多媒体学习原理在规模上预测测验表现

Anita B. Delahay, M. Lovett
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引用次数: 1

摘要

经验支持的多媒体学习(MML)原则[1]提出了设计教学的有效方法,通常是针对图形或活动的顺序元素。我们研究了MML的积极影响是否可以在MOOC的大型教学单元中检测到。我们编码了与MML原则相对应的教学设计(ID)特征,将测试项目映射到这些特征以及MOOC参与者对它们的使用,并试图预测测试表现。我们发现与MML相关的教学特征,即具有高质量示例和简明文本的实践问题,具有积极的预测性。我们认为,从教学材料的特征来预测测验项目的表现是可能的,并建议将这种方法扩展到ID的其他方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimedia learning principles at scale predict quiz performance
Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.
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