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

IF 5.1 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Journal of Computer Assisted Learning Pub Date : 2024-04-23 DOI:10.1111/jcal.12991
Julius Moritz Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey
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引用次数: 0

摘要

在基于范例的学习中,范例通常与生成活动相结合,例如对范例进行比较性自我解释。比较对工作记忆的要求很高,尤其是在复杂的领域。因此,只有较强的学习者才能从比较性自我解释中获益。虽然基于文本的静态示例可以很容易地进行比较,但对于复杂领域中使用的基于视频的瞬时建模示例来说,这却具有挑战性,因为同时处理两段视频是不可行的。为了进行这种比较,我们将基于视频的建模示例与问题解决过程中观察到的最优解和次最优解的静态表示(即汇总表)相结合。比较性自我解释提示要求学习者比较不同的解决方法。我们的研究调查了基于视频的建模示例与独立解决问题对认知负荷和解决问题技能发展的影响。在一项实验中,118 名汽车学徒学习了汽车故障诊断策略。学徒被分为三组:(1) 有比较性自我解释提示的示范示例组;(2) 有顺序提示的示范示例组;(3) 没有示例或提示的示范示例组。在干预前后对诊断知识和技能进行了评估。尽管没有观察到对认知负荷的影响,但有脚手架的示范示例增强了诊断知识和诊断技能,而无脚手架的示范示例则没有增强独立诊断技能。这说明需要更多的练习机会来培养独立诊断技能。此外,比较性提示对于已有知识较多的学习者来说似乎很有前途。基于视频的建模示例比练习应用诊断策略更有利于学习。静态表征可以对视频示例进行比较,而比较性提示对已有知识较多的学习者很有帮助(参见专业知识反向效应)。有必要开展进一步的研究,尤其是关于对认知负荷的影响的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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.

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来源期刊
Journal of Computer Assisted Learning
Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
9.70
自引率
6.00%
发文量
116
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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