Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder - Liu, Tianci; Wang, Chun; Xu, Gongjun (Frontiers in Psychology, 2022)
Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, one major challenge arising in MIRT applications is that many existing estimation methods become computationally demanding and not scalable to such big data. To address this issue, this work proposes an importance sampling enhanced Variational Autoencoder (VAE) approach for the estimation of the three- and four- parameter MIRT Models. The key idea is to adopt a variational inference procedure in machine learning literature to approximate the intractable marginal likelihood, and further use importance sampling to boost the trained VAE with a better log-likelihood approximation. Simulation studies are conducted to demonstrate the computational efficiency and scalability of the new algorithm in comparison to the popular alternative algorithms, i.e., Monte Carlo EM and Metropolis-Hastings Robbins-Monro methods. The good performance of the proposed method is also illustrated by a NAEP multistage testing dataset.