Predicting student's appraisal of feedback in an ITS using previous affective states and continuous affect labels from EEG data

• Main Authors: Inventado, Paul Salvador B., Legaspi, Roberto S., Bui, The Duy, Suarez, Merlin Teodosia C.
• Format: text
• Published: Animo Repository 2010
• Subjects: Intelligent tutoring systems; Feedback (Psychology); Brain stimulation; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2373
• Institution: De La Salle University

Students have different ways of learning and have varied reactions to feedback. Thus, allowing a system to predict how students would appraise certain feedback gives it the capability to adapt to what would help a student learn better. This research focuses on the prediction of a student's appraisal of feedback provided in an intelligent tutoring system (ITS). A regression model for frustration and excitement is created to perform prediction. The frustration model was able to achieve a 0.724 correlation with a 0.164 RMSE and the excitement model was able to achieve 0.6 a correlation with a 0.189 RMSE. These results indicate the potential of using these models for allowing systems to adjust feedback automatically based on student's reactions while using an ITS.