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 p aper, 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. The results did not match Flow theory: boredom was more common for poorly -known material, and frustration was common both for very difficult material and very easy material. These results suggest that design for optimal engagement within online learning may require further study of the factors leading students to become bored on difficult material, and frustrated on very well-known material.