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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.

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.]

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.

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.

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]

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]

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.

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.

Since emergence of the field "Educational Neuroscience" (EN) in the late nineties of the previous century, a debate has emerged about the potential this field holds to influence teaching and learning in the classroom. By now, most agree that the original claims promising direct translations to teaching and learning were too strong. I argue here that research questions in (health professions) education require multi-methodological approaches, including neuroscience, while carefully weighing what (combination of) approaches are most suitable given a research question. Only through a multi-methodological approach will convergence of evidence emerge, which is so desperately needed for improving teaching and learning in the classroom. However, both researchers and teachers should become aware of the so-called "seductive allure" of EN; that is, the demonstrable physical location and apparent objectivity of the measurements can be interpreted as yielding more powerful evidence and warranting stronger conclusions than, e.g., behavioral experiments, where in fact oftentimes the reverse is the case. I conclude that our tendency as researchers to commit ourselves to one methodological approach and to addressing educational research questions from a single methodological perspective is limiting progress in educational science and in translation to education.

To prevent radiologists from overlooking lesions, radiology textbooks recommend "systematic viewing," a technique whereby anatomical areas are inspected in a fixed order. This would ensure complete inspection (full coverage) of the image and, in turn, improve diagnostic performance. To test this assumption, two experiments were performed. Both experiments investigated the relationship between systematic viewing, coverage, and diagnostic performance. Additionally, the first investigated whether systematic viewing increases with expertise; the second investigated whether novices benefit from full-coverage or systematic viewing training. In Experiment 1, 11 students, ten residents, and nine radiologists inspected five chest radiographs. Experiment 2 had 75 students undergo a training in either systematic, full-coverage (without being systematic) or non-systematic viewing. Eye movements and diagnostic performance were measured throughout both experiments. In Experiment 1, no significant correlations were found between systematic viewing and coverage, r = -0.10, p = 0.62, and coverage and performance, r = -0.06, p = 0.74. Experts were significantly more systematic than students F[subscript 2,25] = 4.35, p = 0.02. In Experiment 2, significant correlations were found between systematic viewing and coverage, r = -0.35, p < 0.01, but not between coverage and performance, r = 0.13, p = 0.31. Participants in the full-coverage training performed worse compared with both other groups, which did not differ between them, F[subscript 2,71] = 3.95, p = 0.02. In conclusion, the data question the assumption that systematic viewing leads to increased coverage, and, consequently, to improved performance. Experts inspected cases more systematically, but students did not benefit from systematic viewing training.

Making a diagnosis involves ratifying or verifying a proposed answer. Formalizing this verification process with checklists, which highlight key variables involved in the diagnostic decision, is often advocated. However, the mechanisms by which a checklist might allow clinicians to improve their verification process have not been well studied. We hypothesize that using a checklist to verify diagnostic decisions enhances analytic scrutiny of key variables, thereby improving clinicians' ability to find and fix mistakes. We asked 16 participants to verify their interpretation of 12 electrocardiograms, randomly assigning half to be verified with a checklist and half with an analytic prompt. While participants were verifying their interpretation, we tracked their eye movements. We analyzed these eye movements using a series of eye tracking variables theoretically linked to analytic scrutiny of key variables. We found that more errors were corrected using a checklist compared to an analytic prompt (0.27 ± 0.53 errors per ECG vs. 0.04 ± 0.43, F[subscript 1,15] = 8.1, p = 0.01, ?[superscript 2] = 0.20). Checklist use was associated with enhanced analytic scrutiny in all eye tracking measures assessed (F[subscript 6,10] = 6.0, p = 0.02). In this experiment, using a key variable checklist to verify diagnostic decisions improved error detection. This benefit was associated with enhanced analytic scrutiny of those key variables as measured by eye tracking.

Checklists that focus attention on key variables might allow clinicians to find and fix their mistakes. However, whether this approach can be applied to clinicians of varying degrees of expertise is unclear. Novice and expert clinicians vary in their predominant reasoning processes and in the types of errors they commit. We studied 44 clinicians with a range of electrocardiography (ECG) interpretation expertise: novice, intermediate and expert. Clinicians were asked to interpret 10 ECGs, self-report their predominant reasoning strategy and then check their interpretation with a checklist. We found that clinicians of all levels of expertise were able to use the checklist to find and fix mistakes. However, novice clinicians disproportionately benefited. Interestingly, while clinicians varied in their self-reported reasoning strategy, there was no relationship between reasoning strategy and checklist benefit.

When studying verbal materials, both adults and children are often poor at accurately monitoring their level of learning and regulating their subsequent restudy of materials, which leads to suboptimal test performance. The present experiment investigated how monitoring accuracy and regulation of study could be improved when learning idiomatic phrases. Elementary school children (fourth and sixth-graders) were instructed to predict their test performance by providing judgments of learning (JOLs). They provided JOLs immediately after studying each idiom, after studying all idioms, or after studying all idioms followed by generating sentences in which the idioms were used. Correlations between JOLs and test performance showed that delayed-JOLs and delayed-JOLs with sentence generation were more accurate than immediate JOLs. JOLs after sentence generation also improved regulation of study compared to delayed-JOLs only. Analyses of JOL reaction times suggest that delayed-JOLs led children to retrieve the literal meaning of the idiom, whereas JOLs after sentence generation led children to focus on connections between studied information, contextual information, and prior knowledge. Sentence generation presents a promising method to improve regulation of study, and thus idiom learning.

The study investigated whether activation of inaccurate prior knowledge before study contributes to primary-school children's commission errors and overconfidence in these errors when learning new concepts. Findings indicate that inaccurate prior knowledge affects children's learning and calibration. The level of children's judgments of learning for recall responses for which they would not receive credit was inappropriately high after activation of inaccurate prior knowledge. Moreover, results showed that activation of inaccurate prior knowledge was not only detrimental for monitoring judgments during learning, but also for calibration accuracy after test taking. When judging the quality of their recall responses on the posttest, children were more overconfident when they had activated inaccurate prior knowledge. Also, the children often discarded concepts from further study after activation of inaccurate prior knowledge. These results suggest that in order to improve self-regulated learning, it may be important to detect inaccuracies in children's prior knowledge. (Contains 5 tables and 3 figures.)

Comparison learning is a promising approach for learning complex real-life visual tasks. When medical students study radiological appearances of diseases, comparison of images showing diseases with images showing no abnormalities could help them learn to discriminate relevant, disease-related information. Medical students studied 12 diseases on chest x-ray images. They were randomly assigned to a group (n = 31) that compared radiographs of diseases with normal images and a group (n = 30) that only studied radiographs of diseases. On a visual diagnosis test, students who compared with normal images during study were better able to diagnose focal diseases (i.e., lesions at one location) than students who could not compare, but for the diagnosis of diffuse diseases (i.e., involving both lungs) there was no significant difference between groups. Results show that comparison with normal images made it easier to discriminate relevant information for focal diseases. (Contains 1 table and 4 figures.)

Anatomy education often consists of a combination of lectures and laboratory sessions, the latter frequently including surface anatomy. Studying surface anatomy enables students to elaborate on their knowledge of the cadaver's static anatomy by enabling the visualization of structures, especially those of the musculoskeletal system, move and function in a living human being. A recent development in "teaching methods" for surface anatomy is body painting, which several studies suggest increases both student motivation and knowledge acquisition. This article focuses on a "teaching approach" and is a translational contribution to existing literature. In line with best evidence medical education, the aim of this article is twofold: to briefly inform teachers about constructivist learning theory and elaborate on the principles of constructive, collaborative, contextual, and self-directed learning; and to provide teachers with an example of how to implement these learning principles to change the approach to teaching surface anatomy. Student evaluations of this new approach demonstrate that the application of these learning principles leads to higher student satisfaction. However, research suggests that even better results could be achieved by further adjustments in the application of contextual and self-directed learning principles. Successful implementation and guidance of peer physical examination is crucial for the described approach, but research shows that other options, like using life models, seem to work equally well. Future research on surface anatomy should focus on increasing the students' ability to apply anatomical knowledge and defining the setting in which certain teaching methods and approaches have a positive effect. (Contains 1 table and 3 figures.)

Clinicians are believed to use two predominant reasoning strategies: system 1 based pattern recognition, and system 2 based analytical reasoning. Balancing these cognitive reasoning strategies is widely believed to reduce diagnostic error. However, clinicians approach different problems with different reasoning strategies. This study explores whether clinicians have insight into their problem specific reasoning strategy, and whether this insight can be used to balance their reasoning and reduce diagnostic error. In Experiment 1, six medical residents interpreted eight ECGs and self-reported their predominant reasoning strategy using a four point scale (4S). Self-assessed reasoning strategy correlated with objective assessment by two clinical experts using a post hoc talk-aloud protocol (p = 0.69, p less than 0.0001). Reporting an analytic strategy was also associated with 40% longer interpretation times (p = 0.01). In Experiment 2, twenty-four residents were asked to reinterpret eight ECGs with instructions customized to their 4S. Half of the ECGs were reinterpreted with instructions to use the opposite reasoning strategy to that reported, and half with instructions to use the same reasoning strategy. ECG reinterpretation scores did not differ with potentiating compared to balancing reasoning instructions (F[subscript 1,888] = 0.22, p = 0.64). However, analytic instructions were associated with improved scores (F[subscript 1,188] = 15, p = 0.0001). These data suggest that clinicians are able to recognize their reasoning strategies. However, attempting to balance reasoning strategies through customizable instructions did not result in a reduction in diagnostic errors. This suggests important limitations to the widespread belief in balancing reasoning strategies to reduce diagnostic error.

Although there is abundant experimental metamemory research on the relation between students' monitoring, regulation of learning, and learning outcomes, relatively little of this work has influenced educational research and practice. Metamemory research, traditionally based on experimental paradigms from cognitive psychology, can potentially contribute to designing and improving educational interventions that foster self-monitoring and self-regulation in children, adolescents, and young adult learners. We describe the metamemory paradigm, and provide a short overview of the insights it has generated with regard to improving metacognitive skills in these groups of learners. Moreover, we summarize the contributions to this special issue on translating insights from cognitive psychology research on metamemory to educational research and practice, and describe possible themes and directions for future research that could further bridge the gap between fundamental and more applied research on metacognition, so as to design effective educational interventions. (Contains 1 figure.)

We tested an embodied account of language proposing that comprehenders create perceptual simulations of the events they hear and read about. In Experiment 1, children (ages 7-13 years) performed a picture verification task. Each picture was preceded by a prerecorded spoken sentence describing an entity whose shape or orientation matched or mismatched the depicted object. Responses were faster for matching pictures, suggesting that participants had formed perceptual-like situation models of the sentences. The advantage for matching pictures did not increase with age. Experiment 2 extended these findings to the domain of written language. Participants (ages 7-10 years) of high and low word reading ability verified pictures after reading sentences aloud. The results suggest that even when reading is effortful, children construct a perceptual simulation of the described events. We propose that perceptual simulation plays a more central role in developing language comprehension than was previously thought. (Contains 4 tables.)

The ability to monitor understanding of texts, usually referred to as metacomprehension accuracy, is typically quite poor in adult learners; however, recently interventions have been developed to improve accuracy. In two experiments, we evaluated whether generating delayed keywords prior to judging comprehension improved metacomprehension accuracy for children. For sixth and seventh graders, metacomprehension accuracy was greater when generating keywords. By contrast, for fourth graders, metacomprehension accuracy did not differ across conditions. Improved metacomprehension accuracy led to improved regulation of study. The delayed keyword effect in children reported here is discussed in terms of situation model activation.

The present study was designed to test the effect of self-explanation and prediction on the development of principled understanding of novices learning to play chess. First-year psychology students, who had no chess experience, first learned the basic rules of chess and were afterwards divided in three conditions. They either observed (control condition), predicted, or predicted and self-explained the moves of the computer playing a chess endgame of King and Rook against King. Finally, in the test phase, participants had to play the endgame against the computer and were required to checkmate the opponent King. Apart from their test performance, the conditions were compared on quality of move predictions in the learning phase. The self-explanation condition showed better understanding of the endgame principles than the two other conditions, as indicated by the move predictions in the learning phase that more often exemplified correct application of chess principles. Moreover, participants in the self-explanation condition more often checkmated the black King in the test phase than participants in the two other conditions. However, no differences emerged between the prediction and observation condition. This study showed that, even for novices, providing self-explanations stimulates the discovery of domain principles of chess.

The present study was undertaken to examine the hypothesis that the intermediate effect in clinical case recall is partly explained by experts' lower motivation to write down "everything" they remember when asked for free recall. Medical experts and students were presented with two clinical cases, which they had to read, diagnose, and recall. Participants received an instruction before processing the cases that aimed at minimizing motivation in one condition, and enhance motivation in another. A third condition received a standard instruction, comparable to previous clinical case representation studies. The results showed that medical experts' clinical case processing mode is robust and insensitive to pressure induced by a social comparison instruction. In all conditions, recall data showed an intermediate effect, indicating encapsulated processing by the expert group. Moreover, there were no differences between the conditions in diagnostic accuracy, number of summaries in recall, and study time on the cases. Although experts showed that they were affected by the instruction, their processing mode remained stable over the three conditions. Expert physicians, even under conditions of considerable pressure, process clinical cases in an encapsulated mode.