The Interplay of Cognitive Load, Learners’ Resources and Self-regulation

  • INTERVENTION STUDY
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  • Published: 10 May 2024
  • Volume 36 , article number  50 , ( 2024 )

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research paper on the learning theory

  • Tina Seufert   ORCID: orcid.org/0000-0002-8685-4116 1 ,
  • Verena Hamm 1 ,
  • Andrea Vogt   ORCID: orcid.org/0000-0003-3523-9911 1 &
  • Valentin Riemer   ORCID: orcid.org/0000-0002-6134-3053 1  

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Self-regulated learning depends on task difficulty and on learners’ resources and cognitive load, as described by an inverted U-shaped relationship in Seufert’s (2018) model: for easy tasks, resources are high and load is low, so there is no need to regulate, whereas for difficult tasks, load is too high and resources are too low to regulate. Only at moderate task difficulty do learners regulate, as resources and load are in equilibrium. The purpose of this study is to validate this model, i.e., the inverted U-shaped relationship between task difficulty and self-regulatory activities, as well as learner resources and cognitive load as mediators. In the within-subject study, 67 participants reported their cognitive and metacognitive strategy use for four exams of varying difficulty. For each exam task difficulty, cognitive load, and available resources (such as prior knowledge, interest, etc.) were assessed. Multilevel analysis revealed an inverted U-shaped relationship between task difficulty and the use of cognitive strategies. For metacognitive strategies, only a linear relationship was found. Increasing cognitive load mediated these relationship patterns. For learner resources we found a competitive mediation, indicating that further mediators could be relevant. In future investigations a broader range of task difficulty should be examined.

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Building Bridges between Self-Regulated Learning and Cognitive Load Theory

In recent years, some attempts have been made to build bridges between two of the most important theoretical frameworks in the field of education, i.e., models of self-regulated learning (SRL) and cognitive load theory (CLT; Sweller et al., 1998 ). While SRL and the respective models deal with self-generated thoughts, feelings, and actions, which are systematically oriented toward attainment of students’ own goals (Zimmermann, 2002 ), CLT has a focus on cognitive processes while learning and how instruction can be designed to optimize learners’ limited cognitive resources. One bridge-building approach is the Effort Monitoring and Regulation Framework (EMR; de Bruin et al., 2020 ), which aims to explain how mental effort influences self-regulatory processes such as monitoring. De Bruin et al. ( 2020 ) emphasize the importance of linking cognitive load and self-regulated learning perspectives to understand how to optimize self-regulation. The link between the two concepts is bidirectional: Self-regulation requires cognitive resources and thus causes cognitive load. But depending on the load demands of a task, learners can adjust their self-regulatory activities, and thus cognitive load can cause or affect self-regulation. This complex interplay is explained in Seufert’s ( 2018 , 2020 ) model of self-regulation as a function of resources and perceived cognitive load. Depending on task difficulty, load and learners’ resources vary and influence the actual use of self-regulatory activities: Easy tasks, such as learning simple lists of familiar words, are experienced as less demanding and may not activate intensive self-regulatory activities. It is a low-stress task for which learners have sufficient resources. Learners may show strong self-regulation in medium difficult tasks, for which they still have resources but the task is not too loading. For even higher task difficulty, resources might be scarce and demands are too high, so learners may decide not to invest in self-regulation because the task itself is already too demanding.

Overall, these examples illustrate that learners’ self-regulation depends on different aspects, either inherent to the task or due to their own characteristics. The interplay of the learners’ resources and the cognitive load experienced through tasks of varying difficulty seems to influence whether and to what extent learners self-regulate. This is the main assumption of Seufert’s ( 2018 ) model. If the model is valid, it could provide a starting point for research into the interplay between self-regulation and cognitive load. It could also provide a framework for teachers and learners to support the specific factors, i.e. balancing task affordances with learner resources and cognitive load, to promote self-regulation. Based on these overarching objectives, this paper aims to validate the model and empirically test its basic assumptions. Therefore, this research aims to investigate the influence of perceived task difficulty on the use of self-regulatory activities. Due to the often observed inter- and intra-individual differences in learning behavior (Jonassen & Grabowski, 2012 ), the role of cognitive load caused by the learning task and the importance of learners’ available resources in this context will also be investigated.

Self-Regulation as a Function of Resources and Perceived Cognitive Load

The main purpose of Seufert’s ( 2018 , 2020 ) model of self-regulation as a function of resources and perceived cognitive load is to provide a research framework for studies of the interplay between SRL and CLT. However, it makes strong assumptions about the relationship between its main parameters, which need to be supported by empirical evidence.

Before presenting the main parameters and assumptions of the model, it is necessary to understand what Seufert ( 2018 , 2020 ) actually understands by self-regulation in her model. Based on different models of self-regulation (Schmitz & Wiese, 2006 ; Zimmerman, 1990 ), which emphasize the dynamic and cyclical process of self-regulation, learners perform self-regulatory activities before, during and after learning. Learning strategies are crucial in this whole process, and they can be either cognitive, metacognitive or resource-based. While cognitive strategies refer to conscious plans for processing the information of the given task, metacognitive strategies aim to optimize the learning process itself. Resource-based strategies are used to support the learning process by utilizing or managing external sources, like help or time (Boekaerts, 2011 ). In a perfectly self-regulated learning process, learners use metacognitive strategies to set their goals, plan their learning activities, and choose cognitive strategies that match the affordances of the task before learning. During learning, these strategies are applied and learners metacognitively monitor whether strategies and goals are still aligned. If not, they regulate, i.e., they adjust their strategies or goals and use different strategies, regulate their effort, or give up. Even after learning, self-regulation is necessary to reflect on all the processes before and after learning that relate to processing the task and managing the learning process. Based on reflection, learners can draw conclusions for future learning situations (Zimmerman, 1990 ). In summary, based on the description in Seufert ( 2018 ) self-regulatory activities include cognitive and metacognitive strategies before, during, and after learning. As in this model resource-based strategies are not focused specifically we also restrain from analyzing learners’ resource-based strategy activities. Seufert’s model of self-regulation as a function of resources and perceived cognitive load (2018, 2020; see Fig.  1 ) describes various parameters that influence the intensity of these self-regulatory activities.

figure 1

Self-regulation as a function of resources and perceived Cognitive Load (Seufert, 2018 )

The crucial parameter of Seufert’s model (2018, 2020) is the difficulty of the task , which due to the author includes the original learning task and, in addition, the self-regulation possibilities during the handling of this task. A typical example would be a student preparing for an exam. He or she has to memorize and understand the content itself, but at the same time has to organize the learning process, i.e. make plans, monitor progress, and regulate in case of difficulties. The difficulty of the content may be strongly related to the complexity of managing the learning process Moreover, Seufert ( 2020 ) emphasized that task difficulty may arise not only from objective affordances but also from learners’ decisions to engage more or less with the task as needed, for example, because they like it or dislike it, are particularly interested in it or not.

Closely related to task difficulty, two relevant and counteracting forces mediate whether learners actually engage in self-regulatory activities.

First, cognitive load increases as task difficulty increases. Thereby, all three types of load, intrinsic, extraneous and germane load are linked to task difficulty. While intrinsic affordances in terms of task complexity are inherent to the task and can hence be seen as part of task difficulty, cognitive load may also arise from extraneous affordances of the task which are not related to the task objectives like search processes, or due to germane resources invested (Sweller et al., 1998 ). If the overall imposed load of the task is too high, self-regulatory activities may cease.

Second, as task difficulty increases, learners’ resources for successfully completing the task decrease. Or one could argue the other way round that the task is perceived as difficult because of a lack of resources. These resources include cognitive abilities, most importantly prior knowledge (Dochy, 1994 ) or working memory capacity (Cowan, 2014 ) as well as skills or capacities for successful self-regulation, such as motivation or metacognitive skills (Zimmerman, 2008 ). With insufficient resources, learners are no longer able to self-regulate.

Taking all these parameters together, Seufert’s ( 2018 , 2020 ) model shows an inverted U-shaped curve for self-regulatory activities as a function of task difficulty, with the two opposing mediators load and resources. On one end of the spectrum, learners may not need to regulate when the task is very easy because the available resources are sufficient, resulting in a perceived low load. Conversely, at the other end, they may struggle to regulate when tasks are too difficult, as resources become insufficient and load is perceived to be very high (e.g., Moos, 2013 ). Optimal self-regulation occurs with tasks of moderate difficulty, where resources and load are balanced, allowing learners to use their resources effectively. The imposed load serves as a catalyst for the initiation of self-regulation, while still being manageable enough to not hinder the process.

In the following, the main assumptions of the model are explained and justified: (1) task difficulty influences self-regulatory activities, (2) cognitive load increases with task difficulty and mediates the effect on self-regulatory activities, (3) learner resources decrease with task difficulty and mediate the effect on self-regulatory activities. Finally, (4) the relationship between the two possible mediators, resources and load, is discussed.

Task Difficulty Influences Self-Regulatory Activities

With regard to Seufert’s ( 2018 , 2020 ) model, it is important to emphasize that the task not only includes the actual learning task at hand, but also the regulation of the learning process while dealing with the task. With regard to perceived task difficulty, empirical studies show that learning tasks that are moderately challenging and thus of medium difficulty promote the use of self-regulatory strategies (Middleton & Midgley, 2002 ; Turner & Meyer, 2004 ). In contrast, when the challenge is too high or too low, learning tends to be less self-regulated (Turner & Meyer, 2004 ). Seufert’s ( 2018 , 2020 ) model makes the same assumption, but it still needs to be empirically tested.

Cognitive Load Increases with Task-Difficulty and Mediates the Effect on Self-Regulatory Activities

The assumption that cognitive load increases with task difficulty and mediates the effect on self-regulatory activities involves three distinct aspects. First, that the task itself and second, that the regulation of the learning process imposes cognitive load and that the load increases with the difficulty or complexity of both. The third aspect is that the load aspect of task difficulty at both levels mediates the intensity of self-regulatory activities.

That cognitive load increases with the difficulty of the task itself is an inherent assumption of CLT (Sweller et al., 1998 ) and therefore perceived task difficulty is often used as an indicator of mental effort and cognitive load (Paas et al., 2005 ). The number of interrelated elements indicates the intrinsic load of the task, and as the complexity of the elements increases, so does the interactivity of the elements. In addition, tasks can also be more difficult if learners have to deal with extraneous processes such as searching or navigating, which is indicated by extraneous load. And learners can invest mental effort which is germane to the task but nevertheless requiring cognitive capacity.

Studies show that regulating the learning process is often experienced as demanding and stressful because it requires cognitive capacities in addition to the actual learning task (Efklides, 2011 ; Lajoie, 1993 ; Schwonke, 2015 ). Therefore, in all phases of the cyclical model of self-regulated learning, learners are confronted with cognitive and metacognitive demands that increase cognitive load. While the intrinsic cognitive load caused by the regulatory task is mostly dependent on the complexity of the actual learning task, extraneous cognitive load is induced when the demands are not sufficiently integrated into the learning task (Seufert, 2018 ). This could be the case, for example, when external learning goals are not made explicit, thus hindering planning and goal setting. Self-regulatory processes can also be productive for learning and can therefore be considered germane. However, this is only possible if sufficient resources are available to facilitate comprehension, schema construction and automation, and learning success (De Bruin & van Merriënboer, 2017 ; Sweller & Paas, 2017 ). In particular, metacognitive strategies require many capacities (De Bruin & van Merriënboer, 2017 ). As the cognitive load theory only focuses on cognitive factors, an extension of the germane cognitive load to include the concept of metacognitive load has already been proposed (Valcke, 2002 ). It is assumed that the germane cognitive load results from the construction of knowledge schemata, whereas the metacognitive load results from the monitoring of the learning process and the control of schema construction and storage (Schwonke, 2015 ; Valcke, 2002 ). In summary, the total demands imposed by the learning task and self-regulatory processes may exceed the capacity of working memory (Schwonke, 2015 ).

The question of whether task load has the potential to mediate learning behavior, i.e., self-regulatory activities, can be answered with the EMR framework (de Bruin et al., 2020 ), which is based on Koriat’s ( 1997 ) cue utilization framework. Learners use various cues, such as perceived task difficulty or effort, to evaluate their learning process and to adjust their learning behavior. Learners primarily respond to perceived difficulties or discrepancies in the learning process with increased regulation of learning behavior. For example, reading speed is reduced for complex texts (Baker & Brown, 1984 ) and more learning time is invested in more difficult tasks (Van Loon et al., 2017 ). In addition, learners use deep strategies especially when faced with difficulties or inconsistencies during the learning process (D’Mello & Graesser, 2012 ). Difficulties in creating graphics for textual content or explaining what they have learned in their own words can also indicate a lack of understanding, leading to increased strategy use (De Bruin & van Merriënboer, 2017 ; Schleinschok et al., 2017 ).

According to the Cue Utilization Framework (Koriat, 1997 ), task difficulty is an intrinsic cue that can inform the learning process either directly or indirectly through mnemonic cues. Mnemonic cues represent "phenomenal experiences" (Koriat, 1997 , p. 351) during information processing, such as perceived cognitive load. Accordingly, learners may use task difficulty to assess the perceived cognitive load imposed by the task and adjust their learning behavior, including self-regulated learning (Van Loon et al., 2017 ). Therefore, this study examines cognitive load as a mediating variable for the relationship between difficulty and self-regulatory activities.

Although, as mentioned above, learners respond to difficulty with increased strategy use, there is evidence that highly complex tasks leave less working memory capacity for self-regulatory processes, and cognitive overload is possible (Kanfer & Ackerman, 1989 ; Moos & Azevedo, 2008 ). Van Gog et al. ( 2011 ) showed that when working on complex tasks, monitoring the learning process increases cognitive load and leads to impaired learning performance, whereas this is not observed for simpler tasks. Thus, when task load is high, the additional demands of self-regulatory processes may exceed the capacity of working memory, which may explain why less self-regulatory learning occurs (Schwonke, 2015 ; Van Gog et al., 2011 ).

Learners’ Resources Decrease with Task-Difficulty and Mediates the Effect on Self-Regulatory Activities

With regard to the third assumption of Seufert’s ( 2018 , 2020 ) model, which needs to be empirically supported, it is first necessary to discuss which potential resources learners can have for self-regulation and how they are related to task difficulty.

Regarding learner characteristics relevant for self-regulation, studies show that the ability to engage in self-regulated learning increases with age, from childhood to adulthood (Boekaerts, 1999 ; Dolmans & Schmidt, 1994 ; Paris & Paris, 2001 ; Whitebread et al., 2007 ). Other personal factors that are more specific to learning are outlined in the Individual Prerequisites for Successful Learning model (INVO model, Hasselhorn & Gold, 2013 ) or in comparable models like the good information processor model (Pressley et al., 1989 ). They distinguish between cognitive and motivational-volitional prerequisites. Cognitive prerequisites include prior knowledge, strategy use and metacognitive knowledge as well as working memory and attentional focus (Hasselhorn & Gold, 2013 ). As motivational prerequisites particularly intrinsic motivation is described as crucial as well as goal orientation, interest, self-efficacy and learners’ self-concept (Hasselhorn & Gold, 2013 ). These resources are briefly presented in the following section with a strong focus on prior knowledge as one of the most critical factors in learning.

Prior knowledge , as one of the key parameters for learning success, is also important with respect to self-regulated learning (Schwonke, 2015 ). Prior knowledge can facilitate selective attention, lead to faster activation of concepts in working memory, and increase interest and motivation (Hasselhorn & Gold, 2013 ). Prior knowledge also provides free working memory capacity that can be invested in self-regulatory processes (DeStefano & LeFevre, 2007 ; Schwonke, 2015 ). As was shown for hypertext tasks, learners with higher levels of prior knowledge plan and monitor their learning process more than learners without prior knowledge (Moos & Azevedo, 2008 ). Taub et al. ( 2014 ) also highlight the greater use of metacognitive learning strategies by individuals with higher levels of prior knowledge, while no differences are observed in cognitive strategy use as a function of expertise. For individuals without prior knowledge, the use of metacognitive strategies in particular exceeds the limited capacity of working memory, so that free resources are invested only in knowledge acquisition to avoid overload (Kanfer & Ackerman, 1989 ; Moos & Azevedo, 2008 ; Taub et al., 2014 ). As prior knowledge increases, and thus in terms of Seufert’s ( 2018 , 2020 ; Fig.  1 ) model on the left, sufficient capacities are available both for processing the learning task and for planning, monitoring, and regulating the learning process (Van Gog et al., 2005 ). Therefore, cognitive load theory can also be used to explain why self-regulated learning is more effective for individuals with higher levels of prior knowledge (Azevedo et al., 2008 ; Van Gog et al., 2005 ). However, for individuals with very high prior knowledge, it can be assumed that learners no longer need additional self-regulatory processes for successful goal attainment. This argument may also apply to all other resource variables.

In addition to prior knowledge, the INVO model (Hasselhorn & Gold, 2013 ) states that strategy use and metacognitive knowledge are essential for self-regulatory activities, as many studies have shown (Bannert et al., 2015 ; Boekaerts, 1999 ; Butler & Winne, 1995 ). As learners gain experience in using strategies, they will be able to tackle more difficult tasks and apply learning strategies successfully (Dresel et al., 2015 ; Stebner et al., 2022 ).

Working memory capacity and attentional focus are also factors that are strongly related to task difficulty as they improve task performance (Ilkowska & Engle, 2010 ). Dealing with the task and being self-regulatory while doing so at the same time requires working memory capacity and the ability to suppress irrelevant aspects. With sufficient capacity and focus learners are able to handle both task levels.

Motivational components are also important for self-regulated learning. The will to persistently engage in a learning task is associated with greater strategy use (Wolters, 2003 ). In particular, intrinsically motivated individuals learn in a more self-regulated manner in contrast to learners that are extrinsically motivated. For intrinsically motivated learners learning is rewarding in itself, because it is interesting, while extrinsically motivated learners seek reward from others or try to avoid punishment (Deci & Ryan, 1985 ; Standage et al., 2005 ).

Furthermore, motivational goal orientation is central, with different orientations being distinguished (Pintrich, 2000 ). Individuals with a learning goal orientation pursue the goal of improving their skills and knowledge (Butler & Winne, 1995 ; Spinath et al., 2002 ; Wolters et al., 1996 ). Whereas learners with an approach-performance goal orientation strive to prove their abilities, learners with an avoidance-performance goal orientation try to conceal deficient abilities (Ames, 1992 ; Elliot & Harackiewicz, 1996 ). In the final goal orientation, work avoidance, learners try to avoid a lot of work, so they prefer tasks that require less effort (Meece & Holt, 1993 ; Spinath et al., 2002 ). Empirical studies show that individuals with a learning goal orientation are more self-regulated learners and therefore have better academic performance (Abar & Loken, 2010 ; Boekaerts et al., 2006 ; Kolić-Vehovec et al., 2008 ). While an approach-performance goal orientation may also be beneficial for self-regulated learning, an avoidance-performance goal orientation, as well as a tendency to avoid work, is associated with lower use of learning strategies (Abar & Loken, 2010 ; Elliot & Harackiewicz, 1996 ).

High interest is also associated with more frequent use of self-regulatory strategies (Horvath et al., 2006 ; Schiefele, 1991 ). In this context, interest can be considered a facet of motivation and describes the preference to engage with certain topics (Hidi, 2000 ).

Another factor to be considered is expected self-efficacy . This term describes "the belief in one’s own ability to plan and perform the necessary (required) actions in such a way that future situations can be mastered" (Bandura, 1995 , p. 2). Specifically, individuals with higher self-efficacy expectations engage in more self-regulated learning by setting challenging goals, using learning strategies, and demonstrating high effort and persistence in task completion (Butler & Winne, 1995 ; Duijnhouwer et al., 2012 ; Richardson et al., 2012 ; Schunk, 2008 ).

Also closely related to self-efficacy expectancies is the learner’s self-concept , with this study focusing on academic self-concept. Academic self-concept varies across school subjects and describes learners’ perceptions of their own academic abilities (Hasselhorn & Gold, 2013 ; Pintrich & Garcia, 1994 ; Schunk, 1991 ). Studies show that individuals with a positive self-concept use more self-regulatory strategies (Burnett & Proctor, 2002 ; Ommundsen et al., 2005 ). They are also more motivated and attribute learning success to their own abilities and efforts, thus feeling in control of their learning process (Ommundsen et al., 2005 ).

In order to validate the model of Seufert ( 2018 ) all the different prerequisites are incorporated in one overall factor of resources.

Relations between Cognitive Load and Learners’ Resources

Seufert’s ( 2018 , 2020 ) model implies that the two opposing forces of resources and load are negatively related. Available resources can reduce the cognitive load of a task and thus prevent impairments in learning performance. Thus, cognitive load seems to depend on different characteristics of learners, such as ability, interest, or prior knowledge (Brünken et al., 2003 ).

Individuals with a high level of prior knowledge usually estimate tasks more easily due to existing knowledge structures in long-term memory and show a lower cognitive load from the learning task (Van Gog et al., 2005 ). Therefore, individuals with higher prior knowledge also have more working memory capacity available for parallel self-regulatory processes (Große & Renkl, 2006 ; Moreno, 2006 ). Furthermore, research shows that as learning strategies are used more frequently, their application becomes increasingly automatic and less cognitively taxing (Schwonke, 2015 ). In addition, Steele-Johnson et al. ( 2000 ) demonstrated that individuals with a learning goal orientation are better able to cope with cognitively taxing learning situations and also have higher self-efficacy expectations. Individuals with a performance goal orientation believe more strongly in their ability to perform tasks that are less cognitively demanding. Thus, while individuals with a learning goal orientation prefer cognitively taxing learning environments to increase their knowledge, learners with a performance goal orientation prefer less taxing situations to demonstrate their abilities (Steele-Johnson et al., 2000 ).

However, as task difficulty increases, fewer resources are available and the compensatory effect with respect to cognitive load decreases. For example, confidence in successfully completing a task and belief in one’s own abilities decrease, resulting in lower motivation (Schunk, 1991 ). In addition, learners show less interest (Horvath et al., 2006 ) and prior knowledge might be insufficient when facing higher difficulty (Kalyuga, 2007 ; Van Gog et al., 2005 ). Thus, it can be assumed that individuals with sufficient resources are less burdened by additional self-regulatory processes and are more likely to use these (Seufert, 2018 ). However, these resources decrease with increasing task difficulty.

Regarding the model of Seufert ( 2018 , 2020 ), it is only stated that both forces are relevant to explain the intensity of self-regulatory activities and that they are negatively related. It is not explicitly argued that self-regulation varies because of the interaction of both factors. Thus, this paper focuses on the relations between task-difficulty and self-regulation mediated by load and separately by resources as a mediator. For the sake of completeness, the negative relationship between load and resources is will be substantiated.

Present Study

Since the aim of this study is to validate the model of self-regulation as a function of resources and perceived cognitive load (Seufert, 2018 , 2020 ; see Fig.  1 ), the different parameters of the model are evaluated and analyzed in terms of their expected relationships.

The basic idea of this study is to examine these parameters with regard to four successive exams of varying difficulty and to survey students for each exam regarding their perceived task difficulty, their resources and load, and their respective self-regulatory activities. Thus, the task at hand encompasses both the exam and the preparation for the exam, but the perceived task difficulty is primarily determined by the subject and perceived complexity (i.e. intrinsic cognitive load) in relation to the specific exam. The learner’s resources and the perceived load (i.e. extraneous and germane load), on the other hand, primarily come into play in the preparation for the exams.

In order to ensure high task relevance and a wide range of task difficulty, we chose exams in English, Mathematics, German, and a profile subject with presumably varying difficulty from rather easy to rather difficult. Nevertheless, students rated the difficulty of each task, as perceived task difficulty is crucial in the model to be tested. Overall, the study is a within-subject study with four measurement points and additional questionnaires before the first exam to assess descriptive data and task-related resources. As learner resources, prior knowledge, strategy knowledge, interest, motivational goal orientation, academic self-concept, and self-efficacy expectancy were assessed with reference to the INVO-model (Hasselhorn & Gold, 2013 ). Regarding cognitive load, we assessed intrinsic, extraneous, and germane load.

With this setup, we aimed to validate Seufert’s ( 2018 , 2020 ) model and its main assumptions. The research question is whether the intensity of self-regulatory activities depends on perceived task difficulty and whether this influence is mediated by learners’ resources and cognitive load. This interplay of parameters will be analyzed with respect to the use of cognitive strategies (RQ1) and metacognitive strategies (RQ2).

As modeled by Seufert ( 2018 , 2020 ), we hypothesize that the use of cognitive strategies (H1a) is influenced by perceived task difficulty with a significant negative quadratic effect. We further expect that the effect of task difficulty on cognitive strategy use will be mediated by learner resources (H1b) and cognitive load (H1c).

For the use of metacognitive strategies (H2), we also expect that it will be affected by perceived task difficulty with a significant negative quadratic effect (H2a). We further expect that the effect of task difficulty on metacognitive strategy use will be mediated by learner resources (H2b) and cognitive load (H2c).

Participants

The 67 participants in the study (53.7% female) were students in grade 11 at a local vocational school. The decision to use this sample was based on the assumption that students of this age and school type have already developed learning strategies with sufficient variance (Paris & Newman, 1990 ). They were around 17 years old ( M age  = 16.72; SD age  = 0.83) and had one of the following profiles: technology and physics (13.4%), economics (46.3%), welfare, education and psychology (20.9%) and public health (19.4%). Students gave their written informed consent in accordance with the Declaration of Helsinki and with the ethical committee of the authors’ institution.

Design and Procedure

We implemented a within-subject design providing different school subjects with different perceived task difficulty (German, English, Mathematics, profile subject), assessing students’ cognitive load and available resources, and their use of self-regulatory activities in terms of strategy use in their respective exam preparation. In order to ensure a sufficiently large variance in exam difficulty, the selection of exam subjects was based on an expert assessment by the deputy headmaster. According to this, German—as the mother tongue of most pupils—was assessed as the easiest exam, followed by English as a foreign language. Mathematics was rated as the most difficult. The difficulty of the profile subject is assumed to vary according to the students’ chosen focus. The four exams took place over a period of seven calendar days, with a break of two to three days between each two exams. The sequence of exams was the same for all participants, with English, followed by Mathematics, German and the profile subject.

About a week before the first exam, participants completed a pretest. The pretest comprised, an online questionnaire that lasted about 30 min and was completed in the school’s computer labs. Following an introduction with an explanation of the study process participants gave their informed consent for participating in the study. The online survey aimed to collect demographic data, assess the perceived difficulty of subjects, the expected difficulty of the upcoming exams, interest levels, and record the students’ previous grades in each respective subject. Next, the students completed the questionnaires to assess their previous use of learning strategies, learning and achievement motivation in each subject, and their academic self-concept. Additionally, the online-questionnaire gathered data on the students’ self-efficacy expectations regarding the different exams.

In the school hours directly after each exam, the students received a paper–pencil questionnaire focused on the preceding exam and its preparation. Each questionnaire took approximately 15 min to complete and started with items related to exam preparation in general. The students then answered the items for assessing the learning strategies and a differentiated measurement of cognitive load experienced during the respective exam.

Instruments

Perceived task difficulty.

The perceived difficulty of the four exams and exam preparation in English, Mathematics, German, and the profile subject was recorded in the pretest as the central independent variable in this study. We used two items ("How do you rate the difficulty of the respective subject in general?", "How do you rate the difficulty of the upcoming exam in the respective subject?") with a seven-point Likert scale ranging from 1 = very easy to 7 = very difficult. In addition, the perceived complexity of the tasks following the respective examinations was included for this independent variable in order to also take into account the demands of regulating one’s own learning process. For this purpose, the two items for intrinsic cognitive load were integrated (e.g., "The exam was very complex”; “For this exam, many things needed to be kept in mind simultaneously”). For the further statistical analyses, an overall scale for perceived difficulty for each subject was calculated based on the 4 items with an internal consistency of α = 0.64.

  • Learners’ resources

According to Seufert’s model (2018, 2020), the relationship to cognitive load and task difficulty applies to all these resources in the same way. For this reason, no differentiated assumptions were made about the individual resources, but they were summarized in an omnibus measure. Finally, due to pragmatic considerations and the context of the repeated measures design in an authentic school setting, no further resources were included.

To measure available resources as another mediator variable, the study used an overall scale consisting of various resources in the learning context, namely goal orientation, previous strategy use, prior knowledge, interest, self-concept, and self-efficacy. These were recorded subject-specifically in the online pre-test.

Goal orientation

Motivational goal orientation was assessed using the Scales to Assess Motivation to Learn and Achieve (SELLMO; Spinath et al., 2002 ). Since only orientation to learning goals and, to some extent, approach-performance goals have so far been shown to be conducive to the use of learning strategies, only these two scales were measured: Learning goals with six items (α = 0.79, e.g., "In Math/ English/ German/ In the profile subject, I am concerned with understanding complicated content.") and approach-achievement goals with four items (α = 0.73, e.g., "In Math/ English/ German/ In the profile subject, I am concerned with showing that I am good at something."). Items were recorded using a five-point Likert scale (1 = not at all true, 5 = true exactly) separately for each subject.

Previous strategy use

Prior experience in strategy use as another resource was recorded in the pre-test as the frequency of previous strategy use, using the Learning Strategies in Study Questionnaire (LIST; Wild & Schiefele, 1994 ; described earlier). For this study, 11 items were used to assess previous use of cognitive learning strategies with subscales for repetition, organization and elaboration (α = 0.64; e.g., "I make short summaries of the most important content to help me think"). For the metacognitive learning strategies scale, nine items for planning, monitoring and regulation were used (α = 0.47; e.g., "Before learning an area of material, I consider how to proceed most effectively"). The items were selected according to their suitability for the specific context of exam preparation. Since the items cover a wide range of cognitive, respective metacognitive strategies, the low internal consistency is not surprising.

All items were answered on a five-point response scale from 1 = very seldom to 5 = very frequently.

Prior knowledge

As prior knowledge, the grade in the past exam in the respective subject was asked on a six-point response scale from 1 = very good to 6 = insufficient (range of responses across all subjects: past grade: Min  = 1, Max  = 6). For the aggregated resource score, it was recoded so that higher scores mean higher prior knowledge.

Learners’ interest was queried using the item "How interesting do you find the following subjects?" which students answered on a seven-point Likert scale ranging from 1 = not interesting at all to 7 = very interesting.

Self-concept

To determine the perception of one’s own academic abilities in the respective subjects as another resource, the Scales for the Assessment of Academic Self-Concept (SESSKO; Schöne et al., 2002 ) were used. The questionnaire contains 22 items that are used to assess academic ability, differentiating four reference norms: In comparison to classmates ("social," six items, α = 0.95, e.g., "I can do less than my classmates in (subject) … more than my classmates."), in comparison to the demands of the school context ("criterial," five items, α = 0.94, e.g., "When I look at what we have to be able to do in (subject), I find that I can do little … a lot."), compared to earlier time points ("individual," six items, α = 0.93, e.g., "I cope with the tasks in (subject) worse than before … better than before.") and without considering a reference norm ("absolute," five items, α = 0.95, e.g., "I find learning new things in (subject) difficult … easy."). Students responded to the items specifically for each subject using a five-point Likert scale.

Self-efficacy

In addition, the General Self-Efficacy Expectancy scale (Schwarzer & Jerusalem, 1999 ) was used to assess students’ self-efficacy expectancy, and thus their confidence and trust in being able to handle a difficult situation, with 10 items. An exemplary item reads, "I face difficulties calmly because I can always trust my abilities." Participants answered these items for each subject on a four-point Likert scale ranging from 1 = not true to 4 = true exactly (α = 0.93).

For further statistical analyses, an aggregated resource scale was used with an internal consistency of α = 0.86, calculated from the mean of the six resources scales described above. As the resources were based on different scales, they were first converted into percentages and then an overall scale for the resources was formed from the mean.

Strategy use

To assess strategy use during exam preparation as the dependent variable, the items relating to cognitive and metacognitive strategy of the Learning Strategies in Study (LIST; Wild & Schiefele, 1994 ) questionnaire were administered after each exam analogous to the previous strategy use, described above. The items were adapted to refer to the preparation of the previous exam. For cognitive learning strategies scale, with subscales for repetition, organization and elaboration (e.g., "I made short summaries of the most important content to help me think") internal consistency was α = 0.85. For the metacognitive learning strategies scale, with subscales for planning, monitoring and regulation (e.g., "Before learning an area of material, I considered how to proceed most effectively.") internal consistency was α = 0.85.

Cognitive Load

The Questionnaire for the Subjective Measurement of Cognitive Load by Klepsch et al. ( 2017 ) was given after each exam to assess cognitive load experienced during exam preparation. The questionnaire contains three items for extraneous cognitive load (α = 0.74; e.g., “The presentation of the learning material was unfavorable to really learn something”) and two items for germane cognitive load (α = 0.68; e.g., “I have made an effort not only to memorize individual things, but also to understand the overall context”). The two items for intrinsic cognitive load (α = 0.61) were used for perceived task difficulty, as described above. In addition, the questionnaire includes two general items about the effort invested in the learning task. All items were answered by participants on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). For the statistical analyses, an overall scale with an internal consistency of α = 0.62 was formed for cognitive load, consisting of the mean of the items on extraneous and germane cognitive load as well as on invested effort.

Data preparation and analysis

To prepare and analyze the data, we used SPSS Statistics version 25. To test for differences in task difficulty, cognitive and metacognitive strategies between school subjects, we conducted repeated measures ANOVAs. To avoid the accumulation of alpha errors, the bonferroni-corrected p-values for the ANOVA post hoc tests in the manipulation check were calculated and reported at a significance level of p < 0.05. As our study design implied a nested data structure including learners (level 2) and their self-reports and outcomes (level 1: perceived task difficulty, cognitive load, relevant resources, use of learning strategies with respect to each of the four exams), as the assumptions for parametric testing were met, we tested whether hierarchical modeling was suitable for testing our hypothesis. Upon inspection of Q-Q-plots, normality of level 2 residuals was assumed. Homoscedasticity was also assumed based on inspection of scatterplots for level 2 residuals and predicted values. Finally, based on the ICC criteria (> 0.05), hierarchical modeling was appropriate and thus we calculated hierarchical regression models (Heck et al., 2013 ; Nezlek et al., 2006 ). To account for initial differences of the respective learners’ variables, we used random intercept fixed-slope models. Based on our approach of using a homogeneous sample in a comparatively standardized exam context, we assumed that the differences on the dependent variables were quite similar across learners. Thus, no random slopes were tested. To allow reasonable interpretations as well as to compare the relative influence of the different predictors, all variables were z-transformed. Based on our hypothesis, we used suitable paths models and applied Sobel tests to test our hypothesis (see Appendix ). Based on the theoretical model assumptions, a quadratic effect appeared plausible in addition to linear effects, so these were also included in the statistical analyses.

Manipulation and assumption check

We assumed that different school subjects led to differing perceived task-difficulties. Based on the reported perceived task difficulty, differences in task-difficulty were determined (( F (2.71, 178.73) = 8.65, MSE  = 1.09, p  < 0.001, η 2  = 0.12). By post-hoc testing, we revealed a higher perceived task difficulty in Math compared to English ( MD  = 0.69, SE  = 0.19, p  = 0.004, d  = 0.60), a higher difficulty of the profile subject compared to English ( MD  = 0.78, SE  = 0.16, p  < 0.001, d  = 0.77) and a higher difficulty of the profile subject compared to German (see Table  1 for respective means).

To test the assumed interplay of the two forces, load and resources, described in the model of Seufert ( 2018 , 2020 ), the mean correlation between learners’ resources and cognitive load over all subjects was calculated. Overall, with r  = 0.22 ( p  = 0.037) the assumed negative correlation was not supported by the present findings. In detail, we found a heterogenous pattern for the subjects ( r English  = -0.22, p  = 0.068; r German  = 0.24, p  = 0.050; r Mathematics  = -0.04, p  = 0.771; r Profile  = 0.24, p  = 0.048).

Descriptives

We revealed significant differences between subjects, concerning the use of cognitive and metacognitive learning strategies during the respective exam preparation (see Table  2 ).

Post-hoc testing revealed a more frequent use of cognitive learning strategies during preparation for the Mathematics exam compared to the English exam ( MD  = 0.29, SE  = 0.10, p  = 0.04, d  = 0.38). In addition, metacognitive strategies were used more frequently in preparing for the exam in the profile subject than for the German exam ( MD  = 0.32, SE  = 0.08, p  = 0.001, d  = 0.36). With regard to cognitive load, the repeated-measures ANOVA revealed significant differences between school subjects ( F (3, 198) = 4.32, MSE  = 0.70, p  = 0.006, η 2  = 0.06). The post-hoc test implied a significantly higher load with respect to the Mathematics ( MD  = 0.44, SE  = 0.14, p  = 0.020, d  = 0.47) and profile subject ( MD  = 0.45, SE  = 0.17, p  = 0.049, d  = 0.47) exams compared to the English exam. Based on the repeated-measures ANOVA, we found no significant differences between subjects in available resources ( F (3, 198) = 2.46, MSE  = 115.31, p  = 0.064).

Effects of Perceived Task Difficulty on Cognitive Strategies (H1)

We tested our hypotheses based on random intercept fixed slope models (for details see method section: data preparation and analysis).

Our first hypothesis focused on the effects of perceived task difficulty on cognitive strategies. In line with our expectations (H1a), which postulated a significant effect of perceived task difficulty on cognitive strategy use, we found significant linear and quadratic total effect (linear γ0(β1) = 0.22, SE  = 0.05, p < 0.001; quadratic: γ0(β2) = -0.07, SE  = 0.03, p = 0.020; see Table 3 in the appendix). For an overview see Fig.  2 . This finding reflects that although cognitive strategy use increased with increasing perceived task difficulty, cognitive strategy use in exam preparation decreased at higher levels of perceived task difficulty.

figure 2

Path models for cognitive strategy use with resources as mediator (on the left) and cognitive load as mediator (on the right); total effects in brackets

We hypothesized a mediating effect of available resources (H1b). In line with our hypothesis, we revealed a significant indirect linear effect of perceived task difficulty on cognitive strategy use, mediated by available resources ( a  ×  b  = -0.18, z  =—4.90, SE  = 0.04, p  < 0.001; see Table 3 ). With increasing perceived task difficulty, fewer resources were available, which resulted in lower cognitive strategy use. The opposing signs of direct and indirect paths (see Fig.  2 ) indicate a competitive mediation (Zhao et al., 2010 ). Thus, the positive direct effect of difficulty on cognitive strategy use competes with the negative indirect effect that difficulty exerts through decreased resources. Moreover, no significant mediation was found for the quadratic effect ( a  ×  b  = -0.02, z  =—1.88, SE  = 0.01, p  = 0.060).

As hypothesized, we found cognitive load to be a significant mediator (H1c): For the linear effect, based on the Sobel test, we identified cognitive load as a partial mediation ( a  ×  b  = 0.10, z  = 3.27, SE  = 0.03, p  = 0.001; see Table 3  in the appendix). This effect implies that with increasing perceived task difficulty cognitive load also increases, which leads to a more frequent cognitive strategy use. Moreover, consistent with our hypothesis (H1c), the quadratic relationship was fully mediated by cognitive load ( a  ×  b  = -0.02, z  =—2.28, SE  = 0.01, p  = 0.023).

Effects of Perceived Task Difficulty on Meta-Cognitive Strategies (H2)

In our second hypothesis, we focused on the effect of perceived task difficulty on the use of meta-cognitive strategies. For an overview of the results see Fig.  3 . With regard to metacognitive strategy use, we revealed the expected linear total effect (H2a) of perceived task difficulty on learning strategy use (linear: γ0(β1) = 0.16, SE  = 0.05, p  = 0.001). With increasing perceived task difficulty, the use of meta-cognitive strategies increased. However, we found no significant quadratic total effect (quadratic: γ0(β2) =—0.06, SE  = 0.03, p  = 0.063; see Table 4  in the appendix). Due to the lack of a total quadratic effect, we therefore focused more on the total linear effect as well as the indirect effects.

figure 3

Path models for metacognitive strategy use with resources as mediator (on the left) and cognitive load as mediator (on the right); total effects in brackets

As hypothesized, we found an indirect linear effect of perceived task difficulty on metacognitive strategy use mediated by resources ( a  ×  b  = -0.12, z  = -3.61, SE  = 0.03, p  < 0.001). Similar to cognitive strategy use, the pattern of direct and indirect effects revealed a competitive mediation effect. Thus, while increasing difficulty directly relates to increased metacognitive strategy use, it exerts a negative indirect effect by decreasing available resources. No significant, indirect quadratic effect could be supported by the data ( a  ×  b  = -0.01, z  = -1.77, SE  = 0.01, p  = 0.076).

Analyzing the mediation effect of cognitive load (H2c) using a Sobel test, an indirect quadratic relationship was revealed for perceived task difficulty on metacognitive strategy use ( a  ×  b  = -0.02, z  = -2.32, SE  = 0.01, p  = 0.020). In addition, a partial mediation of the linear relationship by cognitive load was found ( a  ×  b  = 0.10, z  = 3.41, SE  = 0.03, p  < 0.001).

The aim of our study was to analyze the assumptions of the model of Self-regulation as a function of resources and perceived Cognitive Load by Seufert ( 2018 , 2020 ) and thus the interplay of task difficulty and self-regulatory activities mediated by resources and load. In order to challenge the model of Seufert ( 2018 , 2020 ) empirically, it was necessary to operationalize different levels of difficulty. We assumed that the exams in English, German, Mathematics, and profile subject would be of increasing difficulty. As our manipulation check confirmed, learners in fact rated the difficulty of these exams in this increasing order. Self-regulatory activities, i.e. the use of different learning strategies, also varied significantly across the four different exams, again in the same increasing order. This indicates a linear increase in both difficulty and self-regulatory activities. Perceived cognitive load also followed this pattern. In contrast, learners’ resources showed a different pattern and tended to remain stable over the four exams. In the following sections the two research questions regarding the interplay of task difficulty, load and resources with cognitive (RQ1) and metacognitive strategy use (RQ2) and the respective hypotheses are discussed.

Effects on Cognitive Strategy Use (RQ1)

Regarding the effects on cognitive strategy use, we observed, consistent with our first hypothesis (H1a), both a linear and a quadratic relationship of perceived task difficulty and cognitive strategy use. According to this, students organized, elaborated, and repeated learning material more often during exam preparation when the task difficulty was moderate, whereas they used cognitive learning strategies less often when the task difficulty was easy or difficult; however, the linear relationship was stronger than the quadratic one. This effect may be attributed to the constrained variance in exam difficulty and the categorization of exams as no more than moderately difficult. Consequently, based on the data collected, it is not possible to fully empirically explore the extent to which difficult exams are related to the theoretically hypothesized decline.

Regarding the ratings of cognitive load, a similar pattern could be observed. Accordingly, it is possible that the cognitive load was not so high with regard to each test, so that, in contrast to previous research (Efklides, 2011 ; Kanfer & Ackerman, 1989 ; Lajoie, 1993 ; Moos & Azevedo, 2008 ; Schwonke, 2015 ), further working memory capacity was still available for the application of self-regulatory strategies. In summary, and in line with other research findings, students responded to perceived difficulty in particular with more frequent use of cognitive strategies to increase their learning success (Baker & Brown, 1984 ; D’Mello & Graesser, 2012 ; Van Loon et al., 2017 ). However, a decrease in strategy use with increasing difficulty was not evident, as both difficulty and cognitive load were, on average, rated as moderate at best.

Regarding the mediation paths we found no mediation by resources with regard to the quadratic relationship between perceived task difficulty and the application of cognitive learning strategies, but a significant indirect linear effect (H1b). Accordingly, as task difficulty increased, available resources, such as interest or self-efficacy expectancy, decreased, resulting in lower cognitive strategy use during exam preparation. This observation supports previous research findings that higher task difficulty is associated with a decrease in resources (Horvath et al., 2006 ; Kalyuga, 2007 ; Schunk, 1991 ; Van Gog et al., 2005 ). In addition, consistent with other research findings, self-regulatory strategies were used more frequently during exam preparation as resources increased, such as higher levels of prior knowledge (DeStefano & LeFevre, 2007 ; Schwonke, 2015 ) and interest (Horvath et al., 2006 ; Schiefele, 1991 ), stronger learning goal or performance goal orientation (Abar & Loken, 2010 ), or higher self-efficacy expectancies (Butler & Winne, 1995 ; Duijnhouwer et al., 2012 ). At the same time, a significant positive direct effect of task difficulty on the use of cognitive strategies remained, revealing a pattern of competitive mediation (Zhao et al., 2010 ). This indicates task difficulty is likely to exert an additional positive effect on cognitive strategy use through one or several other mediators. One possible candidate could be the domain characteristic and their typical learning materials. In math or natural science for example other strategies could be instrumental to deal with abstract materials like formulas, tables or diagrams than in more text-related domains like language or history.

Cognitive load turned out to be a mediator between task difficulty and cognitive strategy use as expected. The inverse U-shaped relationship between task difficulty and cognitive strategy use was fully explained (H1c). Supporting the assumptions of Seuferts model ( 2018 ), learners’ cognitive load initially increased with increasing task difficulty, and according to the quadratic relationship, cognitive load decreased again with higher task difficulty. Hence, cognitive load is directly linked to task difficulty with its task-immanent demands and the additional self-regulatory demands and cognitive load is an important predictor for self-regulatory activities. This is even more interesting as both aspects, extraneous and germane aspects of load have been incorporated. Which of these load types act in which way nevertheless needs further, differentiated investigations.

Effects on Metacognitive Strategy Use (RQ2)

Regarding the effects on metacognitive strategy use, a positive linear effect of perceived difficulty on the use of metacognitive learning strategies was plausible whereas the quadratic relationship was not supported (H2a). As task difficulty increased, students more frequently planned, monitored, and regulated their learning process during exam preparation. This finding was in contrast to the assumption that metacognitive strategies in particular are used less frequently when task difficulty is too high, as planning, monitoring, and regulating the learning process requires a particularly large amount of cognitive capacity (De Bruin & van Merriënboer, 2017 ). Thus, the present findings were not in line with previous research findings that learners are most self-regulated when faced with moderate challenges (Atkinson, 1957 ; Middleton & Midgley, 2002 ; Turner & Meyer, 2004 ). As discussed earlier, this finding may be due to the fact that there were variance limitations with respect to task difficulty and cognitive load.

Regarding the assumed mediation by the available resources, we found a negative indirect linear effect of perceived difficulty on the use of metacognitive learning strategies, only (H2b). The higher the task difficulty, the fewer resources were available, resulting in less use of metacognitive learning strategies in exam preparation. Based on our findings, learners had decreasing available resources with increasing task difficulty. Hence high task difficulty went along with less planning, monitoring, and regulating the learning process during exam preparation. Similar to cognitive strategies, the remaining positive direct effect indicated a competitive mediation pattern. Therefore, the effect of task difficulty on metacognitive strategy use may be further explained by complementary mediators. As mentioned earlier, domain characteristics could be taken into account.

We found an indirect quadratic effect and a full mediation by cognitive load of the linear effect of task difficulty on metacognitive strategy use (H2c). Students reported the highest cognitive load at moderate task difficulty, where cognitive load was associated with more frequent metacognitive strategy use. The linear relationship between cognitive load and metacognitive strategy use was not in line with the findings of previous research literature that when cognitive load is too high, there are no longer sufficient resources for additional self-regulatory processes (e.g. Eitel et al., 2020 ). Given that even the exams with the highest difficulty were only rated moderately in load, learners appear to have sufficient resources to cope with the load, even for the most difficult exams analyzed in this study.

Theoretical and Empirical Implications

The integrated model of Seufert ( 2018 ) could be partially supported by the results. Key correlations were reflected in the results. It was found that as task difficulty increased, cognitive load increased while resources decreased. Due to the lack of interaction between cognitive load and resources, it was not shown, as expected, that individuals with moderate task difficulty increased self-regulated learning, as cognitive load and resources were balanced in this case. That load and resources are not negatively related as expected might be explained by the combination of extraneous and germane load into one overall load indicator as both types could have reverse relations with resources. While for extraneous load one would assume a negative relation to resources, this might be the other way round for germane load. Learners can invest more mental effort when they have more resources. This might have been the case in this study. Future research should re-test this model with differentiated measures of load and separate analyses for germane and extraneous processing. The same is the case for the analysis of learners’ resources. Based on the model an overall, combined indicator with many different resources have been built and analyzed. However, a differentiated analysis of how different resources affect strategy use specifically would be valuable. Moreover, a stronger focus on cognitive resources and a higher variance in difficulty could be theoretically and empirically interesting.

Methodological implications, strengths and limitations

Based on the interpretation of the results and the theoretical implications, limitations as well as strengths of the study can be identified.

The first positive aspect of this study is that a high degree of everyday relevance was achieved by asking students to prepare for upcoming exams as usual and to report on their preparation, cognitive load, and resources. Thus, participants were not asked to acquire subject matter in an artificially created learning situation. In addition, established questionnaires were used to assess learning strategies, cognitive load, and available resources. Another strength is that all hypotheses were tested separately for cognitive and metacognitive learning strategies, whereas previous studies often addressed self-regulatory processes in general terms. Furthermore, this study included other relevant variables such as motivational goal orientation, academic self-concept, or self-efficacy expectancies, which are of central importance in the learning context. The within-subject design also has advantages. For example, because the same person is interviewed repeatedly, fewer experimental participants are needed. Furthermore, the design allows for perfect parallelization of all person-specific confounding variables (Charness et al., 2012 ). Finally, the computation of multilevel models can be evaluated positively, as dependencies between repeated interviews of the same person are taken into account (Heck et al., 2013 ).

Despite these strengths, the study has methodological limitations. First, the sample was small, so that weaker or medium effects may have gone undetected due to low statistical power. While the general rule of thumb for multi-level models is that 20–30 units at level 2 are sufficient, the literature indicates that a significantly higher number (80 or more) at level 2 is required for multi-level mediation in order for the model to converge reliably (Li & Beretvas, 2013 ).

In addition, the sample was homogeneous and not very representative, as only students in the 11th grade of a technical secondary school were surveyed. This makes it difficult to extrapolate the results to other age groups or school types and thus to generalize. Moreover, the limited number of measurement points, specifically the four times we measured in relation to the written exams per subject, might have posed a significant issue in accurately capturing the extended period of exam preparation. This scarcity in data points could undermine the reliability of multilevel model calculations and raise doubts about the accuracy of the parameter estimates. Hence, in forthcoming studies, the utilization of methodologies like experience sampling could potentially provide deeper insights and yield more dependable data for parameter estimation purposes. Finally, due to the small sample size, complex models could not be established (Li & Beretvas, 2013 ). However, more complete models that address resources and cognitive load simultaneously as mediators are indispensable to fully uncover the complex relationships indicated by Seufert ( 2018 , 2020 ). Additionally, exploring more complex analyses, such as incorporating random slope models, could provide valuable insights into potential individual variations in the relationships and interplay of self-regulation, cognitive load, and task difficulty. Another important issue that might be addressed in future studies is the integration of learning outcome measures as an additional criterion. Based on models of self-regulation, actual performance is reflected after learning and will therefore inform future learning situations and engagement in self-regulatory activities (e.g. Zimmerman, 2002 ). In the present study, this influence could only have been measured after the assessment of the exam, i.e. during the holidays, which was not practical. From a theoretical point of view, we only focused on self-regulatory activities as this is the dependent measure in Seufert’s ( 2018 ) model, but the picture would still be more complete with complementary data. It could be assessed how current self-regulatory activities are related to actual learning performance and whether learners’ planning for the next phase of exam preparation is affected. Weaknesses in the operationalization of this study are also evident. For example, the independent variable focused more on the difficulty of the learning task to be completed, whereas the difficulty of simultaneously using self-regulation strategies was only indirectly taken into account via the retrospective recording of intrinsic cognitive load.

In addition, the use of self-report questionnaires for retrospective assessment of self-regulated learning and cognitive load can be viewed critically. In this regard, the quality of self-report questionnaires must be questioned, as individuals often exhibit introspection deficits, cannot adequately recall their strategy use, and thus make inaccurate statements about their learning behavior (Greene & Azevedo, 2010 ). Direct situational measures of strategies or multi-method approaches are mostly stronger related to learning outcome measures (Artelt, 2000 ; Dörrenbächer-Ulrich et al., 2021 ; Rovers et al., 2019). In addition, the assessments of strategy utilisation could have been affected by the performance experiences in the exams. Furthermore, self-report measures of self-regulatory strategy use are based on the assumption that self-regulated learning is static and can be recorded separately from the current learning process (Greene & Azevedo, 2010 ). Thus, the cyclical process of self-regulated learning could not be captured in this study. This raises the question of the extent to which the difficulty ratings of the exams also changed during the learning process, and the extent to which self-regulated learning behaviors changed as a result. In addition, changes in cognitive load during the learning process could not be taken into account because a self-report questionnaire was used for retrospective recording (Schmeck, et al., 2015 ). These weaknesses could be counteracted by diary studies, for example. However, these represent an ethically questionable burden for students in the phase of highly relevant final examinations. For this reason, we consider the applied approach of retrospective recording to be the most appropriate for this authentic setting, despite the weaknesses mentioned.

Finally, the measurement of learners’ resources comprised a highly aggregated score of different constructs. This was in line with Seufert’s ( 2018 , 2020 ) model, in which the assumptions about the relationship between learners’ resources and task difficulty apply equally to all types of resources. However, this assumption can itself be questioned, although this was not the focus of the present study. In addition, further resources or measurement methods could have been considered. Cognitive resources like prior knowledge, measured by valid tests instead of prior grades, or working memory capacity could have been taken into account and could have strengthened the relation to cognitive load. With regard to the INVO model, learners’ achievement emotions could be a relevant parameter as they influence learning motivation, strategy use, and academic performance (Mega et al., 2014 ; Pekrun et al., 2002 ). Because of these limitations, the practical implications that can be derived from the study are limited, and therefore further research is needed.

Practical Implications and Future Perspectives

Self-regulated learning is an important area of research due to its profound educational implications in shaping individuals’ lifelong learning journey (Dignath & Büttner, 2008 ). In our study, we focused on the context of learning in a Vocational College. In this context our findings imply that task difficulty, cognitive load, and resources are relevant factors to consider in relation to self-regulated learning.

Therefore, it seems to be important for students that they are challenged with different task difficulties, including challenging ones, in order to stimulate self-regulation processes in a systematic way. However, based on previous literature, this goes along with the inherent risk of an overload of working memory, which is why fewer learning strategies are used (Efklides, 2011 ; Kanfer & Ackerman, 1989 ; Lajoie, 1993 ; Moos & Azevedo, 2008 ; Schwonke, 2015 ). Future research should continue to address this issue. When challenging learners, resources like a positive self-concept, high self-efficacy expectations, a strong interest and motivation seem to be crucial as they mediated at least the linear effects on cognitive and metacognitive strategy use. Interest could be raised by creating a reference to everyday life even for abstract learning content (Hasselhorn & Gold, 2013 ). Self-efficacy and confidence could be fostered by positively reinforcing small successes with praise (Drössler et al., 2007 ). In addition, to improve academic self-concept, strengths should be highlighted and weaknesses should be addressed with tips and suggestions for improvement (Hasselhorn & Gold, 2013 ). These instructional approaches could be used to foster self-regulated learning while being challenged by difficult tasks.

In order to adequately capture self-regulated learning, contemporary and process-oriented methods should be used in future studies. For example, the thinking aloud method (Bannert & Mengelkamp, 2008 ) or the evaluation of traces of cognitive processing during the learning process are suitable for this purpose, e.g., notes, markings, or diagrams drawn (Winne & Perry, 2000 ). Alternatively, learning diaries can be used to continuously record the learning process over time (Nückles et al., 2020 ; Zimmerman, 2008 ). In this way, it would be possible to record the cyclical phases of self-regulated learning. In addition, further subscales of resource-related strategies should be considered in the future, as well as a more general focus on the individual subscales of all learning strategies.

With regard to a renewed review of Seufert’s ( 2018 ) integrated model for predicting self-regulated learning, future research should focus more on cognitive resources, as these are presumably associated with learners’ cognitive load. Despite the model’s general assumptions on resources and load it would nevertheless be interesting to test the model for effects of different resources and for different aspects of cognitive load.

The aim of the present study was to empirically challenge Seufert’s model (Seufert, 2018 ). The assumed interplay that self-regulatory strategies are increasingly used with increasing task difficulty, which was mediated by increasing cognitive load, could be empirically supported. For cognitive strategies even the u-shaped relation could be found which indicates that for too difficult tasks learners cease to use those strategies. With increasing task difficulty, learners exhibit fewer personal factors relevant to successful learning, such as a positive self-concept or high self-efficacy expectations. As a result, the positive influence of difficulty on the use of self-regulatory strategies may be compromised. Therefore, the task of teachers in promoting self-regulated learning is to confront learners with challenging tasks and at the same time to strengthen relevant facilitating factors or individual prerequisites for successful learning while managing cognitive load.

In future studies, the correlations observed in this research should also be examined with regard to interindividual differences in a classroom in order to achieve the best possible promotion of self-regulated learning for all students. This could be one promising way to help learners discover the world independently and to actively construct knowledge and gain learning competencies.

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Seufert, T., Hamm, V., Vogt, A. et al. The Interplay of Cognitive Load, Learners’ Resources and Self-regulation. Educ Psychol Rev 36 , 50 (2024). https://doi.org/10.1007/s10648-024-09890-1

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Learning: Theory and Research

Learning theory and research have long been the province of education and psychology, but what is now known about how people learn comes from research in many different disciplines. This chapter of the Teaching Guide introduces three central learning theories, as well as relevant research from the fields of neuroscience, anthropology, cognitive science, psychology, and education.

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Applications of social theories of learning in health professions education programs: A scoping review

Banan mukhalalati.

1 Clinical Pharmacy and Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar

Sara Elshami

Myriam eljaam, farhat naz hussain.

2 Pharmaceutical Sciences Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar

Abdel Hakim Bishawi

3 Research and Instruction Section, Library Department, Qatar University, Doha, Qatar

Associated Data

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author/s.

Introduction

In health professions education (HPE), acknowledging and understanding the theories behind the learning process is important in optimizing learning environments, enhancing efficiency, and harmonizing the education system. Hence, it is argued that learning theories should influence educational curricula, interventions planning, implementation, and evaluation in health professions education programs (HPEPs). However, learning theories are not regularly and consistently implemented in educational practices, partly due to a paucity of specific in-context examples to help educators consider the relevance of the theories to their teaching setting. This scoping review attempts to provide an overview of the use of social theories of learning (SToLs) in HPEPs.

A scoping search strategy was designed to identify the relevant articles using two key concepts: SToLs, and HPEPs. Four databases (PubMed, ERIC, ProQuest, and Cochrane) were searched for primary research studies published in English from 2011 to 2020. No study design restrictions were applied. Data analysis involved a descriptive qualitative and quantitative summary according to the SToL identified, context of use, and included discipline.

Nine studies met the inclusion criteria and were included in the analysis. Only two SToLs were identified in this review: Bandura's social learning theory ( n = 5) and Lave and Wenger's communities of practice (CoP) theory ( n = 4). A total of five studies used SToLs in nursing programs, one in medicine, one in pharmacy, and two used SToLs in multi-disciplinary programs. SToLs were predominantly used in teaching and learning ( n = 7), with the remaining focusing on assessment ( n = 1) and curriculum design ( n = 1).

Conclusions

This review illustrated the successful and effective use of SToLs in different HPEPs, which can be used as a guide for educators and researchers on the application of SToLs in other HPEPs. However, the limited number of HPEPs that apply and report the use of SToLs suggests a potential disconnect between SToLs and educational practices. Therefore, this review supports earlier calls for collaborative reform initiatives to enhance the optimal use of SToLs in HPEPs. Future research should focus on the applicability and usefulness of other theories of learning in HPEPs and on measuring implementation outcomes.

Systematic Review Registration: https://www.researchregistry.com/browse-the-registry#registryofsystematicreviewsmetaanalyses/registryofsystematicreviewsmeta-analysesdetails/60070249970590001bd06f38/ , identifier review registry1069.

Health professions education (HPE) is the field of expertise applied to the education of health care practitioners which caters to the specific requirements of students and is used to develop, implement, and evaluate all aspects of health professions curriculum ( 1 ). Acknowledging and understanding the theories behind the learning process is important in optimizing learning environments, enhancing efficiency, and harmonizing the education system ( 2 ), since theory and practice are inextricably linked and mutually inform each other ( 3 , 4 ). Understanding learning theories helps academics and researchers recognize the nature of knowledge acquisition and how to measure learning outcomes. This improved perception will enhance the scholarship of teaching and the understanding of educators within various contexts, namely teaching, curriculum development, mentoring, academic leadership, and learner assessment ( 5 ). Furthermore, it will help learners recognize their learning processes and ultimately assist in enhancing their learning outcomes ( 6 ). Learning theories can be implemented, based on appropriateness, in learning processes at individual, group or community levels and in various forms of educational activities ( 7 ).

In health professional education programs (HPEPs), learning theories are not regularly and consistently implemented, which has resulted in accreditation bodies dictating educational agendas ( 8 ), variation in the extent to which learning theories are used in HPEPs, and ultimately a potential disconnect between learning theories, curriculum design, outcome evaluation, and educational practices ( 9 ). This is also evidenced by an unfamiliarity among educators inadequately trained to apply theories in a range of contexts with various learner characteristics ( 5 , 6 , 10 – 12 ). Mukhalalati and Taylor provide an easy-to-use summarized guide of key learning theories used in HPEPs with examples of how they can be applied. The guide aims to assist healthcare professional educators in selecting the most appropriate learning theory to better inform curricula design, teaching strategies, and assessment methods, which in turn reflects on learner experience ( 13 ). There is a paucity of literature reviewing the use of learning theories in HPE, the majority of this being generally descriptive, explaining different learning theories and potential HPEP application. For example, little has been reported about the use of learning theories or active learning strategies in e-learning for evidence-based practices ( 14 ), or making suggestions for utilizing the conceptual aspects of learning theories in the identification and implementation of effective practices for evaluating teaching practice ( 15 ). With a focus on the significance of health professions educators' professional development, using learning theories to enhance teaching skills, particularly in clinical settings ( 16 ), the extant literature does not provide clear guidance, for example via the provision of examples of practical application and how these conceptual frameworks might advance the scholarship of teaching and learning in HPEPs. Therefore, health professions education scholars recommend conducting more research into the influence of implementing learning theories on core education components of the HPEPs, namely: curriculum design, content development, teaching, and assessment ( 13 , 17 ). Such research aims to demonstrate the benefits of implementing learning theories and pedagogies in HPEPs, ultimately reducing the gap between learning theories and educational practices ( 17 ).

Social theories of learning (SToLs) play an important role in the design and implementation of HPEPs ( 2 , 10 , 18 ). SToLs integrate the concept of behavioral modeling and focus on social interaction, the person, context, community, and the desired behavior as the main facilitators of learning ( 19 ). The use of SToLs in HPEPs varies possibly due in part to a lack of awareness of available SToLs and a paucity of specific in-context examples to help educators consider the theories relevant to their teaching situation. SToLs include zone of proximal development, sociocultural theories, Bandura's social learning and social cognitive theories (SLT and SCT), situated cognition, and communities of practice ( 13 , 20 – 25 ). Zone of proximal development is defined as “the distance between the actual development level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with a more capable peer” ( 26 ). According to sociocultural theories, learning and development are embedded within social events and take place as a learner interacts with other people, things, and events in a collaborative setting ( 26 ). Bandura's social learning theories (SLTs), i.e., SLT and SCT, stress the necessity of observing, modeling, and mimicking other people's behaviors, attitudes, and emotional reactions such that environmental and cognitive variables interact to impact human learning and behavior ( 18 , 25 ). Situated cognition theory asserts that learning occurs when a learner is doing something in both the real and virtual worlds, and hence learning takes place in a situated activity with social, cultural, and physical settings ( 27 ). Community of practice “are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly” ( 28 ).

To date, no study has examined the application of SToLs in HPEPs and the nature of their use. Consequently, this scoping review aims to examine the application of SToLs in HPEPs. The specific objectives are to (1) identify the SToLs applied to HPEPs, and (2) examine how SToLs are applied to learning and teaching processes in HPEPs.

Protocol and registration

This study adopted a scoping review approach involving exploring and documenting the breadth of knowledge and practice in the investigated topic ( 29 ). The protocol for this scoping review was registered at RESEARCH REGISTRY [ https://www.researchregistry.com/browse-the-registry#registryofsystematicreviewsmeta-analyses/registryofsystematicreviewsmeta-analysesdetails/60070249970590001bd06f38/ ] with the number [reviewregistry1069]. This scoping review is compliant with the PRISMA statement for scoping reviews (PRISMA-ScR) ( 30 ).

Eligibility criteria

The main focus of this review was to identify articles that describe the applications of SToLs in undergraduate or postgraduate teaching and learning processes. The eligibility criteria included primary research studies that were electronically available in their entirety, published in English during the last 10 years (i.e., 2011–2020), and that reported the use of a SToL, namely: zone of proximal development, sociocultural theories, Bandura's SLTs, situated cognition, and communities of practice.

Primary research articles should report the use of SToLs explicitly and as a central theme, and a description of how SToLs were applied in HPEPs should be mentioned in order to be included in the study. No restrictions were applied to the study design. Primary research studies that used a theory other than the determined ones, mentioned SToLs only in the introduction, or used SToLs for data analysis, and/or as a theoretical framework, rather than as an intervention or an application in HPEP teaching and learning processes, were also excluded. Moreover, articles published more than 10 years ago were not included. Based on the authors experience in this field and on their extensive review of the literature, the scarcity of research that applies SToLs to undergraduate and postgraduate HPE became apparent ( 8 , 9 , 31 ). An initial testing search was conducted with no timeframe boundaries, to refine the search strategy and conduct a comprehensive review. Despite returning a significant number of records, initial screening indicated the irrelevance of the vast majority of studies. Therefore, the authors decided to restrict the timeframe to 10 years to reflect the most recent application of SToLs in HPE and the growth and volume of knowledge related to teaching and learning. Article types other than primary research literature (e.g., reviews, editorials, letters, opinion articles, commentaries, essays, preliminary notes, pre-print/in process, and conference papers) were also excluded from this review because such applications are usually reported in primary research articles. Theses and dissertations were also excluded because they risked being less scientifically rigorous due to a lack of peer-review and being unpublished in commercial journals ( 32 ).

Information sources

The search strategy was developed by a multidisciplinary team. This included academics (BM, FH, ME, and SE) with expertise in pharmacy, healthcare professions education, learning theories, and systematic review studies, and an academic research and instruction librarian (AB) with expertise in health science, education, pharmacy, and medical databases. A search of the electronic literature was performed by AB in December 2020 and January 2021, using PubMed, ERIC, ProQuest, and Cochrane databases. Two key concepts (SToLs, HPEPs) were combined using the Boolean connector (AND). Keywords used in the social learning theories concept search included: “social learning theories,” “social theories of learning,” “social cognitive theories,” “zone of proximal development,” “sociocultural theories,” “situated cognition,” “community/communities of practice.” Keywords for this concept were combined using the Boolean connector (OR). Keywords used to search for the HPEPs concept included “healthcare professional education,” “health care professional education,” “medical program education,” “pharmacy program education,” “health sciences program education,” “nursing program education,” “midwifery program education,” “nutrition program education,” “dietician program education,” “biomedical program education,” “physiotherapy program education,” “physical therapy program education,” “occupational therapy program education,” “radiation therapy program education,” “public health program education,” and “dental program education.” Keywords for this concept were combined using the Boolean connector (OR). Keywords were matched to database-specific indexing terms and applied based on each database as appropriate.

The PubMed database was searched on December 22, 2020, implementing date (i.e., 2011–2020) and language (i.e., English only) filters, resulting in 689 articles. The following search strategy was used: “social learning theor * ”[Title/Abstract] OR “social theor * of learning”[Title/Abstract] OR “social cognitive theor * ”[Title/Abstract] OR “zone of proximal development”[Title/Abstract] OR “situated cognition”[Title/Abstract] OR “sociocultural theor * ”[Title/Abstract] OR communit * of practice[Title/Abstract] AND Education[MeSH Terms] OR healthcare professional education[Title/Abstract] OR health care professional education[Title/Abstract] OR health sciences program education[Title/Abstract] OR nutrition program education[Title/Abstract] OR diet * program education[Title/Abstract] OR biomedical program education[Title/Abstract] OR physiotherapy program education[Title/Abstract] OR physical therapy program education[Title/Abstract] OR occupational therapy program education[Title/Abstract] OR radiation therapy program education[Title/Abstract]. Completed search strategies for other databases are presented in Supplementary material 1 .

Selection of sources of evidence

Two investigators (BM and FH) conducted the title and abstract screening for the identified articles from the search strategy outlined above after duplicates and any clearly irrelevant articles had been removed. Full-text screening was conducted initially by two investigators (BM and MJ) who assessed the eligibility of the studies independently. Further multiple rounds of full-text reviews were performed by four investigators (BM, SE, MJ, and FH) to ensure that studies directly relevant to the objectives were included in this review. Disagreements were resolved by consensus via meetings and discussions.

Data charting process and data items

An extraction sheet was designed to tabulate data from the included articles using a Microsoft Excel ® spreadsheet. The extracted data included: (1) article information (title, author(s), year of publication, and journal name), (2) setting information (setting, organization name, whether the organization was public or private, and country), (3) research information (objective, design, HPEP), (4) theory information (name of the theory, context of application, description of how the theory was applied, the outcomes assessed, and methods of analysis), (5) outcome information (number of participants involved, intervention provided, duration of intervention, overall outcome and recommendations, and reported limitations related to the theory), and (6) the applicability to other disciplines. The context of the SToLs application includes teaching and learning (strategies used to deliver and receive educational content in clinical or non-clinical settings, etc.), curriculum development (learning objectives, planning of teaching strategies, program evaluation, etc.), or assessment (development, validation, and administration of assessment activities, etc.). The data extraction sheet was piloted by two investigators (BM and MJ) using four sample articles included in this review. Based on successful piloting, complete data extraction was done by four investigators (BM, FH, ME, and SE).

Critical appraisal of individual sources of evidence

The included studies were not evaluated for quality or critically appraised because of methodological heterogeneity among studies. However, this lack of quality evaluation and critical appraisal aligns with the general standards of scoping reviews ( 33 ).

Synthesis of results

Descriptive numeric analysis was used to summarize data retrieved from the included articles according to the proportion of (1) articles per discipline, (2) SToLs applied, and (3) contexts in which SToLs were used. Moreover, the analysis of the data involved conducting a narrative description of the included articles by two independent investigators (MJ and FH). Consensus was reached on the basis of the analyzed data.

Out of 5,303 articles retrieved from databases, 247 were duplicates and hence removed ( Figure 1 ). Following the title and abstract screening of 5,056 articles, 310 articles were eligible for full-text screening. Primary reasons for exclusion include: article types other than primary research literature (e.g., review articles, description of a theory, editorial letters, commentaries, protocols), thesis or dissertations, articles that described the use of theories other than SToLs, articles that did not implement SToLs or did not implement them in undergraduate or postgraduate education (e.g., implemented them for faculty development), articles that focused on other professions and not on health professions, and articles that used SToLs for data analysis purposes. Other reasons for exclusion included manually detected duplicates. A total of nine articles were qualified for inclusion and were used to inform this scoping review.

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Prisma flow diagram.

Characteristics of included studies

Of studies published between 2013 and 2019, two studies were conducted in the USA ( 34 , 35 ), three in Australia ( 36 – 38 ), and one study in each of these countries: Sweden ( 39 ), Canada ( 40 ), Scotland ( 41 ), and Italy ( 42 ). A total of five studies used Bandura's SLTs ( 34 , 36 – 38 , 40 ), while four used Lave and Wenger's CoP theory ( 35 , 39 , 41 , 42 ). Three studies used a qualitative research methodology ( 39 – 41 ), two studies used quantitative research methodology ( 37 , 42 ), and three studies used a mixed-method design ( 35 , 36 , 38 ). The remaining study, educational innovation, focused on describing the implementation of a teaching strategy ( 34 ). A total of five studies used SToLs in nursing programs ( 34 , 38 , 40 – 42 ), one in medicine ( 39 ), one in pharmacy ( 37 ), and two were multi-disciplinary, including: paramedicine, psychology, nutrition and dietetics, nursing, public health, medicine, and other HPEPs ( 35 , 36 ). Seven studies used SToLs in teaching and learning ( 34 , 36 – 39 , 41 , 42 ) , one in assessment ( 40 ), and one in curriculum design ( 35 ). The included studies covered a total of 1,780 participants (i.e., undergraduate students, residents, clinical teachers, and healthcare professionals) ( Table 1 ).

Characteristics of the included studies.

CoP, Community of Practice; HCP, Healthcare professionals; HPE, Health professions education; SCT, Social cognitive theory; SLT, Social learning theory; SRL, Self-regulated learning; NCSES, Nursing Competence Self-Efficacy Scale; NE, Nutrition Educator; NPT, Near peer teaching; PPR, Pharmacy practice research; RN, Registered nurses .

Bandura's SLTs

Five studies in this scoping review focused on utilizing Bandura's SLTs in the teaching, learning, and assessment of health professions students ( 34 , 36 – 38 , 40 ). The use of Bandura's SLTs in the included studies suggested its advantages in improving students' self-efficacy and confidence, collaborative learning, learning experiences and future teaching experience and career research intentions.

In 1977, Bandura proposed an SLT based on a series of human behavioral studies ( 24 ). According to Bandura, learning takes place in social settings and occurs not only through an individual's own experiences, but by observing the actions of others and their consequences ( 24 , 43 ). Social learning is also referred to as observational learning because learning takes place as a result of observing others (i.e., models), which Bandura's previous studies demonstrated as a valuable strategy for acquiring new behaviors ( 44 ). Bandura and his colleagues continued to demonstrate modeling/observational learning as a very efficient method of learning ( 44 ). Bandura's theorizing of the social development process later incorporated motivational and cognitive processes into SLT ( 44 ). In 1986, Bandura renamed his original SLT to SCT to emphasize the critical role that cognition plays in encoding and the performance of activities ( 44 , 45 ). SCT suggests that learning occurs in a social context with a dynamic and reciprocal interaction of the person, environment, and behavior ( 25 ). The core constructs of SCT include modeling/observational learning, outcome expectancies, self-efficacy and self-regulation ( 25 , 44 ). Bandura's observational learning consists of four stages: (1) attention: learners see the behavior they want to reproduce, (2) retention: learners retain the behavior they have seen entailing a cognitive process in which learners mentally rehearse the behavior they wish to replicate, (3) reproduction: learners put the processes obtained in attention and retention into action, and (4) motivation: learners imitate the observed behavior through reinforcement (direct, vicarious or self-reinforcement).

Based on Bandura's argument that human behavior is learnt via interactions with, and modeling of others in social contexts, Carroll et al. ( 36 ) applied the four stages of observational learning to investigate the effectiveness of GoSoapBox, a student response system (SRS). The study proved the effectiveness of this online tool in stimulating discussions on controversial topics, improving learning experiences and in-class engagement among paramedic, psychology, nutrition and dietetics, nursing, and public health students.

Carter et al. ( 37 ) focused on the self-efficacy, outcome expectancy and social influence components of SCT to develop and test a model that evaluates undergraduate pharmacy students' intentions to pursue a higher pharmacy practice research (PPR) degree. The authors suggest that educators must provide links between practice and research and increase student self-confidence to undertake PPR, thereby increasing interest in this as a future career path. This is because exposure alone has minimal influence on a student's interest in PPR as a career.

Irvine et al. ( 38 ) explored self-regulated learning (SRL), a learning model situated in SCT, strategies utilized by final year nursing students in both their approaches to learning and practical teaching sessions (peer-teaching). The study findings support the use of SRL in nursing education, as highlighted by the high level of motivational behaviors and learning strategies reported among undergraduate nursing students in their approach to learning and their roles as peer-teachers.

Kennedy et al. ( 40 ) used the construct of self-efficacy to develop and psychometrically assess a scale that examines undergraduate nursing students' self-efficacy practice competence, assist educators in determining the level of education that students receive, as well as their level of confidence and advocacy for positive changes.

Furthermore, Koo et al. ( 34 ) indicated that implementing a self-efficacy construct to develop a formative standardized patient experience allowed nursing students to develop the concepts of inter-professional collaborative communication, and enhanced their problem-solving and communication skills, as well as their clinical competency.

Lave and Wenger's theory: Communities of practice (CoP)

The CoP theory consists of three key components: the domain (the common interest among all members), the practice (the implicit and explicit knowledge shared), and the community (made up of mutually beneficial interactions between experts and learners leading to learning, engagement, and identity development) ( 10 , 46 – 48 ). All the articles retrieved in this review described a CoP as a group of people who share similar characteristics and collaborate toward a common goal, therefore enhancing mutual learning through sharing relevant knowledge and fostering the development of a shared identity. Three of the studies implemented CoP theory with a focus on teaching and learning among health professions students, and one with a focus on HPEP curricula design. All studies indicated that implementing the CoP learning theory enhanced student learning, collaboration, and identity.

Alsio et al. ( 39 ) found that when CoP theory was used to create teams of practicing nurses, physicians, and undergraduate medical students with the mandate of developing learning activities during their clinical placements, learning was stimulated through self-reflection and consideration of their perspectives during patient interactions. Further, inter-professional reflection was vital for successful introduction of new students into a CoP and was effective for structural and cultural development. Moreover, staff and students' awareness of their roles and responsibilities facilitated their motivation to participate in the CoPs implementation.

Similarly, Molesworth et al. ( 41 ) and Protoghese et al. ( 42 ) explored the experiences of undergraduate nursing students regarding their application of the CoP theory during clinical placements. Both studies argued that CoP helped students to integrate their theoretical learning of bioscience into practice ( 41 ), and to advance their existing clinical knowledge ( 42 ). Moreover, application of bioscience knowledge within a CoP facilitated effective inter-professional relationships ( 41 ). Additionally, students perceived that they received more respect, support, and feedback while learning within a CoP ( 42 ). This further emphasizes the significance of mutual engagement and the collaborative relationship component of the CoP theory in enhancing student learning ( 42 ).

Furthermore, Chen et al. ( 35 ) used CoP theory in a curricular design for the HPEP aimed at helping undergraduate medical students, residents, fellows, and learners from other HPE schools to develop their identities as future health professions educators. The program has demonstrated its effectiveness in providing learners with the knowledge and skills to realize their career aspirations. It also enhanced learners' enthusiasm for teaching and increased their interest in educational leadership, innovation, and research.

This scoping review attempted to provide an overview of how SToLs have been used in the teaching and learning of HPEPs over the last decade. This review highlighted some interesting findings that, collectively, may provide insights into how educational practices in HPEPs are shaped and influenced by learning theories.

Bandura's SLTs were applied predominantly in teaching and instruction strategies within the HPEPs. This review demonstrated the application of Bandura's observational learning model in the form of in-class integrated collaborative learning activities through an online tool for improving learning experiences and engagement ( 36 ). It is argued that observational learning provides a faster and safer approach to learning complicated patterns of behavior than trial and error, making it consistent with and suitable for HPE ( 7 , 49 ). Self-efficacy, defined by an individuals' assessment of their capacity to perform given tasks or activities and achieve specified goals ( 50 ), was the most highlighted construct in the included articles. This can be explained by Bandura's argument that self-efficacy is central to social learning because it significantly impacts a wide range of human endeavors, including developmental and health psychology, education, and in the workplace ( 19 ). The findings suggest that the self-efficacy construct is beneficial to the learning outcome, particularly in simulation contexts, as demonstrated in the review conducted by Lavoie et al. ( 51 ). This aligns with previous literature about the self-efficacy construct indicating that individuals with stronger self-efficacy for certain tasks are more motivated to execute them ( 50 , 52 ). Furthermore, the self-efficacy construct was used to develop an assessment tool that evaluates students competence and confidence level and advocacy for positive changes as they become professional nursing practitioners ( 40 ). In this context, it is worth mentioning that assessment tools based on self-efficacy found in previous health-related literature are task-specific ( 53 , 54 ). Previous literature has also argued that feelings of confidence among medical students are associated with competence and proficiency ( 55 , 56 ), and lack of confidence leads to nurses leaving the profession ( 57 ). Moreover, clinical educators' self-efficacy and confidence are critical to their ability to carry out their teaching and training responsibilities as they affect student achievement and patient outcomes ( 58 ).

Lave and Wenger's CoP theory

In this review, CoP theory was mainly employed in the teaching and learning of health professions students, educators, and providers to improve learning, collaboration, and identity. However, as highlighted by Hörberg et al. ( 59 ), it would be better used to identify team challenges and provide more meaningful interventions. It is noteworthy that none of the included studies highlighted any long-term benefits of CoP, aligning with Allen et al.'s ( 60 ) argument that there is a paucity of health professions studies exploring the long-term effect of CoP on individuals and the relevance to educational outcomes. Additionally, several studies in healthcare education and practice indicated the scarcity of studies that focus on the development and assessment of CoPs ( 10 , 61 , 62 ).

This review highlights a scarcity of research focusing on the application of SToLs in the development, validation, and conduction of assessment activities within HPEPs. Only one study used the self-efficacy construct to develop a tool for assessing student competence ( 40 ). This is consistent with a recent literature review suggesting that SToLs are not applied in performing assessment activities compared to other learning theories, such as humanistic theories or motivational models ( 13 ). This is despite evidence of the utility of CoP learning theory in planning and implementing effective assessment measures in the PharmD program ( 20 ).

The current review suggests that the application of SToLs in designing HPEPs' curricular content, learning objectives, syllabus or influencing educational competencies is also not common. In this regard, Mukhalalati and Taylor proposed a novel CoP theory-informed framework that can be used in designing a new HPEP to reduce the disconnect between the educational practice and learning theories ( 10 ). The authors suggest key components to consider when developing a CoP-based curriculum, including but not limited to, complementing formal with informal learning, transferring tacit knowledge to explicit knowledge through socialization and externalization, re-contextualizing knowledge, and aligning students' learning needs to learning activities ( 10 ). These components are compatible with several SToLs and claimed to be applicable in various HPEPs ( 10 ).

An important observation in this review was the exclusion of a large number of retrieved articles because they failed to inform how SToLs are implemented in the educational practices and in delivering educational goals ( 63 ), or because they aimed to use SToLs as a lens to explore HPEPs teaching and learning practices, or as a theoretical framework to conceptualize or analyze HPE research data ( 64 – 66 ). This aligns with previous research that highlighted the significance of using theories to enhance research rigor and its relevant outcomes ( 67 ). However, it is suggested to use learning theories to critique HPE and guide its advancement initiatives ( 68 , 69 ). Furthermore, several excluded studies utilized SToLs for healthcare professionals continuing professional development ( 70 – 75 ), which seems to be a common application of SToLs. Although examining SToLs utilization in continuing professional development activities was not the aim of conducting this review, this aspect is extremely important as it indirectly influences students who will ultimately become health care professionals. Collectively, the small number of included eligible studies in this review that applied SToLs in HPEPs suggests disconnect between SToLs and HPEPs educational practices. It is argued that it is challenging for HPEPs educators to apply the educational theories because they received minimal or no educational training about their significance and implementation ( 5 ). Therefore, as recommended by previous research, a collaborative reform initiative should be enacted to enhance the optimal use of SToLs in educational practice and examine the applicability and usefulness of other theories of learning in HPEP ( 20 ). Moreover, this review did not include studies from Africa, Eastern Mediterranean, and South-East Asia, suggesting that exploratory and experimental educational research utilizing various learning theories are highly warranted in these regions.

Strengths and limitations

This review explored SToLs use in HPEPs and provided a valuable overview for educators in a broad range of health education fields. Studies included were conducted in various countries which further enhanced the results' applicability to other contexts. However, a number of limitations should be acknowledged when interpreting the findings of this review. For example, this review was limited to only four databases and to the last decade, potentially missing relevant articles in other major databases such as Scopus and Web of Science and those published before 2011. Moreover, as is inherent to scoping reviews, a quality assessment for the included articles was not conducted necessitating caution in interpreting conclusions. Additionally, since SToLs can be categorized and named differently, this might inadvertently result in the omission of relevant articles.

This review provides an overview of the application of SToLs in HPEPs from 2011 to 2020. Only two SToLs were identified in this review: Bandura's SLT and SCT; and Lave and Wenger's CoP theory. Bandura's four-stage model of observational learning, as well as self-efficacy construct, were applied in the included studies. CoP theory was mainly employed to improve learning, collaboration, and identity, whilst SToLs use was predominantly focused on teaching and learning with less focus on assessment and curriculum design. This review demonstrated a limited number of HPEPs applying and reporting an application of SToLs despite the significance of the social aspect of learning concepts in those theories and within HPEP. This suggests a potential disconnect between SToLs and HPEP educational practices. Nonetheless, this review illustrated the successful and effective implementation of StoLs in various HPEPs, which is applicable to other HPEPs. Finally, this review supports the call for collaborative reform initiatives to optimize the use of StoLs in HPEPs educational practices. Future research should focus on the applicability and usefulness of other theories of learning in HPEP and investigate the long-term outcomes of theory implementation.

Data availability statement

Author contributions.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Open Access funding is provided by the Qatar National Library.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2022.912751/full#supplementary-material

research paper on the learning theory

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Learning Styles Debunked

By Med Kharbach, PhD | Last Update: May 9, 2024

research paper on the learning theory

Every time I broach the topic of learning styles theory here, I receive a flurry of negative feedback from teachers. Many label the theory as obsolete and thoroughly debunked. Prompted by this reaction, I decided to delve deeper into what the research actually says. It turns out that while there’s a growing body of research that cautions against the uncritical application of learning styles theory, it’s not completely dismissed as irrelevant. To shed more light on this, I’ve taken the time to synthesize some of the main research critiques for you.

For a refresher of what the learning styles theory is all about, check out the full overview in Learning Styles Theory: Strengths and Weaknesses!

For convenience, I divided it into into three main parts:

1. Quick Scan : For those who prefer a brief glance, this section summarizes the key points of various papers in just a few sentences.

3. Detailed Summaries: If you’re interested in a more comprehensive understanding, section two offers a detailed summary of each critique.

1. My Take : If you’re looking for a quick overview of my findings in the research, check out this section. I’ve distilled the essence of the research into an easily digestible format.

I hope this structure helps you navigate through the information efficiently and gain a clearer understanding of the current stance on learning styles theory.

I. Quick Scan of the Research Debunking Learning Styles Theory

Here is a quick overview of the main points covered in each featured research paper:

An & Carr (2017):

  • Critique of the lack of a clear explanatory framework in learning styles theory.
  • Problems with measurement and linking learning styles to academic achievement.
  • Suggestion to focus on cognitive and developmental psychology for understanding individual learning differences.

Kirschner (2017):

  • Argument against tailoring education to preferred learning styles like visual, auditory, or kinesthetic.
  • Emphasis on the difference between preferred learning methods and effective learning.
  • Lack of scientific validity and support for learning styles theory.

Knoll et al. (2017):

  • Investigation of the impact of learning styles on learning performance and Judgments of Learning (JOLs).
  • Finding that learning styles may influence subjective perceptions but not objective learning outcomes.

Newton & Miah (2017):

  • Challenge to the effectiveness of learning styles in education.
  • Highlighting the lack of empirical evidence supporting the theory.
  • Discussion on the potential negative impacts of adhering to learning styles theory.

Pashler (2009):

  • Critical review of the idea that tailored teaching improves educational outcomes.
  • Lack of substantial scientific evidence supporting learning-styles-based instruction.
  • Recommendation against incorporating learning styles assessments in education.

Riener & Willingham (2010):

  • Assertion that there is no credible evidence for the existence of learning styles.
  • Suggestion to understand and apply knowledge about student differences in classroom.
  • Attribution of the popularity of learning styles theory to confirmation bias and “common knowledge.”

Rohrer & Pashler (2012):

  • Critique of the widespread acceptance of learning styles without empirical scrutiny.
  • Questioning the practicality and legitimacy of style-based instruction.
  • Conclusion on the lack of empirical justification for tailoring instruction to different learning styles.

Scott (2010):

  • Discussion on how the search for individual differences in education led to the popularization of learning styles.
  • Argument that learning styles theory continues due to cultural alignment, not effectiveness.
  • Emphasis on the opportunity costs of adhering to the learning styles myth.

Williamson & Watson (2007):

  • Exploration of the significance of learning styles in education.
  • Focus on how a student’s personality correlates with their preferred learning style.
  • Caution against using learning style theories to judge students’ intelligence or abilities.

Willingham et al. (2015):

  • Examination of the scientific status of learning styles theories.
  • Pointing out the lack of belief in the accuracy of learning styles theories due to absent scientific support.
  • Highlighting the difficulty in proving a theory definitively wrong but emphasizing the need for evidence to support classroom application.

research paper on the learning theory

II. Summary of the Research Criticizing Learning Styles Theory

Here are summaries of each of some of featured research papers critiquing learning styles theory:

1. An, D., & Carr, M. (2017). Learning Styles Theory Fails to Explain Learning and Achievement: Recommendations for Alternative Approaches. The paper “Learning Styles Theory Fails” critiques the traditional concept of learning styles by highlighting its three major flaws: the lack of a clear explanatory framework, problems with measurement, and the failure to link learning styles to actual academic achievement. The authors propose alternative approaches to understanding individual differences in learning, suggesting a focus on factors such as verbal and visual skills, domain knowledge, self-regulation, and perfectionism. These alternative approaches, rooted in cognitive and developmental psychology, aim to provide a more effective framework for predicting and explaining individual learning differences.

The paper argues that learning styles theories are ineffective in explaining the causes of individual differences in student learning and that teaching based on these styles does not result in improved learning. In fact, it often leads to hindered development and poor achievement, as this approach fails to address the learners’ weaknesses.

The authors suggest that a better understanding of individual learning differences can be achieved through cognitive and developmental theories, as well as temperament and personality theories. They recommend that educators focus on these theories, rather than on learning styles, to better cater to the individual differences they observe in their classrooms.

2. Kirschner, P. A. (2017). Stop propagating the learning styles myth In this article, Kirschner argues against the common belief that educational approaches should be tailored to students’ preferred learning styles, such as visual, auditory, or kinesthetic. He highlights major issues with this notion, emphasizing that there’s a significant difference between someone’s preferred way of learning and what actually leads to effective learning. According to the author, the concept of learning styles, often based on categorizing people into distinct groups, lacks support from objective studies and fails to meet key criteria for scientific validity.

The author also urges educators and researchers to stop endorsing the learning styles myth, as it is not grounded in solid evidence. Kirschner concludes that the premise of different learning styles requiring different instructional methods is more of a belief than a proven fact, with little to no scientific backing.

The author further explains that there are fundamental problems with measuring learning styles, and the theoretical basis for the interaction between learning styles and instructional methods is weak. Furthermore, significant empirical evidence supporting the learning-styles hypothesis is almost nonexistent. The concept of learning styles, as the paper confirms, is so vaguely defined that it becomes ineffective for instructional purposes, leading some to adhere to it merely for convenience rather than educational efficacy.

3. Knoll, A. R., Otani, H., Skeel, R. L., & Van Horn, K. R. (2017). Learning style, judgements of learning, and learning of verbal and visual information. This study investigates the impact of learning styles on learning performance, focusing on the relationship between learning styles and Judgments of Learning (JOLs). Participants were assessed for their preference for verbal or visual information and then studied and recalled word and picture pairs while making JOLs.

The results indicated that while preferences for certain types of information were linked to higher immediate JOLs, there was no significant correlation between these preferences and actual recall performance or JOL accuracy, suggesting that learning styles might influence subjective perceptions of learning but not the objective outcomes.

Further analysis in the study explored immediate, delayed, and global JOLs in relation to learning styles. Immediate JOLs were influenced by learning style preferences, reflecting the processing fluency hypothesis (ease of processing influences JOLs) and the beliefs hypothesis (beliefs about learning effectiveness guide JOLs).

However, learning styles showed no significant relation to delayed or global JOLs, which are more reflective of actual learning performance. The paper concludes while learning styles may affect initial perceptions about learning, they do not significantly impact the deeper aspects of learning and memory retrieval.

4. Newton, P. M., & Miah, M. (2017). Evidence-Based Higher Education – Is the Learning Styles ‘Myth’ Important? This pape challenges the effectiveness of learning styles in education. It points out a recurring theme in the critique of learning styles theory which is lack of empirical evidence supporting the idea that matching teaching methods to students’ learning styles enhances learning outcomes. Despite its popularity among educators, the concept, as the authors stated, is labeled a “myth” due to its failure in empirical validation.

Additionally, the paper discusses the potential negative impacts of adhering to the learning styles theory. These include limiting students to specific learning categories, misusing educational resources, and damaging the credibility of educational research. The authors’ survey of UK academics revealed a significant belief in learning styles, but also an acknowledgment of its theoretical flaws. This , as the authors contend, highlights the challenge in changing educational practices based on deeply entrenched beliefs, despite contrary evidence.

5. Pashler. (2009). Learning Styles: Concepts and Evidence The paper “Learning Styles: Concepts and Evidence” critically reviews the idea that educational outcomes improve when teaching is tailored to individual learning styles, such as visual or verbal preferences. Despite the popularity of this concept in education, the authors find no substantial scientific evidence to support the effectiveness of learning-styles-based instruction. They argue that credible validation would require specific experimental findings demonstrating that students learn better when taught according to their preferred learning style.

Their literature review reveals very few studies that meet the necessary criteria to test this theory, and those that do, including a notable study by Sternberg et al., show methodological weaknesses and inconclusive results. Other studies with appropriate designs contradict the popular hypothesis that teaching methods should match individual learning styles.

Consequently, the authors recommend against incorporating learning styles assessments into general educational practice, advising a focus on other educational practices with a stronger evidence base.

6. Riener, C., & Willingham, D. (2010). The Myth of Learning Styles. In the “The Myth of Learning Styles”, Reiner and Willingham assert that there is no credible evidence supporting the existence of learning styles. While acknowledging that learners are indeed different from each other and these differences affect their performance, the authors further explain that these variations do not validate the concept of learning styles. Instead, it suggests that understanding and applying knowledge about student differences in the classroom can improve education. The authors also emphasize that a belief in learning styles is unnecessary for incorporating effective teaching strategies.

According to the authors, the widespread acceptance of learning styles theory is attributed to it becoming “common knowledge,” reinforced by confirmation bias—where people seek information that supports their beliefs while ignoring contrary evidence. This cognitive phenomenon leads to misconceptions about learning preferences and their impact on education.

The article also highlights the opportunity costs of adhering to the learning styles myth, suggesting that educators should instead focus on research in cognitive science and education that offers insights into effective learning strategies. The authors caution that focusing on learning styles, for which there is no solid evidence, may cause educators to overlook scientifically supported research on learning.

7. Rohrer, D., & Pashler, H. (2012). Learning styles: where’s the evidence? The paper “Learning Styles: Where’s the Evidence?” by Doug Rohrer and Harold Pashler critically examines the widespread acceptance of learning styles in educational practices. The authors argue that, unlike evidence-based treatments in modern medicine, most instructional techniques, including learning styles, have not undergone thorough empirical scrutiny. Despite the popularity and profitability of tailoring instruction to students’ supposed learning styles, such as visual or verbal preferences, the authors assert that a comprehensive review of existing data does not support the efficacy of style-based instruction.

The paper highlights that only a few studies have employed the appropriate design to test the effectiveness of style-based instruction, and most of these have resulted in negative findings. The authors question the practicality of style-based instruction, considering its logistical demands and costs, and suggest that the perceived legitimacy of this concept may be more illusory, based on superficial similarities to true observations that do not logically support style-based instruction.

Ultimately, the paper concludes that there is no empirical justification for tailoring instruction to different learning styles and advises educators to focus on developing more effective and cohesive methods of presenting content.

8. Scott, C. (2010). The Enduring Appeal of “Learning Styles.” The paper “The Enduring Appeal of ‘Learning Styles'” discusses how the search for individual differences in education, driven by Western individualism, has led to the popularization of learning styles. Despite extensive research over four decades, there’s no evidence that learning styles can guide effective teaching practices. The theory, according to the author, continues to thrive, not because of its effectiveness, but due to its alignment with cultural values, even though it may perpetuate harmful stereotypes and ineffective teaching methods.

As the Scot states, the interest in individual differences for pedagogical decision-making dates back to the 1960s, but research has consistently failed to support the effectiveness of personalizing teaching based on these differences. While research has identified effective general principles of teaching and learning, these evidence-based practices lack the appeal and simplicity of the learning styles theory.

The paper argues that the continuous promotion of learning styles theory wastes valuable teaching time, promotes damaging stereotypes, and hinders the adoption of evidence-based teaching practices. It emphasizes that learning styles have no place in education if it aims to be scientifically grounded.

9. Williamson, M. F., & Watson, R. L. (2007). Learning Styles Research: Understanding how Teaching Should be Impacted by the Way Learners Learn Part III: Understanding how Learners’ Personality Styles Impact Learning. This paper explores the significance of learning styles in education from the perspectives of both instructors and students. It particularly focuses on how a student’s personality correlates with their preferred learning style, discussing the implications for Christian education contexts.

The paper emphasizes that learning style theory can be instrumental in enhancing the educational process, aiding students to become effective learners and lifelong learners. It suggests that these theories can help educators identify their strengths and weaknesses and align their teaching methods with the diverse learning styles of students.

However, the paper cautions against using learning style theory to judge students’ intelligence or abilities or to label learners, as this could lead to predetermined expectations about student success. Instead, it advises educators to choose and apply the learning style theory that resonates most with them, continually adapting and improving their teaching methods to cater to different types of learners.

10. Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The Scientific Status of Learning Styles Theories. “The Scientific Status of Learning Styles Theories” critically examines the concept of learning styles, which suggests that individuals have specific preferences for processing information that affect their learning. The paper points out that while many believe in the accuracy of learning styles theories, scientific support for these theories is notably absent. The authors argue that educators would be better served by focusing their time and energy on other theories that have a more solid foundation in aiding instruction.

According to the authors, decades of literature reviews have consistently found no viable evidence to support the theory of learning styles. These reviews have highlighted the unreliability of most instruments used to identify learners’ styles. The paper underscores the difficulty in proving a negative—that a theory is definitively wrong—but emphasizes that for a theory to influence classroom practice, it must be supported by evidence. In the case of learning styles, as the authors contend, there needs to be evidence not only of their existence but also that teaching to these styles benefits students in some way.

III. My Take

Reflecting on the critiques of learning styles theory, it’s clear that the concept, although popular, lacks empirical support and practical effectiveness. you can tell from reading the summaries that a recurring theme across almost all of these studies is the lack of empirical evidence to support the effectiveness of learning styles theory.

As a former teacher and current educational researcher, I’ve always been intrigued by the different ways students learn. However, these studies, spanning several decades, consistently highlight the absence of solid scientific evidence supporting the effectiveness of tailoring teaching to specific learning styles, like visual or auditory preferences. The idea that individual preferences in learning translate into more effective educational outcomes has not been substantiated by credible research.

What’s particularly striking is the emphasis on the difference between preferred learning methods and those that actually lead to effective learning. These critiques suggest that while students may have preferences, these do not necessarily correlate with better learning outcomes.

In fact, focusing too much on these supposed learning styles can lead to stereotyping and ineffective teaching strategies, which ultimately hinder the learning process. It’s also noteworthy how learning styles has become embedded in educational discourse, more because of its intuitive appeal and alignment with cultural values, rather than its empirical validity.

Given this, I believe that as educators, we should shift our focus towards more evidence-based teaching practices that consider the diverse needs and capabilities of learners without confining them to rigid categories. Understanding individual differences in abilities and intelligences, and how these can be leveraged in a learning environment, seems to be a more fruitful approach than adhering to the unproven learning styles theory. This shift not only aligns with the scientific evidence but also supports a more inclusive and effective educational practice.

In conclusion, the dive into the research on learning styles theory reveals a nuanced picture. While there’s substantial criticism and evidence challenging the efficacy and scientific basis of this theory, it’s not wholly discarded in educational discourse. The key takeaway is that educators should approach the concept of learning styles with a critical eye and avoid relying on it as the sole framework for designing educational experiences. Instead, it’s advisable to integrate a variety of evidence-based teaching strategies that acknowledge the diverse needs and abilities of students. This balanced approach can lead to more inclusive and effective educational practices, moving beyond the confines of a single, possibly outdated theory. As educators, our goal should always be to adapt and evolve our methods in line with the best available evidence to provide the highest quality education to our students

  • An, D., & Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116, 410–416. https://doi.org/10.1016/j.paid.2017.04.050
  • Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers and Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
  • Knoll, A. R., Otani, H., Skeel, R. L., & Van Horn, K. R. (2017). Learning style, judgements of learning, and learning of verbal and visual information. The British Journal of Psychology, 108(3), 544–563. https://doi.org/10.1111/bjop.12214
  • Newton, P. M., & Miah, M. (2017). Evidence-Based Higher Education – Is the Learning Styles ‘Myth’ Important? Frontiers in Psychology, 8, 444–444. https://doi.org/10.3389/fpsyg.2017.00444
  • Pashler. (2009). Learning Styles: Concepts and Evidence. Geological Society of America Bulletin, 9(3), 105–119. https://doi.org/info:doi/
  • RIENER, C., & WILLINGHAM, D. (2010). THE MYTH OF LEARNING STYLES. Change, 42(5), 32–35. http://www.jstor.org/stable/25742629
  • Rohrer, D., & Pashler, H. (2012). Learning styles: where’s the evidence? Medical Education, 46(7), 634–635. https://doi.org/10.1111/j.1365-2923.2012.04273.x
  • Scott, C. (2010). The enduring appeal of “learning styles.” The Australian Journal of Education, 54(1), 5–17. https://doi.org/10.1177/000494411005400102
  • Williamson, M. F., & Watson, R. L. (2007). Learning Styles Research: Understanding how Teaching Should be Impacted by the Way Learners Learn Part III: Understanding how Learners’ Personality Styles Impact Learning. Christian Education Journal, 4(1), 62–77. https://doi.org/10.1177/073989130700400105
  • Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The Scientific Status of Learning Styles Theories. Teaching of Psychology, 42(3), 266–271. https://doi.org/10.1177/0098628315589505

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Dr. Med Kharbach is an influential voice in the global educational technology landscape, with an extensive background in educational studies and a decade-long experience as a K-12 teacher. Holding a Ph.D. from Mount Saint Vincent University in Halifax, Canada, he brings a unique perspective to the educational world by integrating his profound academic knowledge with his hands-on teaching experience. Dr. Kharbach's academic pursuits encompass curriculum studies, discourse analysis, language learning/teaching, language and identity, emerging literacies, educational technology, and research methodologies. His work has been presented at numerous national and international conferences and published in various esteemed academic journals.

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

A close-up of a woman's hand writing in a notebook.

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

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In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

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"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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Using ideas from game theory to improve the reliability of language models

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A digital illustration featuring two stylized figures engaged in a conversation over a tabletop board game.

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Imagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend's job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions about the clues you've given. The challenge is that both of you want to make sure you're understanding each other correctly and agreeing on the secret message.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have created a similar "game" to help improve how AI understands and generates text. It is known as a “consensus game” and it involves two parts of an AI system — one part tries to generate sentences (like giving clues), and the other part tries to understand and evaluate those sentences (like guessing the secret message).

The researchers discovered that by treating this interaction as a game, where both parts of the AI work together under specific rules to agree on the right message, they could significantly improve the AI's ability to give correct and coherent answers to questions. They tested this new game-like approach on a variety of tasks, such as reading comprehension, solving math problems, and carrying on conversations, and found that it helped the AI perform better across the board.

Traditionally, large language models answer one of two ways: generating answers directly from the model (generative querying) or using the model to score a set of predefined answers (discriminative querying), which can lead to differing and sometimes incompatible results. With the generative approach, "Who is the president of the United States?" might yield a straightforward answer like "Joe Biden." However, a discriminative query could incorrectly dispute this fact when evaluating the same answer, such as "Barack Obama."

So, how do we reconcile mutually incompatible scoring procedures to achieve coherent, efficient predictions? 

"Imagine a new way to help language models understand and generate text, like a game. We've developed a training-free, game-theoretic method that treats the whole process as a complex game of clues and signals, where a generator tries to send the right message to a discriminator using natural language. Instead of chess pieces, they're using words and sentences," says Athul Jacob, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate. "Our way to navigate this game is finding the 'approximate equilibria,' leading to a new decoding algorithm called 'equilibrium ranking.' It's a pretty exciting demonstration of how bringing game-theoretic strategies into the mix can tackle some big challenges in making language models more reliable and consistent."

When tested across many tasks, like reading comprehension, commonsense reasoning, math problem-solving, and dialogue, the team's algorithm consistently improved how well these models performed. Using the ER algorithm with the LLaMA-7B model even outshone the results from much larger models. "Given that they are already competitive, that people have been working on it for a while, but the level of improvements we saw being able to outperform a model that's 10 times the size was a pleasant surprise," says Jacob. 

"Diplomacy," a strategic board game set in pre-World War I Europe, where players negotiate alliances, betray friends, and conquer territories without the use of dice — relying purely on skill, strategy, and interpersonal manipulation — recently had a second coming. In November 2022, computer scientists, including Jacob, developed “Cicero,” an AI agent that achieves human-level capabilities in the mixed-motive seven-player game, which requires the same aforementioned skills, but with natural language. The math behind this partially inspired the Consensus Game. 

While the history of AI agents long predates when OpenAI's software entered the chat in November 2022, it's well documented that they can still cosplay as your well-meaning, yet pathological friend. 

The consensus game system reaches equilibrium as an agreement, ensuring accuracy and fidelity to the model's original insights. To achieve this, the method iteratively adjusts the interactions between the generative and discriminative components until they reach a consensus on an answer that accurately reflects reality and aligns with their initial beliefs. This approach effectively bridges the gap between the two querying methods. 

In practice, implementing the consensus game approach to language model querying, especially for question-answering tasks, does involve significant computational challenges. For example, when using datasets like MMLU, which have thousands of questions and multiple-choice answers, the model must apply the mechanism to each query. Then, it must reach a consensus between the generative and discriminative components for every question and its possible answers. 

The system did struggle with a grade school right of passage: math word problems. It couldn't generate wrong answers, which is a critical component of understanding the process of coming up with the right one. 

“The last few years have seen really impressive progress in both strategic decision-making and language generation from AI systems, but we’re just starting to figure out how to put the two together. Equilibrium ranking is a first step in this direction, but I think there’s a lot we’ll be able to do to scale this up to more complex problems,” says Jacob.   

An avenue of future work involves enhancing the base model by integrating the outputs of the current method. This is particularly promising since it can yield more factual and consistent answers across various tasks, including factuality and open-ended generation. The potential for such a method to significantly improve the base model's performance is high, which could result in more reliable and factual outputs from ChatGPT and similar language models that people use daily. 

"Even though modern language models, such as ChatGPT and Gemini, have led to solving various tasks through chat interfaces, the statistical decoding process that generates a response from such models has remained unchanged for decades," says Google Research Scientist Ahmad Beirami, who was not involved in the work. "The proposal by the MIT researchers is an innovative game-theoretic framework for decoding from language models through solving the equilibrium of a consensus game. The significant performance gains reported in the research paper are promising, opening the door to a potential paradigm shift in language model decoding that may fuel a flurry of new applications."

Jacob wrote the paper with MIT-IBM Watson Lab researcher Yikang Shen and MIT Department of Electrical Engineering and Computer Science assistant professors Gabriele Farina and Jacob Andreas, who is also a CSAIL member. They presented their work at the International Conference on Learning Representations (ICLR) earlier this month, where it was highlighted as a "spotlight paper." The research also received a “best paper award” at the NeurIPS R0-FoMo Workshop in December 2023.

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MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine . “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis. 

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Computer Science > Machine Learning

Title: improving graph machine learning performance through feature augmentation based on network control theory.

Abstract: Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how certain patterns of external control measures can steer system dynamics towards desired states. Distinguished from other structure-function methodologies, NCT's predictive capabilities can be coupled with deploying Graph Neural Networks (GNNs), which have demonstrated exceptional utility in various network-based learning tasks. However, the performance of GNNs heavily relies on the expressiveness of node features, and the lack of node features can greatly degrade their performance. Furthermore, many real-world systems may lack node-level information, posing a challenge for this http URL tackle this challenge, we introduce a novel approach, NCT-based Enhanced Feature Augmentation (NCT-EFA), that assimilates average controllability, along with other centrality indices, into the feature augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA, on six benchmark GNN models across two experimental setting. solely employing average controllability and in combination with additional centrality metrics. showcases an improved performance reaching as high as 11%. Our results demonstrate that incorporating NCT into feature enrichment can substantively extend the applicability and heighten the performance of GNNs in scenarios where node-level information is unavailable.

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