Investigating the Impact of Skill-Related Videos on Online Learning
Investigating the Impact of Skill-Related Videos on Online Learning
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调查技能相关视频对在线学习的影响
DOI：
10.1145/3573051.3593376
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Heffernan, Neil
中科院分区：
文献类型：
--
作者：
Prihar, Ethan;Haim, Aaron;Shen, Tracy;Sales, Adam;Lee, Dongwon;Wu, Xintao;Heffernan, Neil
许多在线学习平台和 MOOC 将一定量的基于视频的内容纳入其平台，但很少有随机对照实验来评估不同视频集成方法的有效性。鉴于大量公开的教育视频，调查这些内容对学生的影响可能有助于在学习平台中实现更有效、更易于访问的视频集成。在这项工作中，现有的在线学习平台添加了一项新功能，允许学生在完成在线中学数学作业的同时请求与技能相关的视频。共有 18,535 名学生参与了两项与向学生提供公开教育视频相关的大规模随机对照实验。第一个实验研究了为学生提供请求这些视频的机会的效果，第二个实验研究了使用多臂老虎机算法推荐相关视频的效果。此外，这项工作还调查了视频的哪些特征可以显着预测学生的表现，以及哪些特征可以用于个性化学生的学习。最终，学生们大多对与技能相关的视频不感兴趣，而是更喜欢使用平台现有的针对具体问题的支持，并且在这两个实验中都没有任何统计上显着的发现。此外，虽然没有视频特征能够显着预测学生的表现，但两个视频特征与学生的先验知识具有显着的定性交互作用，这表明不同的内容创建者对不同的学生群体更有效。这些发现可用于指导在线学习平台中未来基于视频的功能的设计，以及专门针对知识水平较高或较低的学生的不同教育视频的创建。这项工作中使用的数据和代码可以在 https://osf.io/cxkzf/ 找到。
Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effectiveness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content's impact on students could help lead to more effective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learning platform that allowed students to request skill-related videos while completing their online middle-school mathematics assignments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students' performance and which features could be used to personalize students' learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem-specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students' performance, two video features had significant qualitative interactions with students' prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work can be found at https://osf.io/cxkzf/.
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