Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time

F. V. D. Sluis, Jasper Ginn, T. Zee
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引用次数: 42

Abstract

Understanding why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.
在尺度上解释学生行为:视频复杂性对学生停留时间的影响
了解学生与教育视频互动的原因和方式,对于进一步提高mooc的质量至关重要。在本文中,我们通过研究视频的复杂性来解释学生行为的两个相关方面:停留时间(学生花在观看视频上的时间)和停留率(他们实际看了多少视频)。基于心理语言学的强大传统,我们形式化了视频信息复杂性的定义。此外,基于任务时间测量的最新进展,我们基于点击流跟踪数据形式化了驻留时间和驻留率。由此产生的视频复杂性计算模型解释了22.44%的学生看完一段视频后停留率的差异。视频复杂度与学生居住呈多项式关系,高、低复杂度均增加学生居住。这些结果表明了为什么学生花更多的时间观看(可能是考虑)视频。此外,他们表明,即使是相当直接的学生行为指标,如居住,也可以有多种解释;说明了从学习分析中获得意义的挑战。
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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