大规模在线开放课程辍学预测的时间分析
Massive Open Online Courses Temporal Profiling for Dropout Prediction
摘要：
大规模在线开放课程（MOOC）正在吸引全世界人们的关注。无论是哪个平台，在线课程的注册人数都令人印象深刻，但与此同时，完成率却令人失望。基于学习者个人资料了解辍学机制是MOOC的一项关键任务，因为它将允许在正确的时刻进行干预，以帮助学习者完成课程。在本文中，学习者在MOOC的辍学行为进行了深入研究，首先提取的功能，描述学习者的行为在课程中，然后通过比较三个分类器（逻辑回归，随机森林和AdaBoost）在两个任务：预测用户将在某一个星期退出，并预测用户将退出在特定的一周。前者已经被证明是相当容易的，所有三个分类器都表现得同样好。然而，第二个任务的准确性较低，Logistic回归往往比其他两个算法的性能略好。我们发现，反映用户对MOOC的积极态度的功能，例如提交作业，在论坛上发帖和填写个人资料，是持久性的强有力指标。
英文摘要：
Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
