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
Citation
FWA, Hua Leong and MARSHALL, Lindsay.
Investigating multimodal affect sensing in an affective tutoring system using unobtrusive sensors. (2018). Proceedings of the 29th Annual Workshop Psychology of Programming Interest Group, London, 2018 September 5-7. 78-85.
Available at: https://ink.library.smu.edu.sg/sis_research/7060
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.