This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our…

Background: The proliferation of computer-based learning platforms (CBLPs) has caused an increased focus on understanding how to build scalable systems that optimize learning. Thus, CBLPs often rely on close response questions (e.g., multiple choice questions, "select all that apply," "arrange in the correct order," etc.) for…

Randomized A/B tests within online learning platforms enable us to draw unbiased causal estimators. However, precise estimates of treatment effects can be challenging due to minimal participation, resulting in underpowered A/B tests. Recent advancements indicate that leveraging auxiliary information from detailed logs and employing design-based…