Interpretable Variational Autoencoders for Cognitive Models

Abstract

One of the most used methodologies in the field of education assessment is Item Response Theory (IRT). In this work, we propose the use of a novel Variational Autoencoder (VAE) architecture for a multidimensional IRT model. Our approach combines the advantages of the IRT model while allowing us to model high latent trait dimensions, previously unattainable in prior work. Additionally, it has the advantage of interpretability in the domain of educational assessment.Our experiments show that, given enough data, the new model is competitive with the state-of-the-art methods with respect to predictive power and is much faster in runtime performance. In our experiments, we achieve competitive results on a sample size 20× larger in a runtime that is 40× faster than the state-of-the- art model.