Predictive learning analytics for video-watching behavior in MOOCs

Madhumitha Shridharan, Ashley Willingham, Jonathan Spencer, Tsung Yen Yang, Christopher Brinton

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

3 Scopus citations

Abstract

In this paper, we develop Predictive Learning Analytics (PLA) methodology for learner video-watching behavior in Massive Open Online Courses (MOOCs). After defining features to summarize such behavior from clickstream measurements, we perform a statistical analysis of a real-world MOOC dataset and uncover several interesting relationships between the different features. Motivated by this analysis, we propose three algorithms for predicting future video-watching behavior, which incorporate biases for learners and videos, collaborative filtering across videos, and regularization to reduce overfitting. Through evaluation on our dataset, we find that the predictors obtain low RMSE overall, and that augmenting the bias predictor with either collaborative filtering or regularization improves prediction quality in eight out of nine cases.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Publication series

Name2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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