TY - GEN
T1 - Can a teachable agent influence how students respond to competition in an educational game?
AU - Sjödén, Björn
AU - Lind, Mats
AU - Silvervarg, Annika
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Competition
KW - Educational game
KW - Social influence
KW - Teachable agent
U2 - 10.1007/978-3-319-61425-0_29
DO - 10.1007/978-3-319-61425-0_29
M3 - Paper in conference proceeding
AN - SCOPUS:85022190414
SN - 9783319614243
VL - 10331 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 347
EP - 358
BT - Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
PB - Springer
T2 - 18th International Conference on Artificial Intelligence in Education, AIED 2017
Y2 - 28 June 2017 through 1 July 2017
ER -