# Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits

arXiv: https://arxiv.org/abs/2207.14003 · HTML: https://mlsanity.com/paper/2207.14003/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Multi-Armed Bandits for Intelligent Tutoring Systems](https://mlsanity.com/paper/1310.3174/) (2013)
- [A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM](https://mlsanity.com/paper/2005.06616/) (2020)

## Built on by (most cited first, 1 of 1)

- [PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training](https://mlsanity.com/paper/2603.05361/) (2026)

## Abstract (arXiv)

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.