Maria Ofelia Z. San Pedro

Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School

Research shows that middle school is an important juncture for a student where he or she starts to be conscious about academic achievement and thinks about college attendance. It is already known that access to financial resources, family background, career aspirations and academic ability are indicative of a student’s choice to attend college; though these variables are interesting, they do not necessarily give sufficient actionable information to instructors or guidance counselors to intervene for individual students.


Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System

Csikszentmihalyi’s Flow theory states that a balance between challenge and skill leads to high engagement, overwhelming challenge leads to anxiety or frustration, and insufficient challenge leads to boredom. In this paper, we test this theory within the context of student interaction with an intelligent tutoring system. Automated detectors of student affect and knowledge were developed, validated, and applied to a large data set.