Julius Moritz Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey
{"title":"通过视频示范示例和对比自述提示，教授复杂问题的解决策略","authors":"Julius Moritz Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey","doi":"10.1111/jcal.12991","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results and conclusions</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Takeaways</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"40 4","pages":"1852-1870"},"PeriodicalIF":5.1000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.12991","citationCount":"0","resultStr":"{\"title\":\"Video-based modeling examples and comparative self-explanation prompts for teaching a complex problem-solving strategy\",\"authors\":\"Julius Moritz Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey\",\"doi\":\"10.1111/jcal.12991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results and conclusions</h3>\\n \\n <p>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. 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Video-based modeling examples and comparative self-explanation prompts for teaching a complex problem-solving strategy
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.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
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