Proceedings of the Annual Meeting of the Cognitive Science Society

Decades of research have demonstrated that students facecritical conceptual challenges in learning mathematics. Asnew adaptive learning technologies become ubiquitous ineducation, they bring opportunities both to facilitateconceptual development in more focused ways and to gatherdata that may yield new insights into students’ learningprocesses. The present study analyzes data archives from aperceptual learning intervention designed to help studentsmaster key concepts related to linear measurement andfractions. Using algorithmic data coding on a database of78,034 errors from a sample of sixth graders, both conceptualerrors and other errors were captured and analyzed for changeover time. Results indicate that conceptual errors decreasedsignificantly. This approach suggests additional ways thatsuch datasets can be exploited to better understand how thesoftware impacts different students and how next generationsof adaptive software may be designed to code and respond tocommon error patterns in real time.