Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits
This addresses the challenge of personalizing learning experiences for students, though it is incremental as it builds on existing adaptive tutoring methods.
The paper tackled the problem of low student completion rates in learning systems by using an adaptive curriculum with contextual bandits, resulting in superior completion rates and significantly improved student engagement in a randomized controlled trial.
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.