Abstract
Gamification is a widely used resource to engage and retain users. It is about the use of game elements and mechanics in systems and domains that are not naturally games. Nevertheless, the usage of gamification does not always achieve the expected results due to the too much generalized approach that makes invisible the different motivations, characteristics and playing styles among the players. Currently, research on adaptive gamification deals with the gamification that each particular user needs at a particular moment, adapting gamification to users and contexts. Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. This article proposes an adapted gamification approach for CLCS, through the automatic game challenge generation. Particularly a model of user profile considering the space-time behavior and challenge completion, a model for the different types of challenges applicable in CLCS, a model for the CLCS objectives and coverage, and a strategy for the application of Machine Learning techniques for adaptation.
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References
Dalponte Ayastuy, M., Torres, D.: Behavioral atoms for NY foursquare users in 2012 (April 2021). https://doi.org/10.5281/zenodo.4728128
Dalponte Ayastuy, M., Torres, D., Fernández, A.: Adaptive gamification in Collaborative systems, a systematic mapping study. Comput. Sci. Rev. 39, 100333 (2021). https://doi.org/10.1016/j.cosrev.2020.100333. https://www.sciencedirect.com/science/article/pii/S1574013720304330
Böckle, M., Novak, J., Bick, M.: Towards Adaptive Gamification: a Synthesis of Current Developments. Research Papers (July 2017)
Cochero, J.: Appear: a citizen science mobile app to map the habitat quality of continental waterbodies. Ecología Austral. 28(02), 467–479 (2018)
Dalponte Ayastuy, M., Torres, D.: Relevance of non-activity representation in traveling user behavior profiling for adaptive gamification. In: Proceedings of the XXI International Conference on Human Computer Interaction. Interacción 2021. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3471391.3471431
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, MindTrek 2011, Tampere, Finland, pp. 9–15. ACM, New York (2011). https://doi.org/10.1145/2181037.2181040
Göbel, S., Wendel, V.: Personalization and adaptation. In: Dörner, R., Göbel, S., Effelsberg, W., Wiemeyer, J. (eds.) Serious Games, pp. 161–210. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40612-1_7
Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work? A literature review of empirical studies on gamification. In: 2014 47th Hawaii International Conference on System Sciences, pp. 3025–3034. IEEE (2014)
Iversen, S.: In the double grip of the game: challenge and \(fallout\) 3. Game Stud. 12 (2012). http://www.gamestudies.org/1202/articles/in_the_double_grip_of_the_game
Khoshkangini, R., Valetto, G., Marconi, A., Pistore, M.: Automatic generation and recommendation of personalized challenges for gamification. User Model. User Adap. Inter. 31(1), 1–34 (2020). https://doi.org/10.1007/s11257-019-09255-2
Kishimoto, K., Kobori, H.: Covid-19 pandemic drives changes in participation in citizen science project “city nature challenge” in Tokyo. Biol. Conserv. 255, 109001 (2021). https://doi.org/10.1016/j.biocon.2021.109001. https://www.sciencedirect.com/science/article/pii/S0006320721000537
Tomé Klock, A.C., da Cunha, L.F., de Carvalho, M.F., Eduardo Rosa, B., Jaqueline Anton, A., Gasparini, I.: Gamification in e-learning systems: a conceptual model to engage students and its application in an adaptive e-learning system. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2015. LNCS, vol. 9192, pp. 595–607. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20609-7_56
Lavoué, E., Monterrat, B., Desmarais, M., George, S.: Adaptive gamification for learning environments. IEEE Trans. Learn. Technol. 12, 16–28 (2018). https://doi.org/10.1109/TLT.2018.2823710
Lemmens, R.: A conceptual model for participants and activities in citizen science projects. In: Vohland, K., et al. (eds.) The Science of Citizen Science, pp. 159–182. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58278-4_9
Lopes, R., Bidarra, R.: Adaptivity challenges in games and simulations: a survey. IEEE Trans. Comput. Intell. AI Games 3(2), 85–99 (2011). https://doi.org/10.1109/TCIAIG.2011.2152841
Nugent, J.: Citizen science: iNaturalist. Sci. Scope 041(07), 12–13 (2018)
Orji, R., Tondello, G.F., Nacke, L.E.: Personalizing persuasive strategies in gameful systems to gamification user types. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, pp. 1–14. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3173574.3174009
Sánchez-Anguix, V., Alberola, J.M., Julián, V.: Towards adaptive gamification in small online communities. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds.) SOCO 2021. AISC, vol. 1401, pp. 48–57. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87869-6_5
Thiebes, S., Lins, S., Basten, D.: Gamifying information systems - a synthesis of gamification mechanics and dynamics. In: ECIS 2014, June 2014 (2014)
Tondello, G., Wehbe, R., Diamond, L., Busch, M., Marczewski, A., Nacke, L.: The gamification user types hexad scale. In: CHI PLAY 2016, October 2016 (2016). https://doi.org/10.1145/2967934.2968082
Vahlo, J., Karhulahti, V.M.: Challenge types in gaming validation of video game challenge inventory (CHA). Int. J. Hum. Comput. Stud. 143, 102473 (2020). https://doi.org/10.1016/j.ijhcs.2020.102473. https://www.sciencedirect.com/science/article/pii/S1071581920300756
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Dalponte Ayastuy, M., Torres, D., Fernández, A. (2022). A Model of Adaptive Gamification in Collaborative Location-Based Collecting Systems. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_13
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