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…

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…

To meet the goal of understanding students' complex learning processes and maximizing their learning outcomes, the field of learning analytics delves into the myriad of data captured as students use computer assisted learning platforms. Although many platforms associated with learning analytics focus on students' performance, performance on…

For researchers seeking to improve education, a common goal is to identify teaching practices that have causal benefits in classroom settings. To test whether an instructional practice exerts a causal influence on an outcome measure, the most straightforward and compelling method is to conduct an experiment. While experimentation is common in…

Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature…