share implications for teaching. This first article provides an overview of the 6 key components of the science of reading.

What is the science of reading?

The science of reading is generally used as a catch all expression for the body of research that helps teachers understand what students need to be taught to become effective readers. It is a multi-disciplinary body of research and knowledge from education, linguistics, cognitive psychology, special education, and neuroscience. This article unpacks the 6 key components that make up the science of reading which include:

• Oral language
• Vocabulary (and morphology)
• Reading comprehension
• Phonemic awareness (part of phonological awareness)

For a more detailed discussion please see ACER’s recent paper Unpacking the science of reading research .

These ‘Big 6’ components of the science of reading are inter-related and have different roles at different times in the development of early reading skills. Some, such as phonemic awareness and phonics are somewhat constrained skills. They are largely mastered by the time the child starts reading independently for meaning. Others, such as oral language, vocabulary and comprehension, require deep conceptual development and are unconstrained, which means they can continue to develop for the rest of the child’s life (Turner et al., 2018).

Constrained skills

Phonemic awareness is ‘ the ability to break down and manipulate the individual sounds in spoken language.’ (Stark et al., 2015) . It is part of phonological awareness, which addresses wider spoken language and larger chunks of speech (for example, syllables) . Phonemic awareness includes segmenting words into sounds. It includes blending sounds into words and articulating sounds sequentially to say a word. At its most sophisticated it refers to manipulation of phonemes – deletion, addition and swapping sounds from words to make new words. Phonemic awareness is important for reading development because it supports understanding of the alphabetic principle and orthographic mapping, critical parts of phonics.

Phonics involves combining knowledge of English phonemes (phonemic awareness) with knowledge of English letters (graphemes) to decode words (Rohl, 2000). There are 2 key parts to phonics – the alphabetic principle and word reading by decoding. The alphabetic principle requires mastery of all letter-sound relationships. Combining these skills to learn to decode allows students to read most words they typically encounter. Decoding skills in turn are critical to orthographic mapping, the process whereby students map decoded words and parts of words like morphemes to their current interpretation of their meaning, which supports their reading of irregular words.

Fluency requires accurate reading aloud with appropriate attention to phrasing, intonation and punctuation. Monitoring the development of fluency requires consideration of accuracy and speed, and prosody. Accuracy means reading words correctly. Speed is simply how quickly words are read. Prosody is the use of expression, intonation and phrasing that enhances meaning when reading and is highly correlated with reading comprehension. A students’ reading accuracy and speed can be recorded together in the number of correct words read per minute (CWPM), or their Oral Reading Fluency Assessment (ORFA).

Unconstrained skills

Oral language proficiency underpins communication and learning. This is especially evident in the early stages of learning to read. Research has demonstrated that children with larger oral vocabularies displayed greater reading and mathematics achievement, increased behavioural self-regulation and fewer externalising and internalising problems at school-entry. There is also a strong reciprocal relationship between oral language development and reading development including the obvious links between oral language development and the next Big 6 skill, vocabulary.

A student’s vocabulary is all the words they understand. A rich vocabulary is essential in developing reading comprehension because students must understand the meaning of almost all words in a text to accurately interpret its meaning. A deep and broad vocabulary can drive the development of reading comprehension. A more abundant vocabulary leads to a more comprehensive understanding of ideas, which may in turn enrich reading experiences. Vocabulary instruction must include morphology – the study of morphemes, the smallest meaningful units of a language. These morphemes can be joined together to create specific meanings. Knowing more about morphemes and having a bigger vocabulary supports the development of reading comprehension.

Reading comprehension involves an active process of making, constructing, or deciphering the meaning of a text. It involves elements of decoding, working out meaning, evaluating and imagining. The process draws upon the learner’s existing background knowledge and understanding, text–processing strategies and capabilities, and relies on the integration of all of the previously mentioned skills from the Big 6. At its most sophisticated, reading comprehension involves making inferences, critical analysis and applying knowledge of text types and social and cultural resources to evaluate or interpret a text.

Stay tuned: In the next article, Greta and Kellie will delve into phonemic awareness, phonics and fluency in greater detail.

Related reading:

Kellie Pickier and Greta Rollo have also published an online visual resource, Unpacking the science of reading , that explains the Big 6 pillars of learning to read. You can read it here .

References:

Rohl, M. (2000). Programs and strategies used by teachers to support primary students with difficulties in learning literacy. Australian Journal of Learning Disabilities, 5(2), 17–22. https://doi.org/10.1080/19404150009546622

Rollo, G., & Picker, K. (2024). Unpacking the science of reading research. Australian Council for Educational Research . https://doi.org/10.37517/978-1-74286-742-7

Stark, H., Snow, P. C., Eadie, P. A., & Goldfeld, S. R. (2015). Language and reading instruction in early years’ classrooms: The knowledge and self-rated ability of Australian teachers. Annals of Dyslexia, 66, 28–54.

Turner, R., Adams, R., Schwantner, U., Cloney, D., Scoular, C., Anderson, P., Daraganov, A., Jackson, J., Knowles, S., O’Connor, G., Munro-Smith, P., Zoumboulis, S., & Rogers, P. (2018). Development of reporting scales for reading and mathematics: A report describing the process for building the UIS Reporting Scales. Australian Council for Educational Research. https://research.acer.edu.au/monitoring_learning/33/

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Chemistry Education Research and Practice

Development of problem-solving skills supported by metacognitive scaffolding: insights from students’ written work.

Despite problem solving being a core skill in chemistry, students often struggle to solve chemistry problems. This difficulty may arise from students trying to solve problems through memorising algorithms. Goldilocks’ Help serves as a problem-solving scaffold that supports students through structured problem solving and its elements, such as planning and evaluation. In this study, we investigated how first-year chemistry students solved problems, when taught with Goldilocks’ Help, and whether their problem-solving success and approaches changed over the course of one semester. The data comprised of student written problem-solving work, and was analysed using frequency analysis and grouped based on the problem-solving success and the extent of the demonstrated problem-solving elements. Throughout the course of semester, students exhibited increasingly consistent demonstration of structured problem solving. Nonetheless, they encountered difficulties in fully demonstrating such aspects of problem solving as understanding and evaluating concepts, which demand critical thinking and a firm grasp of chemistry principles. Overall, the study indicated progress in successful and structured problem solving, with a growing proportion of students demonstrating an exploratory approach as time progressed. These findings imply the need for incorporation of metacognitive problem-solving scaffolding, exposure to expert solutions, reflective assignments, and rubric-based feedback into wide teaching practice. Further research is required to extend the exploration of the effectiveness of metacognitive scaffolding, in particular via think-aloud interviews, which should help identify productive and unproductive uses of the problem-solving elements.

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K. Vo, M. Sarkar, P. J. White and E. Yuriev, Chem. Educ. Res. Pract. , 2024, Accepted Manuscript , DOI: 10.1039/D3RP00284E

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• Open access
• Published: 04 July 2024

The application of blended teaching in medical practical course of clinical skills training

• Zhicheng He 1 ,
• Qiang Wang 1 ,
• Qingming Wu 1 &
• Lili Lu 1  

BMC Medical Education volume  24 , Article number:  724 ( 2024 ) Cite this article

148 Accesses

Metrics details

Blended teaching is an effective approach that combines online and offline teaching methods, leading to improved outcomes in medical education compared to traditional offline teaching. In this study, we examined the impact of blended teaching in clinical skills training, a medical practice course.

This study involved forty-eight undergraduate students studying clinical medicine in the fifth semester at Wuhan University of Science and Technology. The students were divided into two groups: the control group, which received traditional offline teaching, and the experimental group, which received hybrid teaching. Following the completion of the 4-month course, both groups underwent the Objective Structured Clinical Examination (OSCE) to evaluate their proficiency in clinical skills. Furthermore, the experimental group was given a separate questionnaire to gauge their feedback on the Blended Teaching approach.

Based on the OSCE scores, the experimental group outperformed the control group significantly ( P <0.05). The questionnaire results indicated that a majority of students (54.2%, 3.71 ± 1.06) believed that blended teaching is superior to traditional offline teaching, and a significant number of students (58.3%, 3.79 ± 1.15) expressed their willingness to adopt blended teaching in other courses. Furthermore, students in the experimental group displayed varying levels of interest in different teaching contents, with emergency medicine (79.2%), internal medicine (70.8%), and surgery (66.7%) being the most popular among them.

Conclusions

This research demonstrates for the first time that blended teaching can achieve a good pedagogical effectiveness in the medical practice course, clinical skills training and practice. Moreover, in different teaching contents, the teaching effects are different. In the content of Emergency Medicine and Surgery, which is more attractive to students, the application of blended teaching could result in a better pedagogical outcome than other contents.

Peer Review reports

Introduction

The education system for clinical medicine students in China primarily follows the “5 + 3” model, with some variations such as the 8-year program. In the “5 + 3” model, students undergo a five-year undergraduate program to get a bachelor degree and then followed by three-year standardized residency training [ 1 ]. Undergraduate education can be divided into three parts: theoretical learning, apprenticeships, and internships. At Wuhan University of Science and Technology (WUST), the undergraduate clinical medical education program follows a unique 2.5 + 2.5 model. In this model, the first 2.5 years are dedicated to studying general courses and basic medical courses at the university. The subsequent 2.5 years are then devoted to completing clinical courses and clinical internships in affiliated hospitals.

In the career of clinical medical students, the acquisition of clinical skills is crucial for demonstrating competence in clinical practice. Before the internship stage, medical students are typically expected to master fundamental clinical skills such as physical examination, cardiopulmonary resuscitation (CPR), major puncture operations, and basic surgical operations. These skills will play an essential role in their future careers. For instance, proficient physical examinations can expedite the treatment process for patients with acute and serious illnesses, as well as guide doctors in conducting other necessary examinations promptly. This not only reduces the financial burden on patients but also improves the allocation of medical resources [ 2 ]. Additionally, regardless of the department they work in, it is imperative for medical students to master CPR and be able to apply it in emergencies [ 3 ]. According to this requirement, WUST has introduced the course “Clinical Skills Training and Practice” in the fifth semester to cultivate students’ basic clinical skills before they enter the affiliated hospitals.

With the rapid development of information technology, the traditional offline medical teaching mode alone is no longer sufficient to meet the evolving needs of medical education in this era. Initially, online medical education was primarily limited to recording and broadcasting courses, often spread through tapes and CDs. This traditional model, however, only catered to basic teaching needs with limited interactivity and feedback. The emergence of internet technologies has paved the way for innovative teaching methods such as online instruction and virtual simulations to gain popularity. These advancements have made teachers more engaging to students and facilitated increased feedback [ 4 , 5 ]. Moreover, the research on the utilization of ChatGPT in medical education reminds us that the evolution of medical education will progress alongside advancements in science and technology [ 6 ]. Changes in educational methods are influenced not only by technological advancements but also by shifts in students’ intrinsic needs. In today’s information-rich environment, traditional teaching methods centered around knowledge transfer alone fall short in meeting students’ requirements. The progress in technology has expanded the possibilities for educational approaches, including the flipped classroom model, project-based learning, and differentiated instruction. These innovations enable educators to focus on enhancing students’ learning experiences, increasing their interest and engagement, catering to their diverse and personalized learning preferences, and ensuring fair and inclusive access to education for a broader audience.

Traditional clinical skills training typically involves a structured presentation by the teacher, followed by the student’s practice under supervision [ 7 ]. However, the COVID-19 pandemic has boosted the development of online courses, such as ‘Clinical Skills Training and Practice’ in WUST. Multiple studies demonstrate that online medical education during this period has yielded unexpected advancements and potential [ 8 , 9 , 10 ]. In the past three years, WUST’s online “Clinical Skills Training and Practice” course has demonstrated promising results in improving pedagogical effectiveness. This prompts us to consider whether blending online education with traditional offline teaching (TOT) could be a better option. Blended teaching (BT), which combines online and offline methods, has been used in medical education since 1990 [ 11 , 12 ]. A meta-analysis comparing BT and TOT in medical education indicates that BT has superior pedagogical effectiveness [ 13 ].

During the transition from being a medical student to becoming a doctor, students need to take medical practice courses to enhance their understanding and application of theoretical knowledge [ 14 ]. Among these courses, clinical skills training is particularly challenging and crucial due to its practical nature. While there has been limited research on the use of BT in clinical skills training.

This study, conducted at the Clinical Skills Training Center of Wuhan University of Science and Technology, aims to investigate the effectiveness of BT in clinical skills training. The participants of this study were undergraduate students majoring in clinical medicine in their fifth semester. The researchers implemented either BT or TOT in their ‘Clinical Skills Training and Practice’ course. The teaching effectiveness was evaluated using Objective Structured Clinical Examination (OSCE) scores and questionnaires. It is hypothesized that students who receive BT will achieve higher OSCE scores and report a more positive teaching experience and effectiveness in the questionnaire.

The study utilized a prospective randomized controlled design and received approval from the Ethics Committee of Wuhan University of Science and Technology (Dossier number 2022151). Sample size was computed with the aim of 0.85 power value, predicated on an effect size of 0.9 and a margin of error set at 0.05. A minimum of 19 participants per group was calculated using PASS 15, resulting in the recruitment of a total of 38 undergraduate students. To address the potential issue of sample dropout during project implementation, the sample size was increased to 48 students. 48 students were recruited based on predefined inclusion criteria from the total 248 third-year undergraduate students from the Department of Clinical Medicine at WUST. The inclusion criteria included: (1) proficient communication and comprehension skills, (2) consistent attendance without absenteeism or truancy, and (3) a positive attitude toward learning. Exclusion criteria comprised: (1) refusal to participate, (2) class absence, (3) failure to complete the final test, and (4) incomplete questionnaire responses. The study emphasized voluntary participation, allowing participants to withdraw at any time without providing a reason. We employed a random digital method to create a set of identification numbers, which were subsequently placed in a box and shuffled. Participants then selected codes from the box to determine their assignment to either the experimental Group A ( n  = 24) or the control Group B ( n  = 24). The random allocation sequence was generated using IBM SPSS Statistics 27. The study was conducted from September 2022 to December 2022. Prior to the commencement of the study, none of the participants had undergone any clinical skills training.

Study design

According to the WUST clinical medicine cultivation program, the course “Clinical Skills Training and Practice” is conducted in the fifth semester. Both groups of students followed the same syllabus and were taught and assessed by the same teaching team. The objectives of this course include gaining theoretical knowledge of various clinical operations and achieving proficiency in performing CPR, the four major puncture operations (thoracentesis, lumbar, myelopuncture, and peritoneal puncture), physical examination, and basic surgical operations (Disinfect & Draping, Donning & Taking off Surgical Gowns, and Incision & Suturing). All faculty members involved in this course are part of the Department of Clinical Medicine, holding both medical practitioner and teaching certificates, and possessing extensive teaching skills and clinical experience. Offline lessons took place at WUST’s Clinical Skills Training Center. The designated textbook for this course is ‘Clinical Skills Training and Practice’ [ 15 ]. The course consists of 144 periods and lasts approximately 4 months.

Interventions

Group A utilized the online course called “Clinical Skills Training and Practice” available on the University Open Online Courses (UOOC) [ 16 ]. The course is divided into five clinical modules: internal medicine, surgery, gynecology, pediatrics, and emergency medicine. Each module consists of theoretical lecture videos, standardized operation demonstration videos, PPT resources, as well as supporting exercises and tests. The course platform also provides a discussion and exchange board for teachers and students to interact and discuss topics online. The online teaching component constitutes 25% of the total class hours (Fig.  1 ).

Before each offline class, the teacher publishes the teaching content on the platform. Students access the platform using electronic devices and independently learn the relevant material. Through platform data, teachers can monitor and adjust the offline teaching content based on students’ progress. For skills that students have mastered well, teachers will primarily guide students to practice independently during offline teaching. For skills with weak mastery data, teachers will initially emphasize the key points of skill operation and provide demonstrations during offline teaching. The approach of targeting weak areas will be more focused, avoiding redundant explanations of basic content, and offering students more chances for self-practice. Instead of traditional lectures and demonstrations, teachers guide students in practical exercises during offline classes and enhance learning through formative evaluations such as group evaluations and teacher feedback (Fig.  2 ). After the offline classes, students return to the online platform to complete tests and assignments for each chapter, reinforcing their understanding of the acquired skills. If students encounter any difficulties, they can communicate with the teacher through the online course platform’s discussion area, ensuring timely teacher-student communication. Additionally, the course team teachers utilize the discussion area of the online platform to provide high-level clinical thinking training content, such as case analysis, to cater to the individualized learning needs of students at higher levels. The specific teaching process is depicted in Fig.  1 .

Group B students adopt the TOT model, which includes theoretical teaching and demonstration conducted by the teacher (25% of class time) followed by practical exercises by the students (75% of class time) (Fig.  1 ). Additionally, student mutual evaluation and teacher comments are used to conduct formative evaluation of students’ learning effects (Fig.  2 ).

Data collection

After the course, both groups of students underwent offline OSCE assessments at the WUST Clinical Skills Training Center. These assessments were conducted by the same group of examiners. The OSCE assessment consisted of 6 examination stations, namely: physical examination, cardiopulmonary resuscitation, four major puncture operations, donning & taking off surgical gowns, disinfection & draping, and incision & suturing (Fig.  1 ).

We designed a questionnaire for students in Group A who adopted BT. We used Alpha to calculate intra-group consistency and reliability. The alpha value of the BT questionnaire in Group A is 0.941, indicating that the questionnaire meets the required reliability. After the OSCE, the teacher distributed an anonymous questionnaire to the students in Group A (Fig.  1 ). The questionnaire included two basic pieces of information about the subjects’ age and gender, 11 scale questions, 1 multiple-choice question, and 1 open-ended question. The question design is based on a Likert scale (the scale ranges from 1 to 5, indicating the degree from strongly disagree to strongly agree). We considered a score ≥ 4 as an agreement.

The primary outcome of this study was to evaluate the scores of OSCE at the end of the course for both groups of students. Additionally, the results of the questionnaire were considered as a secondary outcome.

An overview of the course design: From 248 fifth-semester clinical medicine students, 48 students were randomly selected and divided into Group A and Group B. Group A adopted BT, and Group B adopted TOT. After 4 months of teaching, both of the two groups took OSCE but only Group A took the questionnaire

BT: Blended Teaching; TOT: Traditional Offline Teaching

The formative assessment of Group A and Group B

Statistical analyses

We used the Mac 2019 version of Microsoft Excel to collect all the OSCE score data and the BT questionnaire data. IBM SPSS Statistics 27 was used to test the normality and homogeneity of variance between groups A and B. Continuous variables with normal distribution were presented as mean ± standard deviation (SD); non-normal variables were reported as median (interquartile range). Suppose the data matched the normal distribution the independent samples t-test was used, if not the Mann-Whitney U-test was used. Frequency analysis was conducted to analyze the rate of students’ agreement with each question in the BT questionnaire as reflected in the count data and expressed as a percentage (%). P  < 0.050 determined that it was statistically significant.

Participants’ demographic data

The demographic data of Groups A and B are presented in Table  1 . This study comprised a total of 48 students, with 24 students in Group A and 24 students in Group B. The average age of the students in Group A was 20.08 ± 0.65, while in Group B it was 21.33 ± 0.92. The male/female ratio in Group A was 9/15 and in Group B was 12/12.

Results of OSCE

As demonstrated in Table  2 , the normality of the data was assessed using the Shapiro-Wilk Test. It is important to highlight that the P  value for group A in the total score item is less than 0.05, indicating a lack of normal distribution characteristics. Notably, in studies with small sample sizes ( n  < 50), meeting the criteria for data normality might be challenging. However, if the absolute value of Skewness is below 10 and the absolute value of Kurtosis is below 3, it is acceptable to proceed with corresponding statistical analyses as if the data is normality. Subsequently, based on the outcomes of the data analysis, appropriate statistical methods are applied to different types of data. The OSCE scores are shown in Table  3 . Significant differences were observed in various skills between Group A and Group B. These skills include cardiopulmonary resuscitation (92.50(4.00) vs. 86.00(3.50), P  < 0.050), physical examination (90.04 ± 3.09 vs. 63.83 ± 7.03, P  < 0.050), four major puncture operations (77.21 ± 8.99 vs. 71.17 ± 6.42, P  < 0.050), disinfection & draping (82.79 ± 4.03 vs. 61.42 ± 12.48, P  < 0.050), donning & taking off surgical gowns (84.00(5.75) vs. 78.00(9.00), P  < 0.050), incision & suturing (68.42 ± 5.26 vs. 62.79 ± 8.30, P  < 0.050) and total score (82.13 ± 3.36 vs. 70.00 ± 5.77, P  < 0.050). These results indicate that Group A achieved higher average scores than Group B in all evaluated items at the significance level of 0.05.

Perspectives survey about online and offline blended teaching mode

Table  4 presents the experiences and opinions of students in Group A regarding BT. The questionnaire was designed with questions categorized into four dimensions: course experience, learning effect, teaching evaluation, and overall evaluation. By analyzing the responses to questions 1–4, we can assess the impact of students’ course experience. Among the students who adopted BT, a higher number of students reported an improved course experience. Specifically, the model aided in understanding the theoretical knowledge of clinical skill operations (70.8%, 4.04 ± 1.10) and facilitated faster independent learning of these operations (70.8%, 4.04 ± 1.17). Additionally, it promoted the speed of mastering skills (66.6%, 3.92 ± 1.22) without significantly increasing the learning burden, as observed under good teaching effects (70.8%, 2.79 ± 1.15). Questions 5–7 aimed to assess student learning effectiveness. The majority of students expressed that BT helped prepare for OSCE exams (66.7%, 3.83 ± 1.18), promoting self-directed learning that is not bound by time and space (62.5%, 3.83 ± 1.14), and increasing their interest in the learning process (58.3%, 3.79 ± 1.08). Students’ evaluation of teaching under BT can be assessed using questions 8–9. The majority of students expressed that both online instruction (62.5%, 3.75 ± 1.13) and offline instruction (70.9%, 3.96 ± 1.14) in BT were effective in achieving the desired outcomes and objectives. The students’ overall assessment of BT is reflected in questions 10–11. More than half of the students (54.2%, 3.71 ± 1.06) felt that BT was better than TOT, and a higher proportion of the students (58.3%, 3.79 ± 1.15) expressed their willingness to implement BT into other medical skills training. Furthermore, question 12 revealed the students’ interest in each part of the teaching content, indicating that emergency medicine (79.2%), internal medicine (70.8%), and surgery (66.7%) were the most popular choices.

Clinical skills training in clinical practice courses is characterized by a high degree of practicality and the requirement for more practice time. The TOT model is commonly used, where instructors teach the theory and demonstrate the skills, followed by students practicing on their own. However, this model often limits the duration of students’ practical exercises, which is not beneficial to the training of clinical skills. The present study aims to assess the potential of BT in practice course by integrating online courses with offline practice, developing a BT course that meets pedagogical requirements, and evaluating its teaching effectiveness in different clinical skills. The research findings indicate that students in Group A, who adopted BT, performed better overall in OSCE compared to Group B, who followed the TOT model. Moreover, the results of the questionnaire revealed that Group A students had a positive learning experience and perceived the course to be more effective in terms of pedagogy.

The OSCE is widely recognized as an effective way to judge students’ mastery of clinical skills for formative and summative purposes [ 17 ]. In terms of OSCE scores, students in Group A outperformed those in Group B in both the overall score and each individual item. The differences between all items were statistically significant. When comparing the average scores of each item between the two groups, it can be observed that Group A showed varying levels of improvement in different assessment items. The performance difference between groups A and B was more obvious in the two items of physical examination and Disinfection & Draping compared to the other items. This suggests that although BT demonstrated better teaching effectiveness overall, its strengths vary across different types of items. These two items stand out due to their extensive content but relatively simple operation. With the use of the online platform in BT, students have the opportunity to repeatedly learn and become more proficient in these operations. However, when faced with tasks that require more offline practice, such as CPR and the four major puncture operations, the performance improvement is not as significant as observed in the two aforementioned items. That means online teaching cannot fully substitute offline teaching, especially when it comes to highly practical teaching content. However, online course platforms can be utilized to enhance teaching content, broaden teaching activities, and compensate for the limitations of traditional offline teaching. The results of the questionnaire in Group A revealed that students demonstrated a great interest interest in first aid, internal medicine, and surgery skills. Additionally, Group A achieved higher scores in the OSCE at the CPR site (Emergency Medicine) and the Basic Surgical Skills-related site (Surgery). These findings indicate that when students are presented with more engaging study materials, their motivation to learn is enhanced, leading to improved learning outcomes driven by higher levels of initiative [ 18 ]. Therefore, in the next stage of course construction, it is crucial to explore the development of more course content that can effectively enhance students’ interest in learning.

In contrast to this study, much of the current research on the use of BT in clinical skills education tends to concentrate on specific skills or skill types. Amy L Halverson’s research, for example, delves into surgical skills. The findings of Halverson’s study indicate that BT has a beneficial impact on surgical skills training for rural physicians, aligning with the outcomes of our study. Nevertheless, unlike the present research, Halverson’s study relied solely on questionnaires for drawing conclusions and lacked objective evaluation metrics [ 19 ]. More studies are focusing on evaluating the effectiveness of the BT model in CPR training due to the broad audience it caters to, which includes both medical and non-medical professionals. A study conducted on 832 non-medical professional persons in Taiwan revealed that the BT model was superior to TOT [ 20 ]. Additionally, research on the application of the BT model in CPR training for underage students demonstrated a significant increase in students’ willingness to intervene during a cardiac arrest, from 56.9 to 93.1% post-course [ 21 ]. These findings highlight the positive impact of the BT model on students’ self-confidence and overall teaching outcomes. Our study further supports these results, as the group A trained with the BT model performed notably better in the OSCE at the CPR site. This study innovatively applied the BT model to various types of clinical skills training, comparing its effects with the TOT model across different skill items. Moreover, this research not only examined the differences in application effects between the two teaching models on the same skill items but also compared the differences in teaching effectiveness improvement after applying the BT model among different skill items. The findings offer a more comprehensive theoretical foundation for application of the BT teaching model in clinical skills practice courses.

In the design of the course, we offer a wealth of clinical case materials on the online course platform. These materials are available for students who are eager to learn. Our goal is to foster students’ advanced abilities through the use of relevant cases or scenarios, which can enhance their coping skills and their ability to handle emergencies [ 22 ]. Our study has shown that students in Group A demonstrate higher performance in practical projects like CPR, which require hands-on experience, through online situational clinical case training. This training method allows students to go beyond simply acquiring visual information and instead encourages them to analyze, process, and integrate the visual information. As a result, students can achieve a deeper understanding of the knowledge points, progressing from the lower levels of Bloom’s taxonomy (memorization and comprehension) to higher levels such as analysis, application, and judgment. This approach greatly enhances the effectiveness of learning [ 23 ]. According to several studies, virtual simulation has been found to be more effective in promoting the learning of skills compared to teaching theoretical knowledge alone [ 24 ]. Therefore, in future designs of BT, we propose incorporating virtual simulation teaching into the online platform. This addition aims to address the limitations of the online platform in practical training and enhance the overall learning experience [ 25 ].

The blend of online courses with the traditional TOT model can offer teachers a more personalized teaching environment and timely feedback. The online education platform enables real-time observation and regulation of students’ learning progress, allowing for dynamic adjustments in offline teaching content and methods to better achieve pedagogical goals. Furthermore, we designed a chapter test in the online course. According to Kromann, the inclusion of testing in clinical skills training can be effective in improving the effectiveness of learning [ 26 ]. In this study, we observed that teachers can effectively assess students’ understanding of this particular aspect of the theory through chapter tests. This allows them to provide targeted guidance and reinforcement for students’ weaker areas in the offline course. Such feedback evaluation, developed during the teaching process, plays a crucial role in improving teaching effectiveness due to its timeliness and relevance. In future course designs, we plan to incorporate various forms of accompanying tests in both online and offline sessions to further enhance formative evaluation and teaching effectiveness.

For students, blending the online course with the offline course can provide the advantages of being more accessible and flexible in terms of time and location. It has been claimed that students can arrange their learning according to their own schedule and rhythm through the online platform in the BT model [ 13 ]. A similar phenomenon was observed in our study. For instance, before each offline teaching session or OSCE, there was a noticeable increase in students accessing online platforms. This trend indicates that students are using online platforms to align with their learning or revision strategies. In the online course, we have also introduced a discussion board where the instructor posts clinical case information and related questions. This board serves as a platform for students to actively participate in discussions and answer the questions posed by the instructor. The instructor then provides feedback on the student’s answers. This interactive communication method helps to reinforce the students’ clinical knowledge and skills, while also training them to develop their initial clinical thinking skills. Meanwhile, it also can effectively promote student participation in this course. It has been reported that greater student engagement in courses can increase their positive experience of the course and ultimately improve the effectiveness of the instruction [ 27 ]. However, in the BT model, students are required to possess advanced self-management skills and be familiar with online teaching platforms. Therefore, it is essential to integrate suitable learning monitoring tools and provide adequate training as part of the teaching process [ 28 , 29 ].

In the analysis of the questionnaire, we also noticed that students were slightly more satisfied with offline education (70.9%) than with online education (62.5%). This reminds us that offline teaching still holds its irreplaceability compared to online teaching. For example, face-to-face communication in offline teaching fosters a closer emotional connection between teachers and students. It allows for more intuitive guidance in developing students’ skills and provides faster feedback [ 30 ]. Given the practical nature of clinical skills courses, it is reasonable to conclude that online teaching cannot fully replace offline teaching. However, our research indicates that a combination of online and offline instruction can produce a synergistic effect. The online component of the course expands teaching resources and diversifies teaching methods, while also overcoming time and space constraints and promoting independent learning. On the other hand, the offline component allows teachers to provide personalized face-to-face guidance promptly. By combining these two approaches, we can achieve improved pedagogical effectiveness by leveraging their complementary advantages.

Like all educational research articles, this study has some limitations. Firstly, the sample size in this study is relatively small, which may result in a larger margin of error. Therefore, in our future studies, we plan to increase the sample size to reduce the potential bias caused by the small sample. Additionally, the limited number of clinical skills items included in this research may not provide a comprehensive evaluation of the effectiveness of BT in various clinical skills teaching. In future research, we will incorporate more measures to assess the learning outcomes of students’ clinical skills. This will involve collecting scores from graduation operation examinations and licensing examinations to objectively evaluate students’ mastery of clinical skills. Additionally, we will enhance curriculum development by integrating more clinical skills teaching programs into the BT model. This will allow for a more comprehensive evaluation of the BT model’s effectiveness in training various clinical skills programs. The questionnaire used in this study may have limitations in evaluating the teaching effect of BT due to its subjective nature. It is more suitable for assessing students’ subjective perceptions of the BT teaching model. Future research will aim to enhance the questionnaire design to better capture the subjective experiences of both teachers and students.

The development of the times has resulted in significant changes in medical education. As educators, it is important for us to actively explore new teaching modes and methods to enhance students’ learning experiences and outcomes. This will enable us to better cultivate medical students to meet the demands of the modern era. In conclusion, the results of this research indicate that students adopting BT are better in clinical skills training than those adopting TOT. And then, BT was better at teaching content-rich but easy-to-do items (physical examination and disinfection & draping) than practice-demanding items. Finally, students adopting BT will have better pedagogical outcomes in the more interesting items (emergency medicine and surgery). The application of BT in clinical skills training has demonstrated its potential in this study, leading us to believe that applying BT to other medical skills training and courses could yield unexpected benefits. In the future, we plan to develop more courses using blended teaching to cater to the needs of the new generation of clinical medical students.

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank our colleagues in the Department of Clinical Medicine, and the Affiliated Hospital of Wuhan University of Science and Technology, for participating in the construction of the “Clinical Skills Training and Practice” online course, teaching and scoring the enrolled students.

This work was supported by the Higher Educational Teaching Reform Project of the Hubei Province Education Department (2021236); the College Students’ Innovation Project of Hubei Province of China (Hubei Province Education Department, S202110488072); Graduate Education Quality Engineering Project of Wuhan University of Science and Technology (Yjg202327).

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Zhicheng He, Hua Li, Lan Lu, Qiang Wang, Qingming Wu & Lili Lu

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ZC He wrote the draft, prepared figures and interpreted the data; H Li, L Lu, and Q Wang participated in the organization and implementation of this study; LL Lu and QM Wu conceived and designed this study; LL Lu did the critically revising work; approved the final version submitted; got the funding supporting. All authors reviewed the manuscript.

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Correspondence to Qingming Wu or Lili Lu .

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He, Z., Li, H., Lu, L. et al. The application of blended teaching in medical practical course of clinical skills training. BMC Med Educ 24 , 724 (2024). https://doi.org/10.1186/s12909-024-05730-6

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Received : 20 January 2024

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DOI : https://doi.org/10.1186/s12909-024-05730-6

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