Climate and Space Science, College of Engineering
School of Information
The capacity to predict student success depends in part on our ability to understand “educationally purposeful” student behaviors and motivations and the relationship between behaviors and motivations and academic achievement. My research focuses on how to collect student behaviors germane to learning at a higher granularity and analyze the relationships between student performance and behaviors.
Ultimately this research is aimed at designing and constructing an “earlier warning system” wherein student guidance is quasi-automated and informed by motivation, background and behaviors and delivered within weeks of the beginning of classes.