Feature Selection for Adaptive Gamification
- Type:Bachelor, Master
Gamification (i.e. the use of game elements in non-game contexts) is increasingly becoming a trend in various contexts such as education or healthcare, where it is praised for its ability to increase motivation and engagement of individuals by affording positive experiences usually found in games. To improve the effects of gamification, extant research is increasingly stressing the importance of moving from “one-size-fits-all” approaches to individualized gamification designs that take into account for example the differences in personalities across users. In designing such individualized concepts, methods of artificial intelligence (AI) are also considered highly useful. However, to successfully apply algorithms to individualize gamification, it is first necessary to understand the possible criteria to base adaptivity on (i.e., algorithms inputs, for example different player types), how game elements can be individualized (i.e., algorithm outputs, for example altering the level of difficulty), and how the in- and outputs should map to each other (e.g., when a user is in a bad mood, the difficulty should be decreased). The focus of this thesis lies on the algorithm inputs, which are commonly also referred to as features or variables. In particular, the main goal of this thesis is to borrow from the domain of AI by applying the thoughts of Feature Selection (i.e. selecting a subset of relevant features for constructing an AI model) to adaptive gamification algorithms.
Possible Topics include, but are not limited to:
- Provide an overview of possible features (and possibly outputs) for adaptive gamification algorithms
- Evaluate (i.e. rank or score) the suitability of the collected features as inputs for adaptive gamification algorithms
- Literature Review
- Feature Selection
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Rozi, F., Rosmansyah, Y., & Dabarsyah, B. (2019, July). A systematic literature review on adaptive gamification: components, methods, and frameworks. In 2019 International Conference on Electrical Engineering and Informatics (ICEEI).
Böckle, M., Micheel, I., Bick, M., & Novak, J. (2018, January). A design framework for adaptive gamification applications. In Proceedings of the 51st Hawaii International Conference on System Sciences.
Kumar, V., & Minz, S. (2014). Feature selection: a literature review. SmartCR, 4(3), 211-229.
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