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

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings of the 18th International Conference on Computers in Education: Enhancing and Sustaining New Knowledge Through the Use of Digital Technology in Education, ICCE 2010

First Page

71

Last Page

75

Publication Date

12-1-2010

Abstract

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.

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Disciplines

Computer Sciences

Keywords

Intelligent tutoring systems; Feedback (Psychology); Brain stimulation

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