Multidimensional Item Response Theory in the Style of Collaborative Filtering

Yoav Bergner; Peter F. Halpin; Jill-Jênn Vie

doi:10.1007/s11336-021-09788-9

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arXiv · 2301.00909

Multidimensional Item Response Theory in the Style of Collaborative Filtering

Yoav Bergner·Peter F. Halpin·Jill-Jênn Vie

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Abstract

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 (JML) 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 (MOOC). 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 exam in the course.

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Yoav Bergner, Peter F. Halpin, Jill-Jênn Vie. 2023-01-03. Multidimensional Item Response Theory in the Style of Collaborative Filtering. https://doi.org/10.1007/s11336-021-09788-9

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