Monotonic Variational AutoEncoder based Individually Optimized Problem Recommender System

Abstract

We propose a novel problem recommender system that can suggest moderately challenging problems to learners. By training a Variational AutoEncoder to reconstruct problem-answer data with a small number of latent variables, we can predict the likelihood of a learner's ability to correctly solve unanswered problems. Experimental results showed that the system's predictions were accurate for learners who had solved a sufficient number of problems, even for a wide variety of problems, and that the system was able to recommend problems of moderate difficulty for individual learners.