Analyzing Learner Affect in a Scenario-Based Intelligent Tutoring System (bibtex)
by Benjamin Nye, Shamya Karumbaiah, S. Tugba Tokel, Mark G. Core, Giota Stratou, Daniel Auerbach, Kallirroi Georgila
Abstract:
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.
Reference:
Analyzing Learner Affect in a Scenario-Based Intelligent Tutoring System (Benjamin Nye, Shamya Karumbaiah, S. Tugba Tokel, Mark G. Core, Giota Stratou, Daniel Auerbach, Kallirroi Georgila), In Proceedings of the International Conference on Artificial Intelligence in Education, Springer, 2017.
Bibtex Entry:
@inproceedings{nye_analyzing_2017,
	address = {Wuhan, China},
	title = {Analyzing {Learner} {Affect} in a {Scenario}-{Based} {Intelligent} {Tutoring} {System}},
	isbn = {978-3-319-61425-0},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-61425-0_60},
	doi = {https://doi.org/10.1007/978-3-319-61425-0_60},
	abstract = {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.},
	booktitle = {Proceedings of the {International} {Conference} on {Artificial} {Intelligence} in {Education}},
	publisher = {Springer},
	author = {Nye, Benjamin and Karumbaiah, Shamya and Tokel, S. Tugba and Core, Mark G. and Stratou, Giota and Auerbach, Daniel and Georgila, Kallirroi},
	month = jun,
	year = {2017},
	keywords = {Learning Sciences, Virtual Humans, UARC},
	pages = {544--547}
}
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