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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.

Reading comprehension is a central skill in secondary education. To be able to provide adaptive instruction, teachers need to be able to accurately estimate students' reading comprehension. However, they tend to experience difficulties doing so. Eye tracking can uncover these reading processes by visualizing what a student looked at, in what order, and for how long, in a gaze display. The question is, however, whether teachers could interpret such displays. We, therefore, examined how teachers interpret gaze displays and perceived their potential use in education to foster tailored support for reading comprehension. Sixty teachers in secondary education were presented with three static gaze displays of students performing a reading comprehension task. Teachers were asked to report how they interpreted these gaze displays and what they considered to be the promises and pitfalls of gaze displays for education. Teachers interpreted in particular reading strategies in the gaze displays quite well, and also interpreted the displays as reflecting other concepts, such as motivation and concentration. Results showed that teachers' interpretations of the gaze displays were generally consistent across teachers and that teachers discriminated well between displays of different strategies. Teachers were generally positive about potential applications in educational practice. This study provides first insights into how teachers experience the utility of gaze displays as an innovative tool to support reading instruction, which is timely as rapid technological developments already enable eye tracking through webcams on regular laptops. Thus, using gaze displays in an educational setting seems to be an increasingly feasible scenario.

Eye movement modeling examples (EMMEs) are instructional videos (e.g., tutorials) that visualize another person's gaze location while they demonstrate how to perform a task. This systematic literature review provides a detailed overview of studies on the effects of EMME to foster observers' performance and learning and highlights their differences in EMME designs. Through a broad, systematic search on four relevant databases, we identified 72 EMME studies (78 experiments). First, we created an overview of the different study backgrounds. Studies most often taught tasks from the domains of sports/physical education, medicine, aviation, and STEM areas and had different rationales for displaying EMME. Next, we outlined how studies differed in terms of participant characteristics, task types, and the design of the EMME materials, which makes it hard to infer how these differences affect performance and learning. Third, we concluded that the vast majority of the experiments showed at least some positive effects of EMME during learning, on tests directly after learning, and tests after a delay. Finally, our results provide a first indication of which EMME characteristics may positively influence learning. Future research should start to more systematically examine the effects of specific EMME design choices for specific participant populations and task types.

Deliberate reflection has been found to foster diagnostic accuracy on complex cases or under circumstances that tend to induce cognitive bias. However, it is unclear whether the procedure can also be learned and thereby autonomously applied when diagnosing future cases without instructions to reflect. We investigated whether general practice residents would learn the deliberate reflection procedure through 'learning-by-teaching' and apply it to diagnose new cases. The study was a two-phase experiment. In the learning phase, 56 general-practice residents were randomly assigned to one of two conditions. They either (1) studied examples of deliberate reflection and then explained the procedure to a fictitious peer on video; or (2) solved cases without reflection (control). In the test phase, one to three weeks later, all participants diagnosed new cases while thinking aloud. The analysis of the test phase showed no significant differences between the conditions on any of the outcome measures (diagnostic accuracy, p = 0.263; time to diagnose, p = 0.598; mental effort ratings, p = 0.544; confidence ratings, p = 0.710; proportion of contradiction units (i.e. measure of deliberate reflection), p = 0.544). In contrast to findings on learning-by-teaching from other domains, teaching deliberate reflection to a fictitious peer, did not increase reflective reasoning when diagnosing future cases. Potential explanations that future research might address are that either residents in the experimental condition did not apply the learned deliberate reflection procedure in the test phase, or residents in the control condition also engaged in reflection.

In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still "with them" and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students' level of "with-me-ness" (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (1) gaze measures of "with-me-ness" are related to comprehension, (2) people not trained in eye-tracking can predict students' comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students' comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students' gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students' posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners' comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students' learning.

Multiple-choice questions (MCQs) are popular in vocabulary software because they can be scored automatically and are compatible with many input devices (e.g., touchscreens). Answering MCQs is beneficial for learning, especially when learners retrieve knowledge from memory to evaluate plausible answer alternatives. However, such retrieval may not always occur (e.g., with easy-to-guess answers). Therefore, we tested whether we could optimize MCQs for retrieval practice with a "stepwise display," which presents the question before the answer options. This creates an opportunity for cued recall. In an experimental classroom study (N = 75) and three online experiments with adult participants (N = 45, N = 77, and N = 79), participants practiced vocabulary with either standard MCQs or stepwise MCQs: In standard MCQs, a word was presented with three possible translations; in stepwise MCQs, it was shown for 4 s before the translations appeared. In three of the four experiments (Experiments 1, 3a, and 3b), stepwise MCQs enhanced retention significantly compared to standard MCQs, measured by a posttest several days after practice. Benefits of stepwise MCQs were found for different translation directions (Experiments 1 and 3) and both with easy-to-guess answers (noncompetitive answer options, Experiment 1) and hard-to-guess answers (competitive answer options, Experiment 3). However, the effect was most robust for questions with noncompetitive answer options (Experiments 1, 3a, and 3b) and for learners who reported trying to retrieve the answer from memory during the stepwise display (Experiments 1 and 3). Moreover, retention was generally enhanced by competitive, hard-to-guess answer options (Experiments 3a and 3b). Overall, a stepwise display is a promising and easy-to-implement manipulation to optimize MCQs for retrieval practice.

Background: Student perceptions of teachers' interpersonal closeness positively affect their emotions. If closeness is, however, effortful for the teacher (i.e., emotional labour, signalling less genuine closeness), this may undermine these positive effects. We tested this assumption by using student reports and external observations of teacher closeness and ambulant measures of teacher heart rate, to gauge teachers' physiological effort connected to being close during class. Aims: We investigated the association between teachers' physiological effort connected to closeness and students' lesson-focused emotions. Sample: 75 teachers and their students (N = 1645) participated during one real-life lesson. Methods: Teacher interpersonal closeness was continuously coded based on a video recording and teachers' heart rate was measured continuously as an indicator of physiological effort. Students reported their emotions and perception of teacher closeness at the end of the lesson. Results: Multilevel models with student emotions as DVs and students' perceptions of teacher warmth (L1 predictor) and teachers' physiological effort when being close (i.e., an intra-individual cross-correlation, L2 predictor) were tested. As expected, students reported more positive and less negative emotions when they perceived more teacher closeness. The physiological effort connected to being close was not directly associated with student emotions; however, such effort moderated the effect of perceived closeness, especially with regard to negative student emotions (i.e., cross-level interactions). The more effortful teacher closeness was, the less closeness protected students from negative emotions. Conclusions: In line with extant research on faking enjoyment and emotional labour, students seemed to be affected when teacher closeness was physiologically effortful, and overall positive effects of teacher closeness were undermined.

Background: Eye movement modelling examples (EMME) are demonstrations in which learners' not only see a model's (e.g., a teacher's) task performance on a computer screen (as in regular video examples) but also the model's eye movements (represented as moving coloured dots overlaid on the screen). Thereby EMME help guide learners' attention towards the relevant information and can model cognitive strategies which are otherwise unobservable for learners. Objectives: This study investigated whether EMME can help to learn deductive reasoning strategies and how the presence/absence of a teacher's verbal explanation affects learning from EMME. Methods: Secondary education students (N = 137) were randomly assigned to study video examples under one of four conditions in a 2 (EMME: yes/no) x 2 (verbal explanations: yes/no) between-subjects design. Results and Conclusions: Results revealed only a beneficial effect of the presence of verbal explanations on performance on the practice problems, but no pretest-to-posttest learning gains. Implications: Seeing the teacher's eye movements does not appear to enhance learning of deductive reasoning. The presence/absence of the teacher's verbal explanation does not seem to affect learning deductive reasoning.

Presenting novices with examples and problems is an effective and efficient way to acquire new problem-solving skills. Nowadays, examples and problems are increasingly presented in computer-based learning environments, in which learners often have to self-regulate their learning (i.e., choose what type of task to work on and when). Yet, it is questionable how novices self-regulate their learning from examples and problems, and to what extent their choices match with effective principles from instructional design research. In this study, 147 higher education students had to learn how to solve problems on the trapezoidal rule. During self-regulated learning, they were free to select six tasks from a database of 45 tasks that varied in task format (video examples, worked examples, practice problems), complexity level (level 1, 2, 3), and cover story. Almost all students started with (video) example study at the lowest complexity level. The number of examples selected gradually decreased and task complexity gradually increased during the learning phase. However, examples and lowest level tasks remained relatively popular throughout the entire learning phase. There was no relation between students' total score on how well their behavior matched with the instructional design principles and learning outcomes, mental effort, and motivational variables.

Teaching the contents of study materials by providing explanations to fellow students can be a beneficial instructional activity. A learning-by-teaching effect can also occur when students provide explanations to a real, remote, or even fictitious audience that cannot be interacted with. It is unclear, however, which underlying mechanisms drive "learning by non-interactive teaching effects" and why several recent studies did not replicate this effect. This literature review aims to shed light on "when" and "why" learning by non-interactive teaching works. First, we review the empirical literature to comment on the different mechanisms that have been proposed to explain why learning by non-interactive teaching may be effective. Second, we discuss the available evidence regarding potential boundary conditions of the non-interactive teaching effect. We then synthesize the available empirical evidence on processes and boundary conditions to provide a preliminary theoretical model of when and why non-interactive teaching is effective. Finally, based on our model of learning by non-interactive teaching, we outline several promising directions for future research and recommendations for educational practice.

Preparing students to become self-regulated learners has become an important goal of primary education. Therefore, it is important to investigate how we can improve self-monitoring and self-regulation accuracy in primary school students. Focusing on mathematics problems, we investigated whether and how (1) high- and low-performing students differed in their monitoring accuracy (i.e., extent to which students' monitoring judgments match their actual performance) and regulation accuracy (i.e., extent to which students' regulation judgments regarding the need for further instruction/practice match their actual need), (2) self-scoring improved students' monitoring and regulation accuracy, (3) high- and low-performing students differed in their monitoring and regulation accuracy after self-scoring, and (4) students' monitoring and regulation judgments are related. On two days, students of 9-10 years old from 34 classes solved multiplication and division problems and made monitoring and regulation judgments after each problem type. Next, they self-scored their answers and again made monitoring and regulation judgments. On the multiplication problems, high-performing students made more accurate monitoring and regulation judgments before and after self-scoring than low-performing students. On the division problems, high-performing students made more accurate monitoring judgments before self-scoring than low-performing students, but after self-scoring this difference was no longer present. Self-scoring improved students' monitoring and regulation accuracy, except for low- and high-performing students' regulation accuracy on division problems. Students' monitoring and regulation judgments were related. Our findings suggest that self-scoring may be a suitable tool to foster primary school students' monitoring accuracy and that this translates to some extent into more accurate regulation decisions.

Retrieval practice of isolated words (e.g., with flashcards) enhances foreign vocabulary learning. However, vocabulary is often encountered in context. We investigated whether retrieval opportunities also enhance contextualized word learning. In two within-subjects experiments, participants encoded 24 foreign words and then read a story to further strengthen word knowledge. The story contained eight target words in a "retrieval context," which required participants to recall word meaning from memory to understand the text (e.g., "She borrowed aknyga"), and eight target words in an "inference context" from which meaning could be "inferred" (e.g., "She read aknyga" [book]). After 1 to 2 days, a posttest measured word retention. Reading the words in either the retrieval or inference context increased retention, compared to control words not included in the story. Moreover, in Experiment 1, retention was significantly higher in the inference than in the retrieval condition. In Experiment 2, in which encoding before reading was more extensive and feedback was available, no differences in retention were found between the inference and retrieval + feedback condition (both increased retention, compared to control words). Overall, the findings suggest that the benefits of retrieval may be less pronounced during incidental, contextualized learning than during intentional exercises and that retrieval success must be considered when adding retrieval opportunities to contextualized learning. Under low retrieval success, the better comprehension afforded by an informative context may outweigh the benefits of retrieval opportunities (Exp.1). Yet even when retrieval success was enhanced and feedback was added (Exp. 2), retrieval opportunities were only as beneficial as exposure to rich contextual information.

When physicians do not estimate their diagnostic accuracy correctly, i.e. show inaccurate "diagnostic calibration," diagnostic errors or overtesting can occur. A previous study showed that physicians' diagnostic calibration for easy cases improved, after they received feedback on their previous diagnoses. We investigated whether diagnostic calibration would also improve from this feedback when cases were more difficult. Sixty-nine general-practice residents were randomly assigned to one of two conditions. In the feedback condition, they diagnosed a case, rated their confidence in their diagnosis, their invested mental effort, and case complexity, and then were shown the correct diagnosis (feedback). This was repeated for 12 cases. Participants in the control condition did the same without receiving feedback. We analysed calibration in terms of (1) absolute accuracy (absolute difference between diagnostic accuracy and confidence), and (2) bias (confidence minus diagnostic calibration). There was no difference between the conditions in the measurements of calibration (absolute accuracy, p = 0.204; bias, p = 0.176). Post-hoc analyses showed that on correctly diagnosed cases (on which participants are either accurate or underconfident), calibration in the feedback condition was less accurate than in the control condition, p = 0.013. This study shows that feedback on diagnostic performance did not improve physicians' calibration for more difficult cases. One explanation could be that participants were confronted with their mistakes and thereafter lowered their confidence ratings even if cases were diagnosed correctly. This shows how difficult it is to improve diagnostic calibration, which is important to prevent diagnostic errors or maltreatment.

Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions: (1) to what extent students' (novices) task selections align with instructional design principles; and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively 'safe' to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further improve self-regulated learning from examples and practice problems.

Productive Failure is an instructional approach in which students engage in problem-solving attempts prior to instruction. The Productive Failure approach has been found to be more supportive for conceptual knowledge acquisition during subsequent instruction compared to a direct-instruction condition in which students receive instruction first followed by problem solving. Failing at problem-solving attempts prior to instruction is considered to prepare students to better understand the concepts underlying the problem. However, it is an open question whether students need to fail themselves to be effectively prepared for subsequent instruction. We therefore compared the effectiveness of two alternative preparatory activities: Students either engaged in problem-solving attempts themselves or were shown problem-solving attempts of another student. This experimental variation allows to show the necessity of own failure for the effectiveness of the PF approach. The results indicated that the two conditions were equally effective at preparing students for subsequent instruction.

This study investigated teachers' monitoring and regulation of students' learning from texts. According to the cue-utilization framework (Koriat, in Journal of Experimental Psychology, 126, 349-370, 1997), monitoring accuracy depends on how predictive the information (or cues) that teachers use to make monitoring judgments actually is for students' performance. Accurate monitoring of students' comprehension is considered a precondition for adaptive regulation of students' learning. However, these assumptions have not yet been directly investigated. We therefore examined teachers' cue-utilization and how it affects their monitoring and regulation accuracy. In a within-subjects design, 21 secondary education teachers made monitoring judgments and regulation decisions for fifteen students under three cue-availability conditions: (1) only student cues (i.e., student's name); (2) only performance cues (i.e., diagrams students completed about texts they had read); and (3) both student and performance cues (i.e., student's name and completed diagram). Teachers' absolute and relative monitoring accuracy was higher when having student cues available in addition to diagram cues. Teachers' relative regulation accuracy was higher when having only performance cues available instead of only student cues (as indicated by a direct effect). Monitoring accuracy predicted regulation accuracy and in addition to a direct effect, we also found and indirect effect of cue-availability on regulation accuracy (via monitoring accuracy). These results suggest that accurate regulation can be brought about both indirectly by having accurate monitoring judgments and directly by cue-utilization. The findings of this study can help to refine models of teacher monitoring and regulation and can be useful in designing effective interventions to promote teachers' monitoring and regulation.

There is a need for effective methods to teach critical thinking (CT). One instructional method that seems promising is comparing correct and erroneous worked examples (i.e., contrasting examples). The aim of the present study, therefore, was to investigate the effect of contrasting examples on learning and transfer of CT-skills, focusing on avoiding biased reasoning. Students (N = 170) received instructions on CT and avoiding biases in reasoning tasks, followed by: (1) contrasting examples, (2) correct examples, (3) erroneous examples, or (4) practice problems. Performance was measured on a pretest, immediate posttest, 3-week delayed posttest, and 9-month delayed posttest. Our results revealed that participants' reasoning task performance improved from pretest to immediate posttest, and even further after a delay (i.e., they learned to avoid biased reasoning). Surprisingly, there were no differences in learning gains or transfer performance between the four conditions. Our findings raise questions about the preconditions of contrasting examples effects. Moreover, how transfer of CT-skills can be fostered remains an important issue for future research.

Practicing retrieval of vocabulary items from memory (e.g., with flashcard software or practice tests) is an effective study strategy to remember vocabulary over time. Retrieval practice is often implemented in digital learning environments that increasingly include multimedia (i.e., combining textual and pictorial information). However, it is unknown how multimedia design affects the benefits of retrieval. Therefore, the present study tested the effect of adding images during retrieval practice on students' learning, affective-motivational outcomes, and judgments of learning. We experimentally manipulated the presence and timing of images during retrieval practice of foreign vocabulary in three classroom experiments with students in secondary education. Across experiments, students' vocabulary recall on a posttest (1 to 4 days after practice) was weaker after practice with images that helped them retrieve the answer, compared with practice without images (Experiments 2 and 3) and compared with practice with images that appeared after the retrieval attempt (Experiments 1 and 3). Images enhanced feelings of competence but not enjoyment of practice. The majority of students recognized the negative effects of images on their learning only when the images clearly revealed the answer (Experiment 1) but--incorrectly--considered images that provided partial hints about the answer to be helpful (Experiments 2 and 3). Moreover, students consistently overestimated how much they learned with images that helped them retrieve the answer. During retrieval practice of vocabulary words, informative images are thus potentially harmful and students have limited insight into these effects.

Although the association between teacher-student relations, teacher emotions, and burnout has been proven on a general level, we do not know the exact processes underlying these associations. Recently there has been a call for intra-individual process measures that assess what happens from moment-to-moment in class in order to better understand inter-individual differences in emotions and burnout between teachers. This paper explored the use of process measures of teachers' heart rate and their interpersonal behavior during teaching. Our aim was to illustrate different ways of analyzing and combining physiological and observational time-series data and to explore their potential for understanding between-teacher differences. In this illustration, we focused on two teachers who represented contrasting cases in terms of their self-reported teaching-related emotions (i.e., anxiety and relaxation) and burnout. We discuss both univariate process analyses (i.e., trend, autocorrelation, stability) as well as state-of-the-art multivariate process analyses (i.e., cross-correlations, dynamic structural equation modeling). Results illustrate how the two teachers differed in the nature of their physiological responses, their interpersonal behavior, and the association between these two process measures over time. Along implications and suggestions for further research, it is discussed how the process-based, dynamic assessment of physiology and interpersonal behavior may ultimately help to understand differences in more general teaching-related emotions and burnout.

The Productive Failure (PF) approach prompts students to attempt to solve a problem prior to instruction -- at which point they typically fail. Yet, research on PF shows that students who are involved in problem solving prior to instruction gain more conceptual knowledge from the subsequent instruction compared to students who receive the instruction first. So far, there is no conclusive evidence, however, that the beneficial effects of PF are explained by the attempt to generate "one's own" solutions prior to instruction. The literature on example-based learning suggests that observing someone else engaging in problem-solving attempts may be an equally effective means to prepare students for instruction. In an experimental study, we compared a PF condition, in which students were actively engaged in problem solving prior to instruction, to two example conditions, in which students either observed the complete "problem-solving-and-failing" process of another student engaging in PF or looked at the outcome of this process (i.e., another student's failed solution attempts). Rather than worked examples of the correct solution procedure, the students observed examples of failed solution attempts. We found that students' own problem solving was not superior to the two example conditions. In fact, students who observed the complete PF process even outperformed students who engaged in PF themselves. Additional analyses revealed that the students' prior knowledge moderated this effect: While students who observed the complete PF process were able to take advantage of their prior knowledge to gain more conceptual knowledge from the subsequent instruction, prior knowledge did not affect students' post-test performance in the PF condition.

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.

Problem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the self-regulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to self-monitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections.

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]

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts' (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts' changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts' natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts' behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts' automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.

Eye movement modelling examples (EMMEs) are instructional videos of a model's demonstration and explanation of a task that also show where the model is looking. EMMEs are expected to synchronize students' visual attention with the model's, leading to better learning than regular video modelling examples (MEs). However, synchronization is seldom directly tested. Moreover, recent research suggests that EMMEs might be more effective than ME for low prior knowledge learners. We therefore used a 2 × 2 between-subjects design to investigate if the effectiveness of EMMEs (EMMEs/ME) is moderated by prior knowledge (high/low, manipulated by pretraining), applying eye tracking to assess synchronization. Contrary to expectations, EMMEs did not lead to higher learning outcomes than ME, and no interaction with prior knowledge was found. Structural equation modelling shows the mechanism through which EMMEs affect learning: Seeing the model's eye movements helped learners to look faster at referenced information, which was associated with higher learning outcomes.

Two experiments investigated the effects of an instructional video's setting on learners' retention and application of the video content. Experiment 1 explored competing hypotheses based on theoretical assumptions about whether an authentic setting would serve as a distraction or as a cue for the instructor's expertise. Participants (N = 59) watched a video about floral diagrams and floral formulas that was either shot in a greenhouse (authentic setting) or in front of a white wall (neutral setting). Results showed a beneficial effect of the authentic setting on retention, but not on the application of the video content. Experiment 2 aimed to replicate and extend these findings by investigating whether reinstating the authentic setting as a contextual cue during the test phase would further improve test performance. After watching a video that was either shot in an authentic or a neutral setting, participants (N = 149) worked on the retention and application test while a screenshot of the authentic or the neutral setting was presented as a background. Contrary to our expectations, the effect of setting on retention did not replicate and there was no evidence for context effects, despite using the same learning materials and a comparable sample as in Experiment 1. Findings are discussed with regard to potential boundary conditions.

Previous research has shown that explaining is an effective activity to enhance learning. In prior studies, students were instructed to explain the contents after completing an entire learning phase. Explaining at the end of a learning phase, however, may be less apt to support comprehension monitoring and subsequent regulation activities. In 2 experiments, we investigated whether explaining in earlier phases of studying (i.e., in-between explaining) would foster learning more than explaining after the entire study phase (i.e., afterstudy explaining). In Experiment 1, university students (N = 91) read a text about combustion engines and either explained the contents between the study phases or at the end of the entire study phase. A third group recalled the learning contents aloud at the end of the study phase to control for retrieval-processes that may also be involved in explaining. Results showed no overall effect of explaining in comparison to retrieval practice. However, in-between explaining enhanced students' conceptual knowledge as compared with afterstudy explaining. Verbal protocol analyses showed that this effect was due to students' increased monitoring. Experiment 2 (N = 126), had a 2 × 2-factorial design with between-subjects factors timing (in-between vs. afterstudy) and learning activity (explaining vs. written retrieval practice). We found a cascaded trend: In-between learning activities were more effective than afterstudy learning activities, whereas explaining was more effective than written retrieval practice. These findings suggest that the timing of learning activities is crucial to improve learning. Additionally, our findings reveal that explaining is not simply a result of retrieval practice.

Research on productive failure suggests that attempting to solve a problem prior to instruction facilitates conceptual understanding compared to receiving instruction prior to problem solving. The assumptions are that during the problem-solving phase, students activate their prior knowledge, become aware of their knowledge gaps, and discover deep features of the target content, which prepares them to better process the subsequent instruction. Unclear is whether this effect results from merely changing the order of the learning phases (i.e., instruction or problem solving first) or from additional features, such as presenting problem-solving material in the form of cases that differ in one feature at a time. Contrasting such cases may highlight the deep features and provide grounded feedback to students' problem-solving attempts. In addition, the effect of the order of instruction and problem solving on procedural fluency is still unclear. The present experiment (N = 181, mean age = 14.53) investigated in a 2 × 2 design the effects of order (instruction or problem solving first) and of contrasting cases in the problem-solving material (yes/no) on conceptual understanding and procedural fluency. Additionally, the quality and quantity of students' solution attempts from the problem-solving phase were coded. Regarding the learning outcomes, the ANOVA results suggest that for procedural fluency instruction prior to problem solving was more beneficial than problem solving prior to instruction. Merely delaying instruction did not increase conceptual understanding. The contrasting cases did not affect the quality of solution attempts, nor the posttest results. As expected, students who received instruction first generated fewer, but higher-quality solution attempts.

Eye movements reveal what is at the center of people's attention, which is assumed to coincide with what they are thinking about. Eye-movement displays (visualizations of a person's fixations superimposed onto the stimulus, for example, as dots or circles) might provide useful information for diagnosing that person's performance. However, making inferences about a person's task performance based on eye-movement displays requires substantial interpretation. Using graph-comprehension tasks, we investigated to what extent observers (N = 46) could make accurate inferences about a performer's multiple-choice task performance (i.e., chosen answer), confidence, and competence from displays of that person's eye movements. Observers' accuracy when judging which answer the performer chose was above chance level and was higher for displays reflecting confident performance. Observers were also able to infer performers' confidence from the eye-movement displays; moreover, their own task performance and perceived similarity with the performer affected their judgments of the other's competence.

Studies on the productive failure (PF) approach have demonstrated that attempting to solve a problem prepares students more effectively for later instruction compared to observing failed problem-solving attempts prior to instruction. However, the examples of failure used in these studies did not display the problem-solving-and-failing "process," which may have limited the preparatory effects. In this quasi-experiment, we investigated whether observing someone else engaging in problem solving can prepare students for instruction, and whether examples that show the problem-solving-and -failing "process" are more effective than those that only show the outcome of this process. We also explored whether the perceived model-observer similarity had an impact on the effectiveness of observing examples of failure. The results showed that observing examples effectively prepares students for learning from instruction. However, observing the model's problem-solving-and-failing process did not prepare students more effectively than merely looking at the outcome. Studying examples were more effective if model-observer similarity was high.