Sparse factor analysis for learning and content analytics

Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question's intrinsic difficulty. We estimate these factors given the graded responses to a collection of questions. The underlying estimation problem is ill-posed in general, especially when only a subset of the questions are answered. The key observation that enables a well-posed solution is the fact that typical educational domains of interest involve only a small number of key concepts. Leveraging this observation, we develop both a bi-convex maximum-likelihood-based solution and a Bayesian solution to the resulting SPARse Factor Analysis (SPARFA) problem. We also incorporate user-defined tags on questions to facilitate the interpretability of the estimated factors. Experiments with synthetic and real-world data demonstrate the efficacy of our approach. Finally, we make a connection between SPARFA and noisy, binary-valued (1-bit) dictionary learning that is of independent interest.

Original languageEnglish (US)
Pages (from-to)1959-2008
Number of pages50
JournalJournal of Machine Learning Research
StatePublished - Jun 2014


  • Bayesian latent factor analysis
  • Factor analysis
  • Personalized learning
  • Sparse logistic regression
  • Sparse probit regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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