Visualizing Real-Time Strategy Games: The Example of StarCraft II

Kuan, Yen-Ting; Wang, Yu-Shuen; Chuang, Jung-Hong

doi:10.1109/vast.2017.8585594

Cited by 27 publications

(9 citation statements)

References 14 publications

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“…The first area in which these results have implications is in the domain of computational support for esports. A great number of computational tools for esports exist, which broadly provide players with assistance in decision making (Chen et al, 2018a , b ; Christiansen et al, 2019 ; Eger and Sauma Chacón, 2020 ) and review of gameplay (Wallner and Kriglstein, 2016 , 2020 ; Kuan et al, 2017 ; Afonso et al, 2019 ). These tools are often explicitly motivated by the desire to help players learn and master their game.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, SRL skills, such as goal setting, can have an impact on gameplay performance. In the context of esports, knowledge of the role and impact of SRL would be invaluable to helping players more effectively learn and gain expertise, which is often cited as the primary motivation for the development of computational support tools for esports (Wallner and Kriglstein, 2016 ; Kuan et al, 2017 ; Afonso et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

“…There are several ways that the formal study of SRL in esports could benefit both the learning and games communities. For games, and especially for esports, knowledge of how SRL manifests within the domain could highlight opportunities to more effectively support learning through specialized training or computational tools that target SRL processes (Kuan et al, 2017 ; Afonso et al, 2019 ). Further, knowledge of where SRL processes are not being leveraged by players, or where they differ across skill levels, may highlight elements of the gameplay experience where learning is more difficult.…”

Section: Introductionmentioning

confidence: 99%

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“Because I'm Bad at the Game!” A Microanalytic Study of Self Regulated Learning in League of Legends

2021

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Self-regulated learning (SRL) is a form of learning guided by the student's own meta-cognition, motivation, and strategic action, often in the absence of an educator. The use of SRL processes and skills has been demonstrated across numerous academic and non-academic contexts including athletics. However, manifestation of these processes within esports has not been studied. Similar to traditional athletes, esports players' performance is likely correlated with their ability to engage SRL skills as they train. Thus, the study of SRL in the context of esports would be valuable in supporting players' learning and mastery of play through specialized training and computational support. Further, an understanding of how SRL manifests in esports would highlight new opportunities to use esports in education. Existing work on SRL in games, however, predominantly focuses on educational games. In this work, we aim to take a first step in the study of SRL in esports by replicating Kitsantas and Zimmerman's (2002) volleyball study in the context of League of Legends. We compared the self-regulatory processes of expert, non-expert, and novice League of Legends players, and found that there were significant differences for processes in the forethought phase. We discuss three implications of these findings: what they mean for the development of future computational tools for esports players, implications that esports may be able to teach SRL skills that transfer to academics, and what educational technology can learn from esports to create more effective tools.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“Because I'm Bad at the Game!” A Microanalytic Study of Self Regulated Learning in League of Legends

2021

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“…To date, most player-facing gameplay visualizations feature either aggregate [5,15] or spatio-temporal data [1,24,44]. Aggregate visualization techniques use visual elements such as percentages, graphs, and charts [5,15,29].…”

Section: Related Workmentioning

confidence: 99%

“…Process visualizations, which present human process as a sequence of actions taken [3,30,37,41,42,45,46], appear well suited to preserving causal information. Player-facing, post-play process visualizations are, however, rare and typically included as a secondary feature to a visualization of another type [1,24,32]. As such, they are rarely the focus of research, and we know little about how players extract meaning from them, which is necessary to ensure that we design and implement them in appropriate and effective ways.…”

Section: Introductionmentioning

confidence: 99%

Towards an Understanding of How Players Make Meaning from Post-Play Process Visualizations

Kleinman

Villareale

Shergadwala

et al. 2022

Lecture Notes in Computer Science

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Player-facing, retrospective gameplay visualizations help players track progress and learn from others. Visualizations of a user's step by step process, may be able to advance retrospective visualization. However, we currently do not know how players make meaning from process visualizations of game data. In this work, we take a first step towards addressing this gap by examining how players make meaning from process visualizations of other players' gameplay. We identify two interpretation methods comprised of six techniques and discuss what these results mean for future use of player-facing process visualizations.

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