Classifying Learner Engagement through Integration of Multiple Data Sources.

Intelligent tutoring systems (ITS) can provide effective instruction, but learners do not always use such systems effectively. In the present study, high school students' action sequences with a mathematics ITS were machine-classified into five finite-state machines indicating guessing strategies, appropriate help use, and independent problem solving; over 90% of problem events were categorized. Students were grouped via cluster analyses based on self reports of motivation. Motivation grouping predicted ITS strategic approach better than prior math achievement (as rated by classroom teachers). Learners who reported being disengaged in math were most likely to exhibit appropriate help use while working with the ITS, relative to average and high motivation learners. The results indicate that learners can readily report their motivation state and that these data predict how learners interact with the ITS.