Projects

Collaborative Research: Advancing the Science of STEM Interest Development through Educational Gameplay with Machine Learning and Data-driven Interviews

NSF ECR #2301173

The integration of digital games into STEM education has been an active area of research for quite some time, but details about how students' interactions with educational games may or may not reflect their interest is more difficult to obtain. This project will use a Minecraft-based simulation environment to advance understanding of how educational digital games can support the development of enduring STEM interest. Middle school students in summer and afterschool camps will experiment with a variety of scientific topics in the What-If Hypothetical Implementations in Minecraft (WHIMC) learning system while researchers interview them at key points in their gameplay to better understand how their interest is developing. In this way, the project will contextualize how decisions made by students while engaging with the educational game are related to their prior STEM interest and how they may, in turn, influence the development of enduring STEM interest. This work will contribute advanced tools and methodological resources for studying STEM learning and interest that will help broaden participation in STEM.

Exploring Algorithmic Fairness and Potential Bias in K-12 Mathematics Adaptive Learning

NSF ECR #2000638

Adaptive learning offers an opportunity to provide high quality instruction that is personalized to the needs of individual learners, but little is known about who benefits most from adaptive learning technologies. This project will investigate potential ways in which adaptive learning software might be biased against students from certain groups, and how such biases can be reduced. This project will address this issue by observing and interviewing students who use adaptive math learning software to discover what aspects of their identity are most salient in the adaptive learning context. This project will then investigate possible algorithmic biases related to the identities that students express. Findings from the project will contribute to understanding of the most relevant aspects of student identity in adaptive learning contexts, and how those identities affect their learning experience. Finally, this project will address the biases that are identified, thereby providing a more equitable mathematics education experience for students.

Investigating the Role of Interest in Middle Grade Science with a Multimodal Affect-Sensitive Learning Environment

NSF Cyberlearning #IIS-2016993

This project investigates the role of affect in the development of student interest in middle school science. Specifically, it will investigate how established measures of student interest relate to in-the-moment experiences of epistemic emotions. This research takes place within the context of Crystal Island, a game-based environment aligned to middle school microbiology standards. By combining established BROMP-based methods for collecting data on student affective states with newer methods, we are better able to understand how these feelings emerge and what educators and developers might be able to do support students who are disengaged.