Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits

TL;DR

This paper introduces an adaptive intelligent tutoring system using contextual bandits to personalize learning activities, resulting in higher completion rates and engagement through continuous online learning and automation.

Contribution

It presents a novel model-based reinforcement learning approach with contextual bandits for adaptive curriculum personalization in education.

Findings

Higher student exercise completion rates

Significant improvement in student engagement

Effective online adaptation to new activities

Abstract

We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unlocking new opportunities for learning experience personalization.