Abstract
Teamwork is widely used in higher education for its learning benefits, but students often face issues while working in teams. A peer assessment tool can detect such issues and simultaneously provide support: this could be done by adding a virtual agent throughout the survey, but what should this agent say? This research describes two studies to inform the design of such an agent. We investigate the adaptation of support statements to a student filling in a peer assessment tool. This adaptation is based on the rater’s Emotional Stability and Conscientiousness, and the score assigned to a teammate’s quality of contribution. This adaptation is summarized in an algorithm for a peer assessment tool.
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Notes
- 1.
The stories are in supplementary materials: https://doi.org/10.5281/zenodo.10877474.
- 2.
A statement was reliably categorized with Free-Marginal Kappa \(\ge \) 0.4(\(\kappa \))[14].
- 3.
A: F(4,290) = 16.43; C: F(4,290) = 51.69; E: F(4,290) = 15.99; S: F(4,290) = 12.21.
- 4.
A: F(4,290) = 15.46, C: F(4,290) = 53.29, E: F(4,290) = 11.61, S: F(4,290) = 13.39.
- 5.
When a score is in multiple subsets, we use the subsets combination’s average.
- 6.
There are two complex cases. For Sc 4 - ES High MT is 2.5 which can be 2 or 3 statements. For similarity with ES Low, A C is chosen over A 2C. For Sc 3 - ES High, the medium is 3, but 2A is chosen over 2A S as it is more similar to A, the choice for low ES. S came from combining two subsets of which one had no S.
- 7.
For Sc 1,2 excluding E for High, Low Con is plausible given comments that High Con may mean too high expectations and Low Con may mean unreliable ratings and bad work by Alex themselves.
- 8.
Which could include selecting multiple times the same subcategory.
- 9.
For Sc 5, Low Con and High and Medium ES, the preferred order is A-C-C, but C-A-A was used only once less and is preferred for consistency.
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Saccardi, I., Masthoff, J. (2024). Adapting Emotional Support in Teams: Quality of Contribution, Emotional Stability and Conscientiousness. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science(), vol 14830. Springer, Cham. https://doi.org/10.1007/978-3-031-64299-9_31
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