Uncovering hidden engagement patterns for predicting learner performance in MOOCs

Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé, L. Getoor
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引用次数: 46

Abstract

Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.
揭示预测mooc学习者表现的隐性参与模式
保持和培养学生的参与度是mooc产生广泛教育影响的先决条件。随着课程的进展,了解学生的参与度有助于描述学生的学习模式,并有助于减少辍学率,启动教师干预。在本文中,我们构建了一个连接学生行为和课堂表现的概率模型,将学生参与类型作为潜在变量。我们表明,我们的模型确定了课程成功指标,教师可以使用这些指标来启动干预措施并帮助学生。
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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