Behavior Prediction in MOOCs using Higher Granularity Temporal Information

Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady
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引用次数: 30

Abstract

In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.
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基于高粒度时间信息的慕课行为预测
在本文中,我们提出了早期的研究,评估了在课程开始时具有不同行为的学生亚群中各种时间特征的预测能力。初步结果表明,这些特征预测了亚群之间和课程中不同时间的重要差异。最终,这些结果对有效地针对mooc中亚群体的特定意图和目标定制适应性脚手架具有启示意义。
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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