2012_06_ITS - Semi-Supervised Classification of Realtime Physiological Sensor Datastreams for Student Affect Assessment in Intelligent Tutoring
Abstract: Famously, individual expert tutoring holds the promise of two standard deviations of improvement over classroom-based instruction. Current content-scaling techniques have been able to prove one standard deviation of improvement. However, just as expert tutors take the motivation and emotional state of the student into account for instruction, so too must computer instructors. Differences between individuals and individual baselines make this difficult, but this information is known across one training session. The construction of assessing modules in realtime, from the available performance and sensor datastreams, skirts these problems, but is technically difficult. This research investigates automated student model construction in realtime from datastreams as a solution from which to base pedagogical strategy recommendations.
Brawner, K. W., Sottilare, R., & Gonzalez, A. (2012, June). Semi-Supervised classification of realtime physiological sensor datastreams for student affect assessment in intelligent tutoring. In International Conference on Intelligent Tutoring Systems (pp. 582-584). Springer, Berlin, Heidelberg.