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Students tend to avoid effective but effortful study strategies. One potential explanation could be that high-effort experiences may not give students an immediate feeling of learning, which may affect their perceptions of the strategy's effectiveness and their willingness to use it. In two experiments, we investigated the role of mental effort in students' considerations about a typically effortful and effective strategy (interleaved study) versus a typically less effortful and less effective strategy (blocked study), and investigated the effect of individual feedback about students' study experiences and learning outcomes on their considerations. Participants learned painting styles using both blocked and interleaved studying (within-subjects, Experiment 1, N = 150) or either blocked or interleaved studying (between-subjects, Experiment 2, N = 299), and reported their study experiences and considerations before, during, and after studying. Both experiments confirmed prior research that students reported higher effort investment and made lower judgments of learning during interleaved than during blocked studying. Furthermore, effort was negatively related to students' judgments of learning and (via these judgments) to the perceived effectiveness of the strategy and their willingness to use it. Interestingly, these relations were stronger in Experiment 1 than in Experiment 2, suggesting that effort might become a more influential cue when students can directly compare experiences with two strategies. Feedback positively affected students' considerations about interleaved studying, yet not to the extent that they considered it more effective and desirable than blocked studying. Our results provide evidence that students use effort as a cue for their study strategy decisions.

Background: During self-study, students need to monitor and regulate mental effort to replete working memory resources and optimize learning results. Taking breaks during self-study could be an effective effort regulation strategy. However, little is known about how breaktaking relates to self-regulated learning. Aims: We investigated the effects of taking systematic or self-regulated breaks on mental effort, task experiences and task completion in real-life study sessions for 1 day. Sample: Eighty-seven bachelor's and master's students from a Dutch University. Methods: Students participated in an online intervention during their self-study. In the self-regulated-break condition (n = 35), students self-decided when to take a break; in the systematic break conditions, students took either a 6-min break after every 24-min study block (systematic-long or 'Pomodoro technique', n = 25) or a 3-min break after every 12-min study block (systematic-short, n = 27). Results: Students had longer study sessions and breaks when self-regulating. This was associated with higher levels of fatigue and distractedness, and lower levels of concentration and motivation compared to those in the systematic conditions. We found no difference between groups in invested mental effort or task completion. Conclusions: Taking pre-determined, systematic breaks during a study session had mood benefits and appeared to have efficiency benefits (i.e., similar task completion in shorter time) over taking self-regulated breaks. Measuring how mental effort dynamically fluctuates over time and how effort spent on the learning task differs from effort spent on regulating break-taking requires further research.

Desirable difficulties are learning conditions that are often experienced as effortful, but have a positive effect on learning results and transfer of knowledge and skills (Bjork & Bjork, 2011; Bjork, 1994). Learners often do not appreciate the beneficial effects of desirable difficulties, and the negative experiences of high effort and perceived low learning make them resistant to engage in desirable difficulties (Biwer et al., 2020a). This ultimately limits learning outcomes and academic achievement. With the increasing emphasis on self-regulation in education, characterized by higher learner agency and abundant choices in what, when, and how to study, the field of educational psychology is in need of theoretical and empirically testable assumptions that improve self-regulation in desirably difficult learning conditions with the aim to foster self-regulation abilities, learning outcomes, and academic achievement. Here, we present a framework that describes how to support self-regulation of effort when engaging in desirable difficulties: the "Start and Stick to Desirable Difficulties (S2D2)" framework. The framework builds on the Effort Monitoring and Regulation model (de Bruin et al., 2020). The aim of this framework is (1) to describe evidence for the central role of perceived effort and perceived learning in (dis)engagement in desirable difficulties, and (2) to review evidence on, and provide an agenda for research to improve learners' self-regulated use of desirable difficulties to help them start and persist when learning feels tough, but is actually effective.

Recent research shows the importance to teach students the self-regulated use of effective learning strategies at university. However, the effects of such training programs on students' metacognitive knowledge, use of learning strategies, and academic performance in the longer term are unknown. In the present study, all first-year pharmacology students from one university attended a learning strategy training program, i.e., the 'Study Smart program', in their first weeks. The 20% (n = 25) lowest scoring students on the first midterm received further support regarding their learning strategies. Results showed that all students gained accurate metacognitive knowledge about (in)effective learning strategies in the short- and long-term and reported to use less highlighting, less rereading, but more interleaving, elaboration, and distributed practice after the training program. Academic performance was compared to the prior cohort, which had not received the Study Smart program. While in the previous cohort, students in the top, middle, and bottom rank of midterm 1 stayed in these ranks and still differed significantly in the final exam, students in the Study Smart cohort that received the training program improved throughout the year and differences between ranks were significantly reduced. A learning strategy training program including a remediation track for lower performing students can thus support students to study more effectively and enhance equal chances for all students at university.

In higher education, many students make poor learning strategy decisions. This, in part, results from the counterintuitive nature of effective learning strategies: they enhance long-term learning but also cost high initial effort and appear to not improve learning (immediately). This mixed-method study investigated how students make learning strategy decisions in category learning, and whether students can be supported to make effective strategy decisions through a metacognitive prompt, designed to support accurate monitoring of effort and learning. Participants (N = 150) studied painting styles through blocked and interleaved practice, rated their perceived effort and perceived learning across time, and chose between either blocked or interleaved practice. Half of the participants (N = 74) were provided with a metacognitive prompt that showed them how their subjective experiences per strategy changed across time and required them to relate these experiences to the efficacy of learning strategies. Results indicated that subjective experiences with interleaved practice improved across time: students' perceived learning increased as their perceived effort decreased. Mediation analysis revealed that the increased feeling of learning increased the likelihood to select interleaved practice. The percentage of students who chose interleaved practice increased from 13 to 40%. Students' learning strategy decisions, however, did not benefit from the metacognitive prompt. Qualitative results revealed that students initially had inaccurate beliefs about the efficacy of learning strategies, but on-task experiences overrode the influence of prior beliefs in learning strategy decisions. This study suggests that repeated monitoring of effort and learning have the potential to improve the use of interleaved practice.

The testing effect--the power of retrieval practice to enhance long-term knowledge retention more than restudying does--is a well-known phenomenon in learning. However, retrieval practice is hardly appreciated by students and underutilized when studying. One of the reasons is that learners usually do not experience immediate benefits of such practice which often present only after a delay. We therefore conducted 2 experiments to examine whether students choose retrieval practice more often as their learning strategy after having experienced its benefits. In Experiment 1, students received individual feedback about the extent to which their 7-day delayed test scores after retrieval practice differed from their test scores after restudy. Those students who had actually experienced the benefits of retrieval practice appreciated the strategy more and used it more often after receiving feedback. In Experiment 2, we compared the short-term and long-term effects on retrieval practice use of individual performance feedback and general instruction about the testing effect. Although both interventions enhanced its use in the short term, only the individual feedback led to enhanced use in the long term by those who had actually experienced its benefits, demonstrating the superiority of the individual feedback in terms of its ability to promote retrieval practice use.

Functional neuroimaging is a useful approach to study the neural correlates of visual perceptual expertise. The purpose of this paper is to review the functional-neuroimaging methods that have been implemented in previous research in this context. First, we will discuss research questions typically addressed in visual expertise research. Second, we will describe which kinds of stimuli are employed and which functional-neuroimaging techniques are implemented in this kind of research, with a special focus on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Third, we will summarize the outcomes of recent studies that addressed the neural correlates of visual expertise and will particularly focus on studies that examined the neural correlates of visual expertise in medical image diagnosis. Finally, the review closes with a discussion of the benefits, caveats, and future directions of cognitive-neuroscience research for studying visual expertise.

When compared to high performers, low performers generally have more difficulty to accurately estimate their own performance. This has been explained by low performers being both unskilled and unaware about their performance. However, Miller and Geraci "Journal of Experimental Psychology: Learning, Memory, and Cognition," 37(2), 502-506, (2011) found that low performing university students also assigned less confidence to their estimates (i.e., second-order judgments, SOJs), indicating some metacognitive awareness of their poor calibration. The current study examined whether the relationship between calibration accuracy and confidence in performance estimates is more general, and exists irrespective of performance level, not only for university students but also for secondary school students. We asked university students and secondary school students to estimate their exam grade after taking their exam, and to provide a second-order judgement). The results showed that for university students, poor calibration accuracy was indeed accompanied by low confidence scores, independent from performance level. For secondary school students however, calibration accuracy was unrelated to confidence scores, suggesting a less developed metacognitive awareness.

An exponential increase in the availability of information over the last two decades has asked for novel theoretical frameworks to examine how students optimally learn under these new learning conditions, given the limitations of human processing ability. In this special issue and in the current editorial introduction, we argue that such a novel theoretical framework should integrate (aspects of) cognitive load theory and self-regulated learning theory. We describe the effort monitoring and regulation (EMR) framework, which outlines how monitoring and regulation of effort are neglected but essential aspects of self-regulated learning. Moreover, the EMR framework emphasizes the importance of optimizing cognitive load during self-regulated learning by reducing the unnecessary load on the primary task or distributing load optimally between the primary learning task and metacognitive aspects of the learning task. Three directions for future research that derive from the EMR framework and that are discussed in this editorial introduction are: (1) How do students monitor effort? (2) How do students regulate effort? and (3) How do we optimize cognitive load during self-regulated learning tasks (during and after the primary task)? Finally, the contributions to the current special issue are introduced. [This article was written with EFG-MRE.]

For (facilitating) effective learning from texts, students and teachers need to accurately monitor students' comprehension. Monitoring judgments are accurate when they correspond to students' actual comprehension. Accurate monitoring enables accurate (self-)regulation of the learning process, i.e., making study decisions that are in line with monitoring judgments and/or students' comprehension. Yet, (self-)monitoring accuracy is often poor as the information or cues used are not always diagnostic (i.e., predictive) for students' actual comprehension. Having students engage in generative activities making diagnostic cues available improves monitoring and regulation accuracy. In this review, we focus on generative activities in which text is transformed into visual representations using mapping and drawing (i.e., making diagrams, concept maps, or drawings). This has been shown to improve monitoring and regulation accuracy and is suited for studying cue diagnosticity and cue utilization. First, we review and synthesize findings of studies regarding (1) students' monitoring accuracy, regulation accuracy, learning, cue diagnosticity, and cue utilization; (2) teachers' monitoring and regulation accuracy and cue utilization; and (3) how mapping and drawing affect using effort as a cue during monitoring and regulation, and how this affects monitoring and regulation accuracy. Then, we show how this research offers unique opportunities for future research on advancing measurements of cue diagnosticity and cue utilization and on how effort is used as a cue during monitoring and regulation. Improving measures of cue diagnosticity and cue utilization can provide us with more insight into how students and teachers monitor and regulate students' learning, to help design effective interventions to foster these important skills.

Research has shown a bi-directional association between the (perceived) amount of invested effort to learn or retrieve information (e.g., time, mental effort) and metacognitive monitoring judgments. The direction of this association likely depends on how learners allocate their effort. In self-paced learning, effort allocation is usually data driven, where the ease of memorizing is used as a cue, resulting in a negative correlation between effort and monitoring judgments. Effort allocation is goal driven when it is strategically invested (e.g., based on the importance of items or time pressure) and likely results in a positive correlation. The current study used a meta-analytic approach to synthesize the results from several studies on the relationship between effort and monitoring judgments. The results showed that there was a negative association between effort and monitoring judgments (r = - 0.355). Furthermore, an exploration of possible moderators of this association between effort and monitoring was made. The negative association was no longer significant when goal-driven regulation was manipulated. Furthermore, it was found that the type of monitoring judgment (i.e., a weaker association for prospective judgments) and type of task (stronger association for problem-solving tasks relative to paired associates) moderated the relation between effort and monitoring. These results have important implications for future research on the use of effort as a cue for monitoring in self-regulated learning.

Extensive research has been conducted to understand how accurately students monitor their studying and performance via metacognitive judgments. Moreover, the bases of students' metacognitive judgments are of interest. While previous results are quite consistent regarding the importance of performance for the accuracy of metacognitive judgments, results regarding motivational and personality variables are rather heterogeneous. This paper reports on two studies that simultaneously examined the predictive power of several performance, motivational, and personality variables on metacognitive judgments. The studies investigated a set of judgments (local and global postdictions in Study 1 and global pre- and postdictions in Study 2) and accuracy scores (bias, sensitivity, and specificity) in two different settings. Individual differences in judgments and judgment accuracy were studied via hierarchical regression analyses. Study 1 with N = 245 undergraduate students identified performance and domain-specific self-concept as relevant predictors for judgments after test taking. This was consistently found for local and global judgments. Study 2 with N = 138 undergraduate students hence focused on domain-specific self-concept and extended results to predictions. Study 2 replicated results for global postdictions but not predictions. Specifically, before task processing, students' judgments relied mostly on domain-specific self-concept but not on test performance itself. The studies indicate that different judgments and measures of judgment accuracy are needed to obtain comprehensive insights into individual differences in metacognitive monitoring.

Learning to take safe and effective action in complex settings rife with uncertainty is essential for patient safety and quality care. Doing so is not easy for trainees, as they often consider certainty to be a necessary precursor for action and subsequently struggle in these settings. Understanding how skillful clinicians work comfortably when uncertain, therefore, offers an important opportunity to facilitate trainees' clinical reasoning development. This critical review aims to define and elaborate the concept of 'comfort with uncertainty' in clinical settings by juxtaposing a variety of frameworks and theories in ways that generate more deliberate ways of thinking about, and researching, this phenomenon. We used Google Scholar to identify theoretical concepts and findings relevant to the topics of 'uncertainty,' 'ambiguity,' 'comfort,' and 'confidence,' and then used preliminary findings to pursue parallel searches within the social cognition, cognition, sociology, sociocultural, philosophy of medicine, and medical education literatures. We treat uncertainty as representing the lived experience of individuals, reflecting the lack of confidence one feels that he/she has an incomplete mental representation of a particular problem. Comfort, in contrast, references confidence in one's capabilities to act (or not act) in a safe and effective manner given the situation. Clinicians' 'comfort with uncertainty' is informed by a variety of perceptual, emotional, and situational cues, and is enabled through a combination of self-monitoring and forward planning. Potential implications of using 'comfort with uncertainty' as a framework for educational and research programs are explored.

Although self-regulated learning (SRL) is becoming increasingly important in modern educational contexts, disagreements exist regarding its measurement. One particularly important issue is whether self-reports represent valid ways to measure this process. Several researchers have advocated the use of behavioral indicators of SRL instead. An outstanding research debate concerns the extent to which it is possible to compare behavioral measures of SRL to traditional ways of measuring SRL using self-report questionnaire data, and which of these methods provides the most valid and reliable indicator of SRL. The current review investigates this question. It was found that granularity is an important concept in the comparison of SRL measurements, influencing the degree to which students can accurately report on their use of SRL strategies. The results show that self-report questionnaires may give a relatively accurate insight into students' global level of self-regulation, giving them their own value in educational research and remediation. In contrast, when students are asked to report on specific SRL strategies, behavioral measures give a more accurate account. First and foremost, researchers and practitioners must have a clear idea about their research question or problem statement, before choosing or combining either form of measurement.

We investigated intra-individual monitoring and regulation in learning from text in sixth-grade students and their teachers. In Experiment 1, students provided judgments of learning (JOLs) for six texts in one of three cue-prompt conditions (after writing delayed keywords or summaries or without a cue prompt) and then selected texts for restudy. Teachers also judged their students' learning for each text, while seeing -- if present -- the keywords or summaries each student had written for each text, and also selected texts for restudy. Overall, monitoring accuracy was low (0.10 for students, -0.02 for teachers) and did not differ between cue-prompt conditions. Regulation, indexed by the correlation between JOLs and restudy selections, was significant (-0.38 for students, -0.60 for teachers), but was also not affected by cue-prompt condition. In Experiment 2, teachers judged students' comprehension of six texts without knowing the students' names, so that only the keywords and summaries, not prior impressions, could inform judgments. Again, monitoring accuracy was generally low (0.06), but higher for keywords (0.23) than for summaries (-0.10). These results suggest that monitoring intra-individual differences in students' learning is challenging for teachers. Analyses of the diagnosticity and utilization of keywords suggest that these may contain insufficient cues for improving teacher judgments at this level of specificity.

Advances in computational language models increasingly enable adaptive support for self‐regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves). We compared the accuracy of four Dutch language models (ie, spaCy medium, spaCy large, FastText and ConceptNet NumberBatch) in the context of secondary school students' learning of causal relations from expository texts, scaffolded by causal diagram completion. Since machine learning relies on human‐labelled data for the best results, we used a dataset with 10,193 students' causal diagram answers, compiled over a decade of research using a diagram completion intervention to enhance students' monitoring of their text comprehension. The language models were used in combination with four popular machine learning classifiers (ie, logistic regression, random forests, support vector machine and neural networks) to evaluate their performance on automatically scoring students' causal diagrams in terms of the correctness of events and their sequence (ie, the causal structure). Five performance metrics were studied, namely accuracy, precision, recall, F1 and the area under the curve of the receiver operating characteristic (ROC‐AUC). The spaCy medium model combined with the neural network classifier achieved the best performance for the correctness of causal events in four of the five metrics, while the ConceptNet NumberBatch model worked best for the correctness of the causal sequence. These evaluation results provide a criterion for model adoption to adaptively support SRL of causal relations in DLEs. Practitioner notesWhat is already known about this topic Accurate monitoring is a prerequisite for effective self‐regulation.Students struggle to accurately monitor their comprehension of causal relations in texts.Completing causal diagrams improves students' monitoring accuracy, but there is room for further improvement.Automatic scoring could be used to provide adaptive support during diagramming.What this paper adds Comparison of four Dutch word vector models combined with four machine learning classifiers for the automatic scoring of students' causal diagrams.Five performance metrics to evaluate the above solutions.Evaluation of the word vector models for estimating the semantic similarity between student and model answers.Implications for practice and/or policy High‐quality word vector models could (em)power adaptive support during causal diagramming via automatic scoring.The evaluated solutions can be embedded in digital learning environments (DLEs).Criteria for model adoption to adaptively support SRL of causal relations in DLEs.The increased saliency of (in)correct answers via automatic scoring might help to improve students' monitoring accuracy. [ABSTRACT FROM AUTHOR]

The think-aloud method is an established technique for studying human thought (cognitive) processes. Problem-solving and decision-making are essential skills for medical professionals, and the cognitive processes underlying these skills are complex. Studying these thought processes would enable educators, clinicians, and researchers to modify or refine their approaches and interventions. The think-aloud method has been utilized for capturing cognitive processes in a variety of fields, including computer usability, sports and cognitive psychology. Medical education also recognizes thought processes as valuable data for research and education. This article aims to guide researchers and educators through the preparation and implementation of a think-aloud method to record participants' thought processes during an activity. [ABSTRACT FROM AUTHOR]

The current study investigated kindergarteners and second graders' ability to monitor and evaluate their own and a virtual peer's performance in a paired-associate learning task. Participants provided confidence judgments (CJs) for their own responses and performance-based judgments (judgments provided "after" receiving feedback on their performance) for both their own and a virtual peer's responses. For the performance-based judgments, children were confronted with their own or the peer's answer as well as the correct answer. Additionally, participants were asked to credit their own and the peer's correct and incorrect answers while facing feedback. Results indicate an age-related progression in metacognitive monitoring skills, with second graders differentiating more strongly in their confidence judgments between correct and incorrect responses compared to kindergarteners. Regarding performance-based judgments, children of both age groups provided higher judgments for correctly compared to incorrectly recognized items as well as for their own responses in comparison to the responses of the unknown child. Similarly, when crediting, participants of both age groups gave more credits for correct recognition than for incorrect recognition and for their own responses than for the peer's responses. The significant interaction between age group and recognition accuracy for the crediting shows that second graders gave more credits for correctly recognized items while kindergarteners gave more credits for incorrect answers than the older children--primarily for their "own" incorrect answers. In conclusion, the study provides new insights into 6- and 8-year-olds' evaluations of their own and an unknown child's performance in a paired-associate learning task by showing that children of both age groups generally judged and credited responses in their own favor. These results add to our understanding of biases in children's performance evaluations, including metacognitive judgments and judgments provided after receiving feedback.

Purpose: Learning is optimised when postgraduate trainees engage in clinical tasks in their zone of proximal development (ZPD). However, workplace learning environments impose additional non‐learning goals and additional tasks that may lead to trainees engaging in tasks that do not fall within their ZPD. We do not fully understand how trainees select clinical tasks in the workplace learning environment. If we knew the goals and factors they consider when selecting a task, we could better equip trainees with strategies to select tasks that maximise learning. We explored how postgraduate trainees select clinical tasks using echocardiography interpretation as a model. Methods: Canadian General Cardiology residents and Echocardiography fellows were invited to participate in semi‐structured interviews. Aligning with a theory‐informed study, two independent researchers used a deductive, directed content analysis approach to identify codes and themes. Results: Eleven trainees from seven Canadian universities participated (PGY4 = 4, PGY5 = 3, PGY6 = 1 and echocardiography fellows = 3). Goals included learning content, fulfilling assessment criteria and contributing to clinical demands. Trainees switched between goals throughout the day, as it was too effortful for them to engage in tasks within their ZPD at all times. When trainees had sufficient mental effort available, they selected higher complexity tasks that could advance learning content. When available mental effort was low, trainees selected less complex tasks that fulfilled numerically based assessment goals or contributed to clinical demands. Trainees predominantly used perceived complexity of the echocardiogram as a factor to select tasks to achieve their desired goals. Conclusion: Postgraduate trainees select tasks within their ZPD that enable them to maximise learning when they perceive to have sufficient mental effort available and workplace affordances are adequate. These findings can inform individual and systemic strategies to maximise learning when selecting tasks. Furthering understanding of workplace‐based learning, Blissett et al. demonstrate how trainees take on low complexity tasks to allow mental effort to recharge, thus facilitating maximal engagement with tasks within their Zone of Proximal Development. [ABSTRACT FROM AUTHOR]

Being overconfident when estimating scores for an upcoming exam is a widespread phenomenon in higher education and presents threats to self-regulated learning and academic performance. The present study sought to investigate how overconfidence and poor monitoring accuracy vary over the length of a college course, and how an intervention consisting of (1) a monitoring exercise and (2) a monitoring and regulation strategy, improves students' monitoring accuracy and academic performance. Moreover, we investigated how personality factors (i.e., grandiose and vulnerable narcissism, optimism) influence monitoring accuracy. We found that the Monitoring and Regulation Strategy positively influenced monitoring accuracy and exam scores, whereas the Monitoring Exercise that confronted students with their overconfidence protected students against overconfidence in the second exam score prediction but did not affect exam score. The results further revealed that exam score predictions lowered from the start to the end of the course for both poor and high performing students, but still leaving poor performers overconfident and high performers underconfident. Topic knowledge gained in the course did not wash out the Dunning Kruger effect, and results indicate that poor and high performers use different cues when predicting exam scores. Both grandiose and vulnerable narcissism contributed to overconfidence on exam score predictions but not on the Monitoring Exercise. These findings underline the potential of the Monitoring and Regulation Strategy intervention and ask for upscaling it to include measurements of self-regulated learning activities.

Monitoring accuracy, measured by judgements of learning (JOLs), has generally been found to be low to moderate, with students often displaying overconfidence, and JOLs of problem solving are no exception. Recently, primary school children's overconfidence was shown to diminish when they practised problem solving after studying worked examples. The current study aimed to extend this research by investigating whether practising problem solving after worked example study would also improve JOL accuracy in secondary education. Adolescents of 14-15 years old (N = 143) were randomly assigned to one of five conditions that differed in timing of JOLs, whether practice problems were provided, and timing of the practice problems provided: (1) worked examples--JOL, (2) worked examples--delay--JOL, (3) worked examples--practice problems--JOL, (4) worked examples--practice problems--delay--JOL or (5) worked examples--delay--practice problems--JOLs. Results showed that practice problems improved absolute accuracy of JOLs as well as regulation accuracy. No differences in final test performance were found.