Modeling MOOC student behavior with two-layer hidden markov models

Chase Geigle, Cheng Xiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. In this paper, we propose a method for automatically discovering student behavior patterns by leveraging the click log data that can be obtained from the MOOC platform itself in a completely unsupervised manner.

Original languageEnglish (US)
Title of host publicationL@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages205-208
Number of pages4
ISBN (Electronic)9781450344500
DOIs
StatePublished - Apr 12 2017
Event4th Annual ACM Conference on Learning at Scale, L@S 2017 - Cambridge, United States
Duration: Apr 20 2017Apr 21 2017

Publication series

NameL@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale

Other

Other4th Annual ACM Conference on Learning at Scale, L@S 2017
CountryUnited States
CityCambridge
Period4/20/174/21/17

Keywords

  • Hidden markov models
  • MOOC log analysis
  • Markov models
  • Student behavior modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Education
  • Software
  • Computer Science Applications

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  • Cite this

    Geigle, C., & Zhai, C. X. (2017). Modeling MOOC student behavior with two-layer hidden markov models. In L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale (pp. 205-208). (L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale). Association for Computing Machinery, Inc. https://doi.org/10.1145/3051457.3053986