A Bandit-Based Approach to Educational Recommender Systems: Contextual Thompson Sampling for Learner Skill Gain Optimization

TL;DR

This paper presents a contextual Thompson sampling bandit approach for personalized educational recommendations, optimizing learner skill gain by selecting exercises that maximize individual learning progress in digital learning environments.

Contribution

It introduces a novel bandit-based method that personalizes exercise recommendations using learner data to enhance skill development and adapt to individual differences.

Findings

Recommends exercises linked to greater skill improvement

Effectively adapts to diverse learner needs

Helps identify learners needing additional support

Abstract

In recent years, instructional practices in Operations Research (OR), Management Science (MS), and Analytics have increasingly shifted toward digital environments, where large and diverse groups of learners make it difficult to provide practice that adapts to individual needs. This paper introduces a method that generates personalized sequences of exercises by selecting, at each step, the exercise most likely to advance a learner's understanding of a targeted skill. The method uses information about the learner and their past performance to guide these choices, and learning progress is measured as the change in estimated skill level before and after each exercise. Using data from an online mathematics tutoring platform, we find that the approach recommends exercises associated with greater skill improvement and adapts effectively to differences across learners. From an instructional perspective, the framework enables personalized practice at scale, highlights exercises with consistently strong learning value, and helps instructors identify learners who may benefit from additional support.