Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2018

Abstract

Affect inextricably plays a critical role in the learning process. In this study, we investigate the multimodal fusion of facial, keystrokes, mouse clicks, head posture and contextual features for the detection of student’s frustration in an Affective Tutoring System. The results (AUC=0.64) demonstrated empirically that a multimodal approach offers higher accuracy and better robustness as compared to a unimodal approach. In addition, the inclusion of keystrokes and mouse clicks makes up for the detection gap where video based sensing modes (facial and head postures) are not available. The findings in this paper will dovetail to our end research objective of optimizing the learning of students by adapting empathetically or tailoring to their affective states.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 29th Annual Workshop Psychology of Programming Interest Group, London, 2018 September 5-7

First Page

78

Last Page

85

City or Country

London

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