I am a PhD Candidate at Carnegie Mellon University's Human Computer Interaction Institute. I am advised by Ken Koedinger, and my work focuses on instrumenting learning environments to encode student motivations.
In my work, I explore how we can instrument learning environments to measure students’ motivational and cognitive needs using just log data. Every time a student gets frustrated with an assignment and quits or eagerly jumps into a new assignment, their motivations show through, and educational technology can create a window to peak into these underlying motivations. I am interested in developing and applying methods that leverage these motivationally challenging moments to build valid measures of facets of students motivation. I am also interested in how to make building such models more scalable through the use of AI. I believe log-based models of student's latent motivations can enable a new dimension of personalization that recognizes and supports the whole child.