Abstract
Video games are ostensibly adopted in the education field thanks to their engaging, immersive and adaptive capacities. The greatest problematic in educational games design is how to create a ludic and adaptive experience without neglecting the learning objectives. To create an adaptive educational game modeling the player/learner is a must. In fact, determining the playing/learning style and measuring the player’s abilities and performances will help in adjusting the game content and difficulty. The aim of this paper is to explain how the Adaptive Mechanism for Educational Games (AMEG) adapts the game using metrics. We will define the kind of metrics that the mechanism collects and how they are used to define the learning/playing style as well as their importance in gameplay and learning content adaptation.
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References
Hamdaoui, N., Idriss, M.K., Bennani, S.: AMEG: adaptive mechanism for educational games based on IMSLD and artificial intelligence. In: 10th International Conference on Intelligent Systems: Theories and Applications (2015)
Gee, J.P.: What Video Games Have to Teach Us About Learning and Literacy. Palgrave/Macmillan, New York (2003)
The Kolb Learning Style Inventory—Version 3.1 Technical Specifications 2005
Bartle, R.A.: Hearts, clubs, diamonds, spades: players who suit MUDs. J. MUD Res. 1, 19 (1996)
Drachen, A., El-Nasr, M.S., Canossa, A.: Game analytics—the basics. In: Game Analytics, pp. 13–40 (2013)
Hunicke, R., Chapman, V.: AI for Dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop (2004)
Gilleade, K., Dix, A.: Using frustration in the design of adaptive videogames. In: Advances in Computer Entertainment Technology, pp. 228–232. ACM Press (2004)
Tychsen, A., Canossa, A.: Defining personas in games using metrics. In: Conference on Future Play: Research, Play, Share, pp. 73–80 (2008)
Dynamic game difficulty balancing. https://en.wikipedia.org/wiki/Dynamic_game_difficulty_balancing
Hunicke, R., Chapman, V.: AI for Dynamic Difficulty Adjustment in Games
Shaker, N., Togelius J., Nelson, M.J.: Procedural Content Generation in Games: A Textbook and an Overview of Current Research, p. 3 (2015)
Shaker, N., Yannakakis, G., Togelius, J.: Towards automatic personalized content generation for platform games. In: Artificial Intelligence and Interactive Digital Entertainment (2010)
Demasi, P., Adriano, J.: On-line coevolution for action games. Int. J. Intell. Games Simul. 2, 80–88 (2003)
Plass, J.L.: Metrics in simulations and games for learning. In: Game Analytics, Maximizing the Value of Player Data, pp. 697–729 (2013)
IMS Global Learning Consortium: Learning Design Specification (2003)
Mavroudi, A., Hadzilacos, T.: Implementation of Adaptive Learning Designs. Open University of Cypru
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Hamdaoui, N., Khalidi Idrissi, M., Bennani, S. (2018). Adaptive Educational Games Using Game Metrics. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_21
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DOI: https://doi.org/10.1007/978-3-319-60834-1_21
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