Work

Predicting Student’s Appraisal of Feedback in an ITS Using Previous Affective States and Continuous Affect Labels from EEG Data

Year: 2010

Type: conference-paper

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.

Source: International Conference on Computers in Education

DOI: 10.58459/icce.2010.41

Authors: Paul INVENTADO, Roberto LEGASPI, The Duy BUI, Merlin SUAREZ

Institutions: De La Salle University, The Institute of Scientific and Industrial Research, The University of Osaka, The University of Osaka

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: De La Salle University