Can a teachable agent influence how students respond to competition in an educational game?
(2017) 18th International Conference on Artificial Intelligence in Education, AIED 2017 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10331 LNAI. p.347-358- Abstract
Learning in educational games is often associated with some form of competition. We investigated how students responded to winning or losing in an educational math game, with respect to playing with or without a Teachable Agent (TA). Students could choose between game modes in which the TA took a more passive or active role, or let the TA play a game entirely on its own. Based on the data logs from 3983 games played by 163 students (age 10–11), we analyzed data on students’ persistence, challenge-seeking and performance during gameplay. Results indicated that students showed greater persistence when playing together with the TA, by more often repeating a lost game with the TA, than a lost game after playing alone. Students’... (More)
Learning in educational games is often associated with some form of competition. We investigated how students responded to winning or losing in an educational math game, with respect to playing with or without a Teachable Agent (TA). Students could choose between game modes in which the TA took a more passive or active role, or let the TA play a game entirely on its own. Based on the data logs from 3983 games played by 163 students (age 10–11), we analyzed data on students’ persistence, challenge-seeking and performance during gameplay. Results indicated that students showed greater persistence when playing together with the TA, by more often repeating a lost game with the TA, than a lost game after playing alone. Students’ challenge-seeking, by increasing the difficulty level, was greater following a win than following a loss, especially after the TA won on its own. Students’ gameplay performance was unaffected by their TA winning or losing but was, unexpectedly, slightly worse following a win by the student alone. We conclude that engaging a TA can make students respond more productively to both winning and losing, depending on the particular role the TA takes in the game. These results may inform more specific hypotheses as to the differential effects of competing and collaborating in novel, AI-supported social constellations, such as with TAs, on students’ motivation and ego-involvement in educational games.
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- author
- Sjödén, Björn LU ; Lind, Mats and Silvervarg, Annika
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Competition, Educational game, Social influence, Teachable agent
- host publication
- Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- volume
- 10331 LNAI
- pages
- 12 pages
- publisher
- Springer
- conference name
- 18th International Conference on Artificial Intelligence in Education, AIED 2017
- conference location
- Wuhan, China
- conference dates
- 2017-06-28 - 2017-07-01
- external identifiers
-
- scopus:85022190414
- ISSN
- 03029743
- 16113349
- ISBN
- 9783319614243
- DOI
- 10.1007/978-3-319-61425-0_29
- language
- English
- LU publication?
- yes
- id
- 4f69489c-6c80-4757-bc1f-98b9c37b19fc
- date added to LUP
- 2017-07-24 15:12:31
- date last changed
- 2024-02-29 18:53:10
@inproceedings{4f69489c-6c80-4757-bc1f-98b9c37b19fc,
abstract = {{<p>Learning in educational games is often associated with some form of competition. We investigated how students responded to winning or losing in an educational math game, with respect to playing with or without a Teachable Agent (TA). Students could choose between game modes in which the TA took a more passive or active role, or let the TA play a game entirely on its own. Based on the data logs from 3983 games played by 163 students (age 10–11), we analyzed data on students’ persistence, challenge-seeking and performance during gameplay. Results indicated that students showed greater persistence when playing together with the TA, by more often repeating a lost game with the TA, than a lost game after playing alone. Students’ challenge-seeking, by increasing the difficulty level, was greater following a win than following a loss, especially after the TA won on its own. Students’ gameplay performance was unaffected by their TA winning or losing but was, unexpectedly, slightly worse following a win by the student alone. We conclude that engaging a TA can make students respond more productively to both winning and losing, depending on the particular role the TA takes in the game. These results may inform more specific hypotheses as to the differential effects of competing and collaborating in novel, AI-supported social constellations, such as with TAs, on students’ motivation and ego-involvement in educational games.</p>}},
author = {{Sjödén, Björn and Lind, Mats and Silvervarg, Annika}},
booktitle = {{Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings}},
isbn = {{9783319614243}},
issn = {{03029743}},
keywords = {{Competition; Educational game; Social influence; Teachable agent}},
language = {{eng}},
pages = {{347--358}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Can a teachable agent influence how students respond to competition in an educational game?}},
url = {{http://dx.doi.org/10.1007/978-3-319-61425-0_29}},
doi = {{10.1007/978-3-319-61425-0_29}},
volume = {{10331 LNAI}},
year = {{2017}},
}