Abstract
In order to support and promote new ways of learning, educational technology should be based on sophisticated theories and models of learning. Many issues are raised in the current understanding of learning by the constant evolution of educational technology and the burgeoning of educational contexts using these technologies. By examining the relation between agency and learning gains using a Serious Game for learning Physics, the present study focuses on a main issue of technology use: whether actively playing the game or watching someone play is beneficial for learning. Thirty-seven dyads participated in the study. Randomly assigned, one participant played a Serious Educational Game for learning Physics, Mecanika (Boucher-Genesse et al. 2011), for 120 min, while the other participant watched the player’s gameplay in real-time on a separate screen. As pretest and posttest, the Force Concept Inventory (FCI; Hestenes et al. 1992) was administered to measure learning gains in Physics. Analyses of answers on the FCI demonstrate that a Serious Game, such as Mecanika, is beneficial to learning, regardless if learning is conceived as relatively coarse shifts from wrong to good answers (scientific conceptions) or as more nuanced shifts from fillers/misconceptions to scientific conceptions. Also, individual differences in learning gains across dyads were found, which can be explained by the gameplay of a dyad created by the active player. Furthermore, the effect of agency is systematic and not modulated by individual differences: watchers learn more than players. These results need to be further explained by modeling the learning process.
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References
Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., Fernández-Manjón, B.: Applications of data science to game learning analytics data: a systematic literature review. Comput. Educ. 141 (2019). https://doi.org/10.1016/j.compedu.2019.103612
Boucher-Genesse, F., Riopel, M., Potvin, P.: Research results for Mecanika, a game to learn Newtonian concepts. In: Games, Learning and Society Conference proceedings, pp. 31–38, Madison, Wisconsin (2011)
Stephanidis, C., Salvendy, G., et al.: Seven HCI grand challenges. Int. J. Hum.-Comput. Interact. 35(14), 1229–1269 (2019). https://doi.org/10.1080/10447318.2019.1619259
Clark, A.: Expecting the world: perception, prediction, and the origins of human knowledge. J. Philos. 110(9), 469–496 (2013)
Hestenes, D., Wells, M., Swackhamer, G.: Force concept inventory. Phys. Teach. 30, 141–158 (1992)
Lamb, R.L., Annetta, L., Firestone, J., Etopio, E.: A meta-analysis with examination of moderators of student cognition, affect, and learning outcomes while using serious educational games, serious games, and simulations. Comput. Hum. Behav. 80, 158–167 (2018)
Lupyan, G., Clark, A.: Words and the world: predictive coding and the language-perception-cognition interface. Curr. Dir. Psychol. Sci. 24(4), 279–284 (2015)
Mercier, J., Avaca, I.L., Whissell-Turner, K., Paradis, A., Mikropoulos, T.A.: Agency affects learning outcomes with a serious game. In: 22nd International Conference on Human-Computer Interaction, Copenhagen, Denmark (2020, in press)
SAS Institute Inc.: SAS/ACCESS® 9.4 Interface to ADABAS: Reference. SAS Institute Inc., Cary (2013)
Shute, V.: Stealth assessment in computer-based games to support learning. In: Tobias, S., Fletcher, J.D. (eds.) Computer Games and Instruction, pp. 503–523. Information Age Publishing, Charlotte (2011)
Westera, W.: Simulating serious games: a discrete-time computational model based on cognitive flow theory. Interact. Learn. Environ. 26(4), 539–552 (2018)
Acknowledgements
The authors would like to acknowledge the important technical development work of Anthony Hosein Poitras Lowen underlying the study reported in this paper. The study was conducted with the support of the Social Sciences and Humanities Research Council of Canada and the Canada Foundation for Innovation.
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Mercier, J., Whissell-Turner, K., Paradis, A., Avaca, I.L., Riopel, M., Bédard, M. (2020). Do Individual Differences Modulate the Effect of Agency on Learning Outcomes with a Serious Game?. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. Human and Technology Ecosystems. HCII 2020. Lecture Notes in Computer Science(), vol 12206. Springer, Cham. https://doi.org/10.1007/978-3-030-50506-6_19
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