Analyzing Learner Affect in a Scenario-Based Intelligent Tutoring System

Date

2017-07-01

Author

Nye, Benjamin
Karumbaiah, Shamya
Tokel, Saniye Tuğba
Core, Mark G.
Stratou, Giota
Auerbach, Daniel
Georgila, Kallirroi

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Scenario-based tutoring systems influence affective states due to two distinct mechanisms during learning: (1) reactions to performance feedback and (2) responses to the scenario context or events. To explore the role of affect and engagement, a scenario-based ITS was instrumented to support unobtrusive facial affect detection. Results from a sample of university students showed relatively few traditional academic affective states such as confusion or frustration, even at decision points and after poor performance (e.g., incorrect responses). This may show evidence of "over-flow," with a high level of engagement and interest but insufficient confusion/disequilibrium for optimal learning.

URI

https://hdl.handle.net/11511/39815

DOI

https://doi.org/10.1007/978-3-319-61425-0_60

Conference Name

18th International Conference on Artificial Intelligence in Education (AIED)

Collections

Department of Computer Education and Instructional Technology, Conference / Seminar

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Citation Formats

B. Nye et al., “Analyzing Learner Affect in a Scenario-Based Intelligent Tutoring System,” Cent China Normal Univ, Wuhan, PEOPLES R CHINA, 2017, vol. 10331, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39815.