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Investigating Application of the Self-Explanation Learning Strategy during an Instructional Simulation

机译：自我解释学习策略在教学仿真中的应用研究

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摘要

Computer-based simulations effectively support the acquisition of scientific knowledge when combined with a guided learning approach. Active learning drives complex cognitive processes that enable the integration of new information with existing knowledge. The iCAP (Interactive, Constructive, Active, Passive) Framework provides a conceptual model to describe different types of active learning. Computer-based simulations fit neatly within this framework. Similarly, self-explanation is a generative learning strategy that fits within this framework. Promoting self-explanation using instructional prompts is an effective method for driving application of the strategy. This study compared three combinations of self-explanation prompt and learner activity (closed prompts---overt activity, open prompts---overt activity, open prompts---non-overt activity) when using an instructional simulation to acquire knowledge related to scientific principles. Outcome measures included pretest-posttest comparisons, cognitive load, and self-efficacy.;Results of the study indicated that closed prompts were more effective in driving application of the self-explanation learning strategy and learning outcomes when used within the context of an instructional simulation. Findings were less conclusive in terms of the type of activity (overt/non-overt). Only the closed prompts---overt activity treatment supported the attainment of greater learning outcomes when compared to the other treatments. No significant difference in learning outcomes was found for the open prompts---overt activity, and the open prompts---non-overt activity. In relation to cognitive load, no significant difference was revealed between treatments. In relation to self-efficacy, no significant difference was revealed between treatments or between measures recorded pre-instruction and post-instruction.
机译：与指导学习方法结合使用时，基于计算机的模拟有效地支持了科学知识的获取。主动学习驱动着复杂的认知过程，使新信息与现有知识整合在一起。 iCAP（交互式，构造性，主动，被动）框架提供了一个概念模型来描述不同类型的主动学习。基于计算机的模拟恰好适合此框架。同样，自我解释是一种适合该框架的生成型学习策略。使用指导性提示来促进自我解释是驱动该策略应用的有效方法。这项研究比较了自我解释性提示和学习者活动的三种组合（封闭式提示---公开活动，开放式提示-公开活动，开放式提示-非公开活动），当使用教学模拟来获取与之相关的知识时科学原理。结果测量包括测验前测验后的比较，认知负荷和自我效能感。研究结果表明，封闭式提示在教学模拟的背景下使用时，更能有效地推动自我解释学习策略和学习成果的应用。就活动类型（公开/不公开）而言，结论并不那么确定。与其他治疗方法相比，只有封闭式提示-公开活动治疗支持获得更好的学习成果。公开提示-公开活动和公开提示-非公开活动的学习结果没有发现显着差异。关于认知负荷，治疗之间没有显着差异。关于自我效能，在使用前或使用后记录的治疗之间或记录的措施之间没有显着差异。

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  • 作者

    Mac Loughlin, Paul Michael.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Instructional design.;Educational technology.;Science education.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 110 p.
  • 总页数 110
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

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