Abstract
Memorizing declarative knowledge requires repetition, which can become wearing for learners. In addition, redundant game activities, offering unbalanced challenges in relation to the player’s skills, can also lead to a sense of boredom. To reduce this feeling, learning games must provide adapted and varied activities. Automated generation is one way of building such activities. This article proposes a conceptual framework for the design of activity generators for training declarative knowledge in Roguelite games. The framework has been applied in the context of the AdapTABLES project aiming at multiplication tables training.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bakkes, S., Tan, C.T., Pisan, Y.: Personalised gaming: a motivation and overview of literature. In: Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System, pp. 1–10. ACM, Auckland New Zealand (2012)
Bezza, A., Balla, A., Marir, F.: An approach for personalizing learning content in e-learning systems: a review. In: Second International Conference on E-Learning and E-Technologies in Education, pp. 218–223. IEEE, Lodz, Poland (2013)
Brame, C.J., Biel, R.: Test-enhanced learning: the potential for testing to promote greater learning in undergraduate science courses. LSE 14(2), es4 (2015)
Callies, S., Sola, N., Beaudry, E., Basque, J.: An empirical evaluation of a serious simulation game architecture for automatic adaptation. In: R. Munkvold & L. Kolas, Proceedings of the 9th ECGBL, pp. 107–116 (2015)
Carpentier, K., Lourdeaux, D.: Generation of learning situations according to the learner’s profile within a virtual environment. In: Filipe, J., Fred, A. (eds.) ICAART 2013. CCIS, vol. 449, pp. 245–260. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44440-5_15
Holohan, E., Melia, M., McMullen, D., Pahl, C.: The generation of E-learning exercise problems from subject ontologies. In: 6th International Conference on Advanced Learning Technologies, pp. 967–969. IEEE, Kerkrade, Netherlands (2006)
Ismail, H., Belkhouche, B.: A reusable software architecture for personalized learning systems. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 105–110. IEEE, Al Ain (2018)
Kent, S.: Model driven engineering. In: Butler, M., Petre, L., Sere, K. (eds.) IFM 2002. LNCS, vol. 2335, pp. 286–298. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47884-1_16
Kim, J.W., Ritter, F.E., Koubek, R.J.: An integrated theory for improved skill acquisition and retention in the three stages of learning. Theor. Issues Ergon. Sci. 14(1), 22–37 (2013)
Laforcade, P., Laghouaouta, Y.: Generation of adapted learning game scenarios: a model-driven engineering approach. In: McLaren, B.M., Reilly, R., Zvacek, S., Uhomoibhi, J. (eds.) CSEDU 2018. CCIS, vol. 1022, pp. 95–116. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21151-6_6
Laforcade, P., Mottier, E., Jolivet, S., Lemoine, B.: Expressing adaptations to take into account in generator-based exercisers: an exploratory study about multiplication facts. In: 14th CSEDU. Online Streaming, France (2022)
Lemoine, B., Laforcade, P., George, S.: An analysis framework for designing declarative knowledge training games using roguelite genre. In: Proceedings of the 15th CSEDU, Volume 2, Prague, Czech Republic, April 21–23, pp. 276–287 (2023)
Lemoine, B., Laforcade, P., George, S.: Mapping task types and gameplay categories in the context of declarative knowledge training. In: Proceedings of the 15th CSEDU, Volume 2, Prague, Czech Republic, April 21–23, pp. 264–275 (2023)
Prensky, M.: Computer Games and Learning: Digital Game-Based Learning. Handbook of Computer Game Studies (2005)
Roepke, R., Drury, V., Schroeder, U., Meyer, U.: A modular architecture for personalized learning content in anti-phishing learning games. In: Software Engineering (Satellite Events) (2021)
Sehaba, K., Hussaan, A.M.: GOALS: Generator of adaptive learning scenarios. IJLT 8(3), 224 (2013)
Smith, R.P.: Boredom: a review. Hum. Factors 23(3), 329–340 (1981)
Streicher, A., Smeddinck, J.D.: Personalized and adaptive serious games. In: Dörner, R., Göbel, S., Kickmeier-Rust, M., Masuch, M., Zweig, K. (eds.) Entertainment Computing and Serious Games. LNCS, vol. 9970, pp. 332–377. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46152-6_14
Tang, S., Hanneghan, M.: Game content model: an ontology for documenting serious game design. In: 2011 Developments in E-systems Engineering, pp. 431–436. IEEE, Dubai, United Arab Emirates (2011)
Tchounikine, P., Mørch, A.I., Bannon, L.J.: A computer science perspective on technology-enhanced learning research. In: Balacheff, N., Ludvigsen, S., de Jong, T., Lazonder, A., Barnes, S. (eds.) Technology-Enhanced Learning. Springer, Dordrecht (2009). https://doi.org/10.1007/978-1-4020-9827-7_16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lemoine, B., Laforcade, P. (2024). Generator of Personalised Training Games Activities: A Conceptual Design Approach. In: Dondio, P., et al. Games and Learning Alliance. GALA 2023. Lecture Notes in Computer Science, vol 14475. Springer, Cham. https://doi.org/10.1007/978-3-031-49065-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-49065-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49064-4
Online ISBN: 978-3-031-49065-1
eBook Packages: Computer ScienceComputer Science (R0)