This paper studies the use of Reinforcement Learning (RL) policies for optimizing the sequencing of online learning materials to students. Our approach provides an end to end pipeline for automatically deriving and evaluating robust representations of students' interactions and policies for content sequencing in online educational settings. We…

Previous research in cognitive diagnostic assessment (CDA) of L2 reading ability has been frequently conducted using large-scale English proficiency exams (e.g., TOEFL, MELAB). Using CDA, it is possible to analyze individual learners' strengths and weaknesses in multiple attributes (i.e., knowledge, skill, strategy) measured at the item level.…

Probabilistic models with more than one latent variable are designed to report profiles of skills or cognitive attributes. Testing programs want to offer additional information beyond what a single test score can provide using these skill profiles. Many recent approaches to skill profile models are limited to dichotomous data and have made use of…