Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms
This addresses data sparsity issues in educational platforms for personalized learning, but it is incremental as it refines an existing method.
The paper tackled the problem of standard adaptive strategies like Thompson Sampling underperforming in educational platforms due to sparse data, and introduced WAPTS, which improved content allocation and accelerated learning, enabling earlier identification of promising treatments in a learnersourcing scenario.
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.