This study measures the effectiveness of including images to predict discrimination parameters of reading items. The results suggest that providing images to language models is beneficial. By using both image and text, Lasso regressions (Tibshirani, 1996) could explain data better and random forests (Breiman, 2001) showed higher accuracy.

Only a limited number of predictors can be included in a generalized linear mixed model (GLMM) due to estimation algorithm divergence. This study aims to propose a machine learning based algorithm (e.g., random forest) that can consider all predictors without the convergence issue and automatically searches for the optimal GLMMs.