Behavior in social learning networks: Early detection for online short-courses

Weiyu Chen, Christopher G. Brinton, Da Cao, Mung Chiang

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

7 Scopus citations


We study learning outcome prediction for online courses. Whereas prior work has focused on semester-long courses with frequent student assessments, we focus on short-courses that have single outcomes assigned by instructors at the end. The lack of performance data makes the behavior of learners, captured as they interact with course content and with one another in Social Learning Networks (SLN), essential for prediction. Our method defines several (machine) learning features based on behaviors collected on the modes of (human) learning in a course, and uses them in appropriate classifiers. Through evaluation on data captured from three two-week courses hosted through our delivery platforms, we make three key observations: (i) behavioral data is predictive of learning outcomes in short-courses (our classifiers achieving AUCs ≥ 0.8 after the two weeks), (ii) it has an early detection capability (AUCs ≥ 0.7 with the first week of data), and (iii) the content features have an 'earliest' detection capability (with higher AUC in the first few days), while the SLN features become the more predictive set over time, as the network matures. We also discuss how our method can generate behavioral analytics for instructors.

Original languageEnglish (US)
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053360
StatePublished - Oct 2 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Publication series

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


Other2017 IEEE Conference on Computer Communications, INFOCOM 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


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