MOOC performance prediction via clickstream data and social learning networks

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

77 Scopus citations

Abstract

We study student performance prediction in Massive Open Online Courses (MOOCs), where the objective is to predict whether a user will be Correct on First Attempt (CFA) in answering a question. In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms. Using video-watching clickstream data from one of our MOOCs, we first extract summary quantities (e.g., fraction played, number of pauses) for each user-video pair, and show how certain intervals/sets of values for these behaviors quantify that a pair is more likely to be CFA or not for the corresponding question. Motivated by these findings, our methods are designed to determine suitable intervals from training data and to use the corresponding success estimates as learning features in prediction algorithms. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the 'early detection' capability of such clickstream data. We also discuss how CFA prediction can be used to depict graphs of the Social Learning Network (SLN) of students, which can help instructors manage courses more effectively.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2299-2307
Number of pages9
ISBN (Electronic)9781479983810
DOIs
StatePublished - Aug 21 2015
Event34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong
Duration: Apr 26 2015May 1 2015

Publication series

NameProceedings - IEEE INFOCOM
Volume26
ISSN (Print)0743-166X

Other

Other34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
CountryHong Kong
CityHong Kong
Period4/26/155/1/15

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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  • Cite this

    Brinton, C. G., & Chiang, M. (2015). MOOC performance prediction via clickstream data and social learning networks. In 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015 (pp. 2299-2307). [7218617] (Proceedings - IEEE INFOCOM; Vol. 26). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2015.7218617