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It is well established in educational research that metacognitive monitoring of performance assessed by self-reports, for instance, asking students to report their confidence in provided answers, is based on heuristic cues rather than on actual success in the task. Subjective self-reports are also used in educational research on cognitive load, where they refer to the perceived amount of mental effort invested in or difficulty of each task item. In the present study, we examined the potential underlying bases and the predictive value of mental effort and difficulty appraisals compared to confidence appraisals by applying metacognitive concepts and paradigms. In three experiments, participants faced verbal logic problems or one of two non-verbal reasoning tasks. In a between-participants design, each task item was followed by either mental effort, difficulty, or confidence appraisals. We examined the associations between the various appraisals, response time, and success rates. Consistently across all experiments, we found that mental effort and difficulty appraisals were associated more strongly than confidence with response time. Further, while all appraisals were highly predictive of solving success, the strength of this association was stronger for difficulty and confidence appraisals (which were similar) than for mental effort appraisals. We conclude that mental effort and difficulty appraisals are prone to misleading cues like other metacognitive judgments and are based on unique underlying processes. These findings challenge the accepted notion that mental effort appraisals can serve as reliable reflections of cognitive load.

Background: As a result of the COVID-19 pandemic, many teachers found themselves making a rapid and often challenging shift from in-person classroom teaching to teaching in an online environment. As teachers continue to learn about working in this new environment, research in cognitive and learning sciences, specifically findings from cognitive load theory and related areas, can provide meaningful strategies for teaching in this 'new normal'. Objectives: This paper describes 12 tips derived from contemporary research in educational psychology, focusing particularly on empirically supported strategies that teachers may apply in their online classroom to ensure that learning is optimized. Implications for Practice: These strategies are generalizable across age groups and learning areas, and are categorized into one of two themes: approaches to optimize the design of online learning materials, and instructional strategies to support student learning. A discussion follows, outlining how teachers may apply these strategies in different contexts, with a brief overview of emerging efforts that aim to bridge cognitive load theory and self-regulated learning research.

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.

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.

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.

A central factor in research guided by the Cognitive Load Theory (CLT) is the mental effort people invest in performing a task. Mental effort is commonly assessed by asking people to report their effort throughout performing, learning, or problem-solving tasks. Although this measurement is considered reliable and valid in CLT research, metacognitive research provides robust evidence that self-appraisals of performance are often biased. In this review, we consider the possibility that mental effort appraisals may also be biased. In particular, we review signs for covariations and mismatches between subjective and objective measures of effort. Our review suggests that subjective and most objective effort measures appear reliable and valid when evaluated in isolation, because they discriminate among tasks of varying complexity. However, not much is known about their mutual correspondence--that is, whether subjective measures covariate with objective measures. Moreover, there is evidence that people utilize heuristic cues when appraising their effort, similar to utilization of heuristic cues underlying metacognitive appraisals of performance. These cues are identified by exposing biases--mismatch in effects of cue variations on appraisals and performance. The review concludes with a research agenda in which we suggest applying the well-established methodologies for studying biases in self-appraisals of performance in metacognitive research to investigating effort appraisals. One promising method could be to determine the covariation of effort appraisals and objective effort measures as an indicator of the resolution of effort appraisals.

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 suggests some sequences of examples and problems (i.e., EE, EP) are more effective (higher test performance) and efficient (attained with equal/less mental effort) than others (PP, sometimes also PE). Recent findings suggest this is due to motivational variables (i.e., self-efficacy), but did not test this during the training phase. Moreover, prior research used only short task sequences. Therefore, we investigated effects on motivational variables, effectiveness, and efficiency in a short (Experiment 1; four learning tasks; n = 157) and longer task sequence (Experiment 2; eight learning tasks; n = 105). With short sequences, all example conditions were more effective, efficient, and motivating than PP. With longer sequences, all example conditions were more motivating and efficient than PP, but only EE was more effective than PP. Moreover, EE was most efficient during training, regardless of sequence length. These results suggest that example study (only) is more effective, efficient, and more motivating than PP.

Decades of research has shown that example-based learning is an effective instructional strategy for learning new skills. The field of learning from examples is seeing a shift in focus towards more innovative and use-inspired research, in part because the use of examples for informal and formal learning purposes has mushroomed. This special issue comprises a set of eight papers in which students learned a procedural skill from worked examples or modeling examples. Each study characterizes a recent development towards more innovative example-based learning research. These developments are: (a) the integration of social-cognitive and cognitive example research, (b) the integration of example-based learning and analogical reasoning research, (c) the extension of traditional Cognitive Load Theory effects, (d) a greater focus on learning from (productive) errors, and (e) more research on individual differences. This special issue concludes with insightful commentary articles written by Prof. Dr. Katharina Scheiter and Prof. Dr. Richard Mayer.

Example-based learning (i.e., studying examples to learn a problem-solution procedure, often alternated with solving practice problems) leads to better learning outcomes than solving practice problems only, and video examples are increasingly being used in online and blended learning environments. Recent findings show that the presentation order of examples and problems affects learning: Example-problem pairs have been found to be more effective than problem-example pairs. We investigated a motivational explanation for this difference, which states that starting with a practice problem might be too difficult, causing learners to lose confidence and motivation to study. We investigated this by presenting gifted (n = 61) and nongifted primary school students (n = 65) with two problem-example or example-problem pairs. We hypothesized that gifted students, who generally report higher perceived competence and autonomy and higher need for cognition, would be less affected by the difficulty of starting with a problem. As expected, gifted students indeed reported higher motivation and confidence than nongifted students, and gifted students were more efficient learners. In contrast to our expectations, however, there was no difference between gifted and nongifted students in the effect of the different task sequences on test performance. Studying example-problem pairs was more efficient than studying problem-example pairs, both for gifted and nongifted students.

Recent findings show that after studying a text, teaching the learned content on video to a fictitious peer student improves learning more than restudying the content. This benefit may be in part due to increased arousal associated with the teaching activity. The present experiment investigated whether teaching on video is also effective for acquiring problem-solving skills from worked examples, and explored the role of cognitive load, worry, and arousal. Participants (N = 61 university students) first studied two worked examples on electrical circuits troubleshooting and completed a practice problem. Then they either taught the content of a worked example of the practice problem on video (teaching condition) or studied that worked example (control condition) for the same amount of time. Self-reported cognitive load was measured after each task and self-reported worry after the final task. Effects on arousal were explored via the Empatica wristband measuring electrodermal activity (EDA; i.e., galvanic skin response). Teaching the content of the worked example on video was not associated with more worry, but did result in higher perceived cognitive load, more arousal, and better performance on isomorphic and transfer problems on the posttest. Although this finding has to be interpreted with caution, teaching also seemed to moderate the effect of prior knowledge on transfer that was present in the study condition. This suggests that teaching is particularly effective for students who initially have low prior knowledge.

The present study tests whether presenting video modeling examples from the learner's (first-person) perspective promotes learning of an assembly task, compared to presenting video examples from a third-person perspective. Across 2 experiments conducted in different labs, university students viewed a video showing how to assemble an 8-component circuit on a circuit board. Students who viewed the assembly video recorded from a first-person perspective performed significantly better than those who viewed the video from a third-person perspective on accuracy in assembling the circuit in both experiments and on time to assemble the circuit in Experiment 1, but not in Experiment 2. Concerning boundary conditions, the perspective effect was stronger for more complex tasks (Experiment 1), but was not moderated by imitating the actions during learning (Experiment 1) or explaining how to build the circuit during the test (Experiment 2). This work suggests a perspective principle for instructional video in which students learn better when video reflects a first-person perspective. An explanation based on embodied theories of learning and instruction is provided.

Example-based learning is a very effective and efficient instructional strategy for novices. It can be implemented using text-based worked examples that provide a written demonstration of how to perform a task, or (video) modelling examples in which an instructor (the "model") provides a demonstration. The model-observer similarity (MOS) hypothesis predicts that the effectiveness of modelling examples partly depends on the degree to which learners perceive the models to be similar to them. It is an open question, however, whether perceived similarity with the person who created the example, would also affect learning from text-based worked examples. Therefore, two experiments were conducted to investigate whether MOS would also play a role in learning from worked examples. In Experiment 1 (N = 147), students were led to believe via pictures and a short story that the worked examples were created by a male or female peer student. Males showed higher performance and confidence, but no effects of MOS on learning were found. In Experiment 2 (N = 130), students were led to believe that a peer student or a teacher created the examples. Again, no effects of MOS were found. These findings suggest that the perceived origin of text-based worked examples is not important for learning.

Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer similarity hypothesis suggests that such characteristics may affect learning. Therefore, this study investigated whether the effectiveness of learning how to solve a probability calculation problem from video modeling examples would vary as a function of the model's and observer's gender. In a 2 (Model: Female/Male) × 2 (Observer: Female/Male) between-subject design, 167 secondary education students learned how to solve probability calculation problems by observing video modeling examples. Results showed no effects of Model or Observer gender on learning and near transfer. Male students reported higher self-efficacy than female students. Compared to a female model, observing a male model enhanced perceived competence more from pretest to posttest, irrespective of observers' gender. Furthermore, learning from a male model was less effortful and more enjoyable for male students than for female students. These results suggest that gender of both model and observer can matter in terms of affective variables experienced during learning, and that instructional designers may want to consider this when creating (online) learning environments with video modeling examples.