Multidimensional Item Response Theory in the Style of Collaborative Filtering

Yoav Bergner, Peter Halpin, Jill Jênn Vie

Research output: Contribution to journalArticlepeer-review


This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course. The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative “validation” of the factor model, using auxiliary information about the popularity of items consulted during an open-book examination in the course.

Original languageEnglish (US)
Pages (from-to)266-288
Number of pages23
Issue number1
StatePublished - Mar 2022


  • collaborative filtering
  • item response theory
  • joint maximum likelihood
  • machine learning
  • multidimensionality

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics


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