Predicting Student Examinee Rate in Massive Open Online Courses

  • Wei Lu
  • Tongtong Wang
  • Min Jiao
  • Xiaoying Zhang
  • Shan Wang
  • Xiaoyong Du
  • Hong Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10179)

Abstract

Over the past few years, massive open online courses (a.b.a MOOCs) has rapidly emerged and popularized as a new style of education paradigm. Despite various features and benefits offered by MOOCs, however, unlike traditional classroom-style education, students enrolled in MOOCs often show a wide variety of motivations, and only quite a small percentage of them participate in the final examinations. To figure out the underlying reasons, in this paper, we make two key contributions. First, we find that being an examinee for a learner is almost a necessary condition of earning a certificate and hence investigation of the examinee rate prediction is of great importance. Second, after conducting extensive investigation of participants’ operation behaviours, we carefully select a set of features that are closely reflect participants’ learning behaviours. We apply existing commonly used classifiers over three online courses, generously provided by China University MOOC platform, to evaluate the effectiveness of the used features. Based on our experiments, we find there does not exist a single classifier that is able to dominate others in all cases, and in many cases, SVN performs the best.

Keywords

MOOC Machine learning methods Examinee rate Prediction 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wei Lu
    • 1
    • 2
  • Tongtong Wang
    • 1
    • 2
  • Min Jiao
    • 1
    • 2
  • Xiaoying Zhang
    • 1
    • 2
  • Shan Wang
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  • Hong Chen
    • 1
    • 2
  1. 1.Key Laboratory of Data Engineering and Knowledge Engineering, MOERenmin University of ChinaBeijingChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina

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