Predicting Student’s Appraisal of Feedback in an ITS Using Previous Affective States and Continuous Affect Labels from EEG Data
Paul Salvador Inventado, Roberto Legaspi, The Duy BUI, Merlin Teodosia Suarez · International Conference on Computers in Education · 2010
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.