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The present study was designed to understand and optimize self-assessment accuracy in cognitive skill acquisition through example-based learning. We focused on the initial problem-solving phase, which follows after studying worked examples. At the end of this phase, it is important that learners are aware whether they have already understood the solution procedure. In Experiment 1, we tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation-based cues (ability to explain the problems' solutions) or from performance-based cues (problem-solving performance) and on whether learners were informed about the to-be-monitored cue before or only after the problem-solving phase. We found that performance-based cues resulted in better self-assessment accuracy and that informing learners about the to-be-monitored cue before problem-solving enhanced self-assessment accuracy. In Experiment 2, we again tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation- or performance-based cues. We furthermore varied whether learners received instruction on criteria for interpreting the cues and whether learners were prompted to self-explain during problem-solving. When learners received no further instructional support, like in Experiment 1, performance-based cues yielded better self-assessment accuracy. Only when learners who were prompted to infer their self-assessments from explanation-based cues received both cue criteria instruction and prompts to engage in self-explaining during problem-solving did they show similar self-assessment accuracy as learners who utilized performance-based cues. Overall, we conclude that it is more efficient to prompt learners to monitor performance-based rather than explanation-based cues in the initial problem-solving phase.

Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can be compared easily, this is challenging for transient video-based modelling examples used in complex domains because simultaneous processing of two videos is not feasible. Objectives: To allow for such comparisons, we combined video-based modelling examples with static representations (i.e., summarizing tables) of the observed optimal and a suboptimal solution of the problem-solving process. A comparative self-explanation prompt asked learners to compare the different solution approaches. Our study investigated the impact of video-based modelling examples versus independent problem-solving on cognitive load and problem-solving skill development. Moreover, we investigated the effects of comparative versus sequential self-explanation prompts, depending on learners' prior knowledge. Methods: In an experiment, 118 automotive apprentices learned a car malfunction diagnosis strategy. Apprentices were divided into three groups: (1) modelling examples with comparative self-explanation prompts, (2) modelling examples with sequential prompts, and (3) no examples or prompts. Diagnostic knowledge and skills were assessed before and after the intervention. Cognitive load was measured retrospectively. Results and conclusions: Despite no observed effects on cognitive load, modelling examples enhanced diagnostic knowledge and diagnostic skills with scaffolds, though not independent diagnostic skills without scaffolds. The need for more practice opportunities to foster independent diagnostic skills is assumed. Additionally, comparative prompts seem promising for learners with higher prior knowledge. Takeaways: Video-based modelling examples were more beneficial for learning than practising to apply the diagnostic strategy. Static representations allow for comparisons of video examples and comparative prompts are promising for learners with higher prior knowledge (cf. expertise-reversal effect). Further research, especially on the effects on cognitive load, is necessary.

Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner's prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.

In the context of instructional design and self-regulated learning research, the notion of mental effort allocation, monitoring, and control has gained increasing attention. Bringing together a cognitive perspective, focusing on Cognitive Load Theory, and a motivational perspective, merging central accounts from Situated Expectancy Value Theory and Self-Determination Theory, we plea for a three-fold conception of effort that clearly distinguishes the different psychological sources of experiencing and allocating effort in learning environments: effort-by-complexity, effort-by-need frustration, and effort-by-allocation. Such a detailed conception has important implications for how effort should be studied and how it can be influenced by instructional support or by the learning individual itself. A first conclusion we draw is that cognitively oriented research needs to be careful when taking students' self-reports on the "effortfulness" of a task as an indication of the object-level cognitive requirements of the task, as such appraisals may also reflect the affective-emotional requirements of task execution as well as motivational beliefs regarding the likelihood of success and meaningfulness of a task. A second conclusion is that instructional procedures rooted in cognition-oriented theory ideally are complemented by motivation theory to support student learning optimally.

Background: Students often show unfavourable attribution: they attribute poor school performance to stable factors such as lack of ability and good school performance to variable factors such as effort. However, attribution can be influenced by individualized digital re-attributional feedback leading to positive motivational effects and higher learning outcomes. This is very promising, but it still is unclear, whether this digital re-attributional feedback can also be successfully integrated in everyday classroom activities. Objectives: The present field experiment investigated how integrating digital re-attributional feedback into classroom instruction affects student attribution, motivation and learning outcomes. Methods: In the experiment, 8th-10th grade high school students (N = 322) worked with a digital mathematics learning program which was integrated in a three-week teaching unit. Half the students in each classroom received only standard feedback after each practice task (SF group); the other half received additionally an individual re-attributional feedback (RF group) after every third task. Attribution, mathematics self-concept, and self-efficacy were measured by an online questionnaire twice a week; learning outcomes in mathematics were measured weekly. Results and Conclusion: Hierarchical analyses showed that re-attributional feedback led to a more favourable attribution in case of success on stable factors. Especially low-performing students benefited from this feedback. No effects on attribution in case of failure, self-efficacy or learning outcomes could be found. Further research could investigate if certain adaptations to the digital re-attributional feedback is more effective in a real classroom setting and has a broader impact on different students.

Background & Aims: Cognitive load theory assumes that the higher the learner's prior knowledge (i.e., the more expert the learner), the lower the intrinsic cognitive load (complexity) experienced for a given problem. While this is the case in many scenarios, there can be cases in which the converse is also true, resulting in more expert learners reporting higher intrinsic cognitive load than novices for the same problem. This can occur in relation to problems involving complex systems (e.g., ecological systems), for which novices' problem representations may underestimate problem complexity and therefore report lower intrinsic load than experts. This finding is borne out in the current paper. Samples, Methods & Results: In Study 1 with 118 participants from the Black Forest area in Germany, participants with higher levels of forestry and ecological expertise evaluated a problem relating to the restructuring of the Black Forest to adapt to climate change as more complex than did novices. In Study 2 (within-subjects design, n = 66 primary-school students), we conceptually replicated this finding in a domain more typical of cognitive load theory studies, mathematics. We found that higher prior knowledge also reduced the underestimation of the complexity of 'tricky', but frequently used, mathematics word problems. Conclusion: Our findings suggest that cognitive load theory's assumptions about intrinsic load and prior knowledge should be refined, as there seems to exist a sub-set of problem-solving tasks for which the traditional relationship between prior knowledge and reported ICL is reversed.

In psychology programs, students should acquire both domain-specific knowledge and cross-domain competences important for later practice (e.g., multiple document literacy). Typically, such competences are trained "directly" in courses explicitly devoted to them or indirectly in courses on psychological topics that require them without systematically teaching and training them (e.g., when reading multiple texts about a topic). To exploit the advantages of both direct and indirect training approaches, we combined them in a new psychology course in which students were taught domain-specific knowledge on pedagogy and psychotherapy, and they were trained in the competence facets of (a) epistemic beliefs, (b) multiple document literacy, and (c) argumentative thinking. The direct training took a tried-and-tested example-based learning approach. A central element of the indirect training consisted of course assessments requiring the application of these three competences to the contents about pedagogy and psychotherapy. The combined training approach led to significant increases in declarative knowledge, advanced epistemic beliefs, and greater self-efficacy in implementing strategies relating to multiple document literacy and argumentative thinking. This approach can be adapted to accommodate different psychological content areas or different cross-domain competences.

IntroductionWorked examples support initial skill acquisition. They often show skill application on content knowledge from another, “exemplifying” domain (e.g., argumentation skills have to be applied to some contents). Although learners’ focus should remain on the skill, learners need to understand the content knowledge to benefit from worked examples. Previous studies relied on exemplifying domains that are familiar and contain simple topics, to keep learners’ focus on skill acquisition.AimWe examined whether using a relevant exemplifying domain would allow learners to acquire both skills and content knowledge simultaneously, or whether relevant content distracts from the main learning goal of skill acquisition.Methods and resultsIn a training study with 142 psychology students, we used example-based learning materials with an exemplifying domain that was either relevant or irrelevant for participants’ course outcomes. We assessed cognitive load, declarative knowledge about skills and course-related content knowledge, and argumentation quality. Incorporating relevant content knowledge in worked examples did not reduce learning outcomes compared to a condition using an irrelevant exemplifying domain.DiscussionContrary to previous research, the results suggest that worked examples with a relevant exemplifying domain could possibly be an efficient teaching method for fostering skills and content knowledge simultaneously.

Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence-based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change. We wanted to know whether making use of a computer algorithm to personalize refutation texts would best initiate teachers' conceptual change. Methods: We designed an online experiment, in which N = 129 in-service teachers read either (1) expository texts (without direct refutation), (2) common refutation texts, or (3) personalized refutation texts. The teachers filled in a misconception questionnaire pre and post to assess their conceptual change. Results and Conclusions: Statistical analyses revealed that personalized refutation texts initiated the strongest conceptual change, which was driven by increased feelings of guilt and shame. Common refutation texts did not foster teachers' conceptual change as compared to expository texts. These findings indicate that refutation texts should be personalized for experienced practitioners such as teachers. Takeaways: Personalized refutation seems to be promising in the context of online teacher training programs. Further research should test to which extent the present findings also apply to other groups of experienced learners or practitioners.

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.

A worked-out or an open inventing problem with contrasting cases can prepare learners for learning from subsequent instruction differently regarding motivation and cognition. In addition, such activities potentially initiate different learning processes during the subsequent ("future") learning phase. In this experiment (N = 45 pre-service teachers), we aimed to replicate effects of earlier studies on learning outcomes and, on this basis, to analyze respective learning processes during the future-learning phase via think-aloud protocols. The inventing group invented criteria to assess learning strategies in learning journals while the worked-example group studied the same problem in a solved version. Afterwards, the pre-service teachers thought aloud during learning in a computer-based learning environment. We did not find substantial motivational differences (interest, self-efficacy), but the worked-example group clearly outperformed their counterparts in transfer (BF[subscript +0] > 313). We found moderate evidence for the hypothesis that their learning processes during the subsequent learning phase was deepened: the example group showed more elaborative processes, more spontaneous application of the canonical, but also of sub-optimal solutions than the inventing group (BFs around 4), and it tended to focus more on the most relevant learning contents. Explorative analyses suggest that applying canonical solutions to examples is one of the processes explaining why working through the solution leads to higher transfer. In conclusion, a worked-out inventing problem seems to prepare future learning more effectively than an open inventing activity by deepening and focusing subsequent learning processes.

Prior research indicates that student teachers frequently have misconceptions about multimedia learning. Our experiment with N = 96 student teachers revealed that, in contrast to standard texts, refutation texts are effective to address misconceptions about multimedia learning. However, there seems to be no added benefit of making "concessions" to student teachers' prior beliefs (i.e., two-sided argumentation) in refutation texts. Moreover, refutation texts did not promote the selection of appropriate multimedia material. This study suggests that refutation texts addressing multimedia-learning misconceptions should be applied in teacher education. Yet, further support seems needed to aid the application of the corrected knowledge.

We investigated how to optimize the effectiveness of retrieval-based learning when the instructional text comprises seductive details (i.e., interesting but irrelevant text adjuncts). Specific questions during retrieval practice should help students focus their recall on main ideas--and not on seductive details, which should in turn foster delayed post-test performance. In this experiment, participants (N = 103) learned from an instructional text about coffee, either with or without seductive details; in subsequent retrieval practice, the participants received either unspecific or specific questions (2 × 2 between-subjects design). One week later, all participants received a delayed posttest assessing learning outcomes. As expected, when the instructional text comprised seductive details, participants given specific questions during retrieval practice had better learning outcomes than those given an unspecific question. We conclude that retrieval tasks should be aligned with learning materials: more specific retrieval tasks are better for materials including irrelevant information.

When analyzing classroom video, pre-service teachers can improve their professional vision, that is, their ability to notice important events in a classroom and to interpret them based on theoretical knowledge. However, learning with video is especially challenging for novice learners. Thus, video needs to be embedded into an instructional context to be effective. In an experimental study with 89 pre-service biology teachers, we investigated the effect of a short professional vision training and whether two design principles from multimedia learning research--namely segmenting and self-explanation prompts--could additionally increase training effects. In a one-hour training session on small-group tutoring strategies, participants watched practice video examples either as a whole or segmented. After each video or video segment, respectively, they received either open or focused self-explanation prompts to analyze the scene. We assessed participants' professional vision skills before and after training. Overall, participants' performance substantially increased from pretest to posttest. Moreover, during training, both segmented video examples and focused self-explanation prompts led to increased noticing of relevant strategies. This advantage during training, however, did not result in higher professional vision improvement in posttest scores compared to participants who worked in the less supported training phase conditions. We discuss possible explanations why additional support increased training performance but not learning gains and suggest an additional fading phase as a means to achieve persistent effects.