深度知识追踪与mooc参与

Kritphong Mongkhonvanit, K. Kanopka, David Lang
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引用次数: 28

摘要

mooc和在线课程的流失率非常高[1]。一个挑战是,很难判断学生没有完成课程是因为不感兴趣还是因为课程困难。利用深度知识跟踪框架,我们通过包括课程交互协变量来解释学生参与度。有了这些,我们发现我们可以预测学生对下一个项目的反应,准确率超过88%。利用这些预测,可以向学生提供有针对性的干预措施,并对课程进行有针对性的改进。特别是,这种方法将允许对内容进行限制,直到学生有合理的成功可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Knowledge Tracing and Engagement with MOOCs
MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted interventions can be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding.
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