Sparse Factor Autoencoders for Item Response Theory

Item response theory (IRT) is a popular method to infer student abilities and item difficulties from observed test responses. However, IRT struggles with two challenges: How to map items to skills if multiple skills are present? And how to infer the ability of new students that have not been part of the training data? Inspired by recent advances in variational autoencoders for IRT, we propose a novel method to tackle both challenges: The Sparse Factor Autoencoder (SparFAE). SparFAE maps from test responses to abilities via a linear operator and from abilities to test responses via an IRT model. All parameters of the model offer an interpretation and can be learned in an efficient manner. In experiments on synthetic and real data, we show that SparFAE is similar in accuracy to other autoencoder approaches while being faster to learn and more accurate in recovering item-skill associations. [For the full proceedings, see ED623995.]