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. 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.

Predicting STEM Career Choice... presentation at the 16th International Artificial Intelligence in Education Conference, Memphis, TN, July 2013. 


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Conference proceeding
Maria Ofelia Z. San Pedro
R. Baker
Sujith M. Gowda
Neil T. Heffernan
Artificial Intelligence in Education (AIED)
Informal Learning and Afterschool
Youth Motivation and Interests in STEM
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