Predictive Modeling in Online Learning Environments
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Predictive Modeling in Online Learning Environments

  • Author(s): Arabshahi, Forough;
  • Huang, Furong;
  • Anandkumar, Animashree;
  • Butts, Carter T.
  • et al.
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Abstract— In this work we study scalable probabilistic modeling and prediction for predicting student performance in a Massive Open Online Course (MOOC). Students’ performance sequence form a high dimensional multivariate time series whose joint prediction is a challenging task. We solve the problem through the discovery of hierarchical latent groups that influence the dynamics of the time series. We introduce a Conditional Latent Tree Model (CLTM), in which the latent variables incorporate the unknown groups. The latent tree itself is conditioned on observed covariates such as seasonality, past activity and node attributes. We propose a statistically efficient framework for learning the hierarchical tree structure, and the parameters of the CLTM. We demonstrate competitive performance compared to the baseline (chain CRF) that does not use the hierarchical latent groupings for prediction. Our modeling framework also provides valuable and interpretable information about the hidden group structures and their effect on the evolution of the time series.

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Faculty Advisor:  Animashree Anandkumar, Carter T. Butts

Data Science Initiative, University of California Irvine, May 2015

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Data Science Initiative, University of California Irvine, May 2015