A latent factor model for instructor content preference analysis

Existing personalized learning systems (PLSs) have primarily focused on providing learning analytics using data from learners. In this paper, we extend the capability of current PLSs by incorporating data from instructors. We propose a latent factor model that analyzes instructors’ preferences in explicitly excluding particular questions from learners’ assignments in a particular subject domain. We formulate the problem of predicting instructors’ question exclusion preferences as a matrix factorization problem, and incorporate expert-labeled Bloom’s Taxonomy tags on each question as a factor in our statistical model to improve model interpretability. Experimental results on a real-world educational dataset demonstrate that the proposed model achieves superior prediction performance compared to several other baseline methods commonly used in recommender systems. Additionally, by explicitly incorporating Bloom’s Taxonomy, the model provides meaningful interpretations that help understand why instructors exclude certain questions. Since instructor preference data contains their insights after years of teaching experience, our proposed model has the potential to further improve the question recommendations that PLSs make for learners.