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Proactive Student Persistence Prediction in MOOCs via Multi-Domain Adversarial Learning

EasyChair Preprint no. 7024

14 pagesDate: November 10, 2021


Automatic evaluation of a student’s STEM learning profile to understand her persistence is of national interest. In this paper, we propose an early ``dropout" prediction model that can identify the potentially `marginalized' student learning patterns to facilitate early instructional intervention in Massive Open Online Courses (MOOC) learning platform. Note that in the MOOC setting, building a comprehensive learning profile of the students is particularly more challenging due to the lack of available information and constrained communication modes. Unlike most existing works, which ignore these environmental constraints of missing information to formulate an over-simplified problem of `one-time' prediction task in a supervised setting, the proposed model introduces a continual automated monitoring and proactive estimation process, which transforms its decision making capacity over time with evolving data patterns. In a semi-supervised scenario, the Multi-Domain Adversarial Feature Representation (mDAFR) strategy promotes the emergence of features, which are discriminative for the main learning task, while remaining largely invariant to the data sources (course from which the data was captured) in consideration. This ensures an enhanced distributed learning capacity over different course environments. Compared to transfer learning, mDAFR reports 11-15% improved classification accuracy in KDDCup dataset, and demonstrates a competitive performance against several state-of-the-art methods in both KDDCup and MOOCDropout datasets.

Keyphrases: adversarial feature representation, Adversarial Learning, Classification, course specific learning activity, Domain Adaptation, feature representation, KDDCUP dataset, learning activity, MOOC, multi domain adversarial, Multi-feature learning, student activity detail, target domain, Transfer Learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sreyasee Das Bhattacharjee and Junsong Yuan},
  title = {Proactive Student Persistence Prediction in MOOCs via Multi-Domain Adversarial Learning},
  howpublished = {EasyChair Preprint no. 7024},

  year = {EasyChair, 2021}}
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