Personalized learning (PL) is a student-centered instructional approach in which learning goals, pacing, content, and environments are customized to address individual student needs (Bernacki et al., 2021; Ellis, 2009; Lee, 2014; Miliband, 2006; Office of Educational Technology, 2010; Sota, 2016; Zhang et al., 2020). In grades K-12, PL has been…

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…

Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…

The framework and tools used for classroom assessment can have significant impacts on teacher practices and student achievement. Getting assessment right is an important component in creating positive learning experiences and academic success. Recent government reports (e.g., United States, Australia) call for the development of systems that use…

Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system…