``Backseat Gaming" A Study of Co-Regulated Learning within a Collegiate Male Esports Community

Title of the Paper

“Backseat Gaming" An Interview Study on Co-Regulated Learning within a Collegiate Male Esports Community

Paper Information

• Subject Area: Esports Learning, Social Learning, Information and Computing Support
• Keywords: Co-Regulated Learning, Esports, Gaming, Learning, Social Learning, Esports Teams, Social Gaming

Research Background and Problem

• What issues or challenges did the authors identify?

  • The esports industry is growing rapidly, and the learning processes of its players and communities are important but complex, especially in the context of social learning and long-term dynamics, which remain under-researched.
  • Existing computational support tools fail to provide the personalized and specialized learning assistance needed by esports teams.
  • The skill acquisition process of esports players, particularly the details of social learning and long-term support, remains a gap in research.

• Why is this issue important?

  • Esports is developing rapidly, with the industry valued at over $1 billion, and players can gain benefits such as critical thinking, teamwork skills, and problem-solving abilities.
  • A deeper understanding of the social learning processes in esports is crucial for developing computational support tools that can simulate social interactions and provide timely feedback to enhance learning.

• Research Motivation and Related Work

  • Experienced esports players often rely on team and community resources to improve their skills, but beginners lack similar support and are more likely to quit due to challenges.
  • By utilizing the theoretical framework of "Co-Regulated Learning (CoRL)," the study aims to explore how team players support each other's learning through input exchange (e.g., feedback and guidance), addressing a gap in existing research.

Solution

• What methods or solutions did the authors propose?

  • The authors employed the "Co-Regulated Learning (CoRL)" theory as an analytical framework to conduct a field study on input exchange within esports teams.
  • Through semi-structured interviews, they explored how 14 male players from a collegiate esports club learn through teamwork and feedback.

• What is innovative about this solution?

  • This study is the first to systematically analyze the social learning processes of esports teams from the perspective of "Co-Regulated Learning," particularly focusing on how players optimize learning through input exchange.
  • It provides theoretical and practical insights for designing computational support tools for esports, contributing significantly to a deeper understanding of team collaboration.

• What are the implementation steps? What key techniques were used?

  1. Interview Design: Questions were designed based on the CoRL theory, focusing on input exchange behaviors such as seeking help and providing feedback.
  2. Data Collection: One-on-one semi-structured interviews were conducted with participants to explore their decision-making patterns and use of input.
  3. Data Analysis: Thematic analysis was used to extract key themes such as sources of input, the relationship between failure and feedback, and the hierarchy of input provision.
  4. Results Summary and Discussion: Themes derived from the interviews were summarized to identify common patterns and frameworks in esports learning.

Research Findings

• What specific findings were achieved?

  • Ten themes related to input exchange were identified (e.g., how input sources are selected, the relationship between failure and feedback, and the hierarchical nature of input provision).
  • Three key patterns of co-regulated learning in esports teams were identified:
    1. Hierarchical Nature of Input: Input is only accepted from individuals perceived as more experienced (e.g., captains or coaches), and strict team hierarchies influence learning behaviors.
    2. Relationship Between Failure and Input: Failure is the primary driver for players to seek input. When players make mistakes or experience repeated failures, teams proactively provide feedback.
    3. Game Phases and Learning Stages: Learning priorities and interaction forms vary across three phases (pre-game, in-game, and post-game). For example, pre-game focuses on strategy discussions, post-game emphasizes reflection, while in-game input exchange is minimal.

• What advantages does it have compared to existing solutions?

  • Provides a more detailed description of the social and long-term processes of learning in esports.
  • The findings offer clear guidance for future development of computational tools, particularly in simulating realistic teammate behaviors to enhance technological support.

• What are the experimental or evaluation results?

  • Data saturation indicates that the interviews with 14 participants were sufficient to extract key themes.
  • The study demonstrates how input interactions shape social learning in esports teams and support skill development.

• Limitations and Future Directions

  • Limitations:
    • The sample is limited to 14 male college students, excluding women, non-binary individuals, or players from diverse cultural backgrounds.
    • The study lacks exploration of other types of esports, non-competitive environments, and players of different age groups.
  • Future Directions:
    • Conduct similar studies with other groups (e.g., female players or players from different nationalities) to form generalizable conclusions.
    • Explore how to design computational support tools with social functionality and a sense of authority, particularly in emotional regulation during input acceptance and rejection.
    • Develop specialized models for social learning in esports to better describe non-individual interactive learning processes.