Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division
| Autor: | David Melhart, Antonios Liapis, Alessandro Canossa, Georgios N. Yannakakis, Ahmad Azadvar |
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| Rok vydání: | 2019 |
| Předmět: |
media_common.quotation_subject
Testing digital games gameplay data support vector machines Ubisoft Perceived Experience Questionnaire Predictive models Tools player modelling Psychology gameplay features affective computing Affective computing Competence (human resources) Self-determination theory media_common Preference learning preference learning methods Data models ComputingMilieux_PERSONALCOMPUTING motivation modelling Human Computer Interaction Certainty Människa-datorinteraktion (interaktionsdesign) Data processing Ask price preference learning computer games learning (artificial intelligence) player motivation Games Tom Clancy The Division game human factors Autonomy Cognitive psychology |
| Zdroj: | CoG |
| Popis: | Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players. |
| Databáze: | OpenAIRE |
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