# Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms

arXiv: https://arxiv.org/abs/2501.03999 · HTML: https://mlsanity.com/paper/2501.03999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Online Multi-Armed Bandits with Adaptive Inference](https://mlsanity.com/paper/2102.13202/) (2021)
- [Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task](https://mlsanity.com/paper/2304.04933/) (2023)
- [Lenient Regret for Multi-Armed Bandits](https://mlsanity.com/paper/2008.03959/) (2020)
- [Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments](https://mlsanity.com/paper/2103.12198/) (2021)
- [Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation](https://mlsanity.com/paper/2306.06386/) (2023)
- [Satisficing Regret Minimization in Bandits: Constant Rate and Light-Tailed Distribution](https://mlsanity.com/paper/2406.06802/) (2024)

## Abstract (arXiv)

Adaptive experimentation is increasingly used in educational platforms to personalize learning through dynamic content and feedback. However, standard adaptive strategies such as Thompson Sampling often underperform in real-world educational settings where content variations are numerous and student participation is limited, resulting in sparse data. In particular, Thompson Sampling can lead to imbalanced content allocation and delayed convergence on which aspects of content are most effective for student learning. To address these challenges, we introduce Weighted Allocation Probability Adjusted Thompson Sampling (WAPTS), an algorithm that refines the sampling strategy to improve content-related decision-making in data-sparse environments. WAPTS is guided by the principle of lenient regret, allowing near-optimal allocations to accelerate learning while still exploring promising content. We evaluate WAPTS in a learnersourcing scenario where students rate peer-generated learning materials, and demonstrate that it enables earlier and more reliable identification of promising treatments.