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…

An online learning system has the capacity to offer customized content that caters to individual learner's need and has seen growing interest from industry and academia alike in recent years. Different from traditional computerized adaptive testing setting which has a well-calibrated item bank with new items periodically added, online learning…

Bringing effective, research-based literacy interventions into the classroom is challenging, especially given the cultural and linguistic diversity of today's classrooms. We examined the promise of Assessment-to-Instruction (A2i) technology redesigned to be used at scale to support teachers' implementation of the individualized student instruction…