Abstract
Many learning environments are swiftly abandoned by the learners, even if they are effective. Gamification is as a recent game-based learning approach that can enhance the learners’ motivation. However, individual expectations and preferences towards game-like features may be very different from one person to another. This paper presents a model to adapt gamification features according to a player profile of the learners. Two version of this model are evaluated within a gamified online learning environment. The first version comes from experts’ judgment, and the second one is induced from empirical data. Our experiments confirm that the first version can be efficient to predict the player’s preferences among the gamification features.
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Prensky, M.: Digital game-based learning. McGraw-Hill, New York (2001)
Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From game design elements to gamefulness: defining gamification. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, pp. 9–15 (2011)
Kapp, K.M.: The Gamification of Learning and Instruction: Game-based Methods and Strategies for Training and Education. John Wiley & Sons (2012)
Bíró, G.I.: Didactics 2.0: A Pedagogical Analysis of Gamification Theory from a Comparative Perspective with a Special View to the Components of Learning (2014)
Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work?—a literature review of empirical studies on gamification. In: Proceedings of the 47th Hawaii International Conference on System Sciences (2014)
Bloom, B.S.: The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 4–16 (1984)
Csikszentmihalyi, M.: Finding flow: The psychology of engagement with everyday life. Basic Books (1998)
Bartle, R.: Richard A. Bartle: Players Who Suit MUDs (1996)
Yee, N.: Motivations for play in online games. CyberPsychology & Behavior 9(6), 772–775 (2006)
Ferro, L.S., Walz, S.P., Greuter, S.: Towards personalised, gamified systems: an investigation into game design, personality and player typologies. In: Proceedings of The 9th Australasian Conference on Interactive Entertainment: Matters of Life and Death, p. 7 (2013)
Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: A neurobiological gamer typology survey. Entertainment Computing 5(1), 55–62 (2014)
Hocine, N., Gouaïche, A., Di Loreto, I., Abrouk, L.: Techniques d´adaptation dans les jeux ludiques et sérieux. Revue d’intelligence artificielle 25(2), 253–280 (2011)
Natkin, S., Yan, C., Jumpertz, S., Market, B.: Creating multiplayer ubiquitous fames using an adaptive narration model based on a user’s model. In: Digital Games Research Association International Conference (2007)
Robinson, D., Bellotti, V.: A preliminary taxonomy of gamification elements for varying anticipated commitment. In Proc. ACM CHI 2013 Workshop on Designing Gamification: Creating Gameful and Playful Experiences (2013)
Sailer, M.: Psychological Perspectives on Motivation Through Gamification. Interaction Design and Architecture(s) Journal - IxD&A, 28–37 (2013)
Zichermann, G., Cunningham, C.: Gamification by Design: Implementing game mechanics in web and mobile apps. O’Reilly Media, Inc. (2011)
Hunicke, R., LeBlanc, M., Zubek, R.: MDA: a formal approach to game design and game research. In: Proceedings of the AAAI Workshop on Challenges in Game AI 04-04 (2004)
Monterrat, B., Lavoué, É., George, S.: Motivation for learning - adaptive gamification for web-based learning environments. In: 6th International Conference on Computer Supported Education, pp. 117–125 (2014)
Desmarais, M.C., Naceur, R.: A matrix factorization method for mapping items to skills and for enhancing expert-based Q-matrices. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 441–450. Springer, Heidelberg (2013)
Ayers, E., Nugent, R., Dean, N.: A Comparison of Student Skill Knowledge Estimates. International Working Group on Educational Data Mining (2009)
Shrout, P.E., Fleiss, J.L.: Intraclass correlations: uses in assessing rater reliability. Psychological bulletin 86(2), 420 (1979)
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Monterrat, B., Desmarais, M., Lavoué, É., George, S. (2015). A Player Model for Adaptive Gamification in Learning Environments. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_30
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DOI: https://doi.org/10.1007/978-3-319-19773-9_30
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