Abstract
Self-reflection, a process consisting of evaluation, attribution, and adaptation, is recognized as key to learning in esports games. To better facilitate self-reflection, many games and third-party tools feature retrospective visualizations that provide players with details about their performance. However, previous work suggested that existing visualizations, which typically aggregate data, do not present sufficient information to players. In this work, we explore the impact of process visualizations, which present gameplay as a sequence of actions, on self-reflection and performance in a League of Legends training exercise through a mixed-methods study. Our results found that players who reflected using a process visualization significantly improved their performance and demonstrated significant differences in how they evaluated themselves compared to those who reflected using an aggregate visualization. We discuss what these results mean in relation to self-reflection and learning in esports and how they impact the future use of process visualizations in games.
Original language | English |
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Article number | 346 |
Number of pages | 28 |
Journal | Proceedings of the ACM on Human-Computer Interaction |
Volume | 8 |
Issue number | CHI PLAY |
DOIs | |
Publication status | Published - 15 Oct 2024 |
Keywords
- computational assistants
- esports
- games
- learning
- self-reflection
- self-regulated learning
- user studies
- visualization