Humans procrastinate and avoid performing activities that they deem dull, repetitive, and out of their comfort zone. Gamification was conceived to reverse the situation by turning those activities into fun and entertaining actions exploiting game-like elements. In practice, however, many challenges arise. Gameful environments cannot satisfy every player's preference and motivational need with a one-fits-all strategy. However, meeting players' motivational affordances can provide intrinsic rewards rather than extrinsic (e.g., points and badges). Producing intrinsic rewards is desirable as they are more likely to foster long-term retention than the extrinsic counterpart. Therefore, gamified systems should be designed to learn and understand players' preferences and motivational drivers to generate specific adaptation strategies for each player. Those adaptation strategies govern the procedural generation of personalized game elements - examples are task difficulty, social-play versus solo-play, or aesthetic tools. However, an appropriate personalization requires intelligent and effective player profiling mechanisms. Player profiles can be retrieved through the analysis of telemetry data, and thus in-game behaviors. In this project, we studied players' individual and social behaviors to understand their personalities and identities within the game. Specifically, we analyzed data from an open-world, persuasive, gamified system: Play&Go. Play&Go implements game-like mechanics to instill more ecological transportation habits among its users. The gamified app offers various ways for players to interact with the game and among one another. Despite Play&Go being one of the few examples of gamification implementing more diverse game mechanics than solely points and leaderboards, it still does not reach the complexity of AAA entertainment games. Thus, it limits the applicability of an in-depth analysis of players' behaviors, constrained by the type of available features. Yet, we argue that gameful systems still provide enough information to allow content adaptation. In this work, we study players' in-game activity from different perspectives to explore gamification's potential. Towards this, we analyzed telemetry data to (1) learn from players' activity, (2) extract their profiles, and (3) understand social dynamics in force within the game. Our results show how players' experience in gamified systems is closer to games than expected, especially in social environments. Hence, telemetry data is a precious source of knowledge also in gamification and can help retain information on players' churn, preferences, and social influence. Finally, we propose a modular theoretical framework for adaptive gamification to generate personalized content designed to learn players' preferences iteratively.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/299790 |
| Date | 13 April 2021 |
| Creators | Loria, Enrica |
| Contributors | Loria, Enrica, Marconi, Annapaola |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:139, numberofpages:139 |
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