Kills, Deaths, and (Computational) Assists: Identifying Opportunities forComputational Support in Esport Learning

Title of the Paper

Kills, Deaths, and (Computational) Assists: Identifying Opportunities for Computational Support in Esport Learning

Bibliographic Information

• Subject Area: Skill learning and computational tool support in esports
• Keywords: esports, computational support, AI assistants, data visualization, data-driven tools, learning, professional skills, interview study, qualitative methods

Research Background and Problem

• What problems or challenges did the authors identify?
  • Esports players face numerous challenges when learning and mastering games, including a lack of clear learning pathways, difficulty accessing learning resources, and the complexity of high-level gameplay.
  • Computational support tools for esports learning often rely on high-level understanding and lack in-depth analysis of specific player activities and challenges.
• Why is this problem important?
  • Esports has been shown to cultivate various practical skills, such as emotional regulation and quick reflexes. Improving the experience of learning and skill acquisition can benefit a broader range of players, thereby increasing accessibility to high-level esports gameplay and its potential benefits.
• Research Motivation and Related Work
  • Existing research focuses more on describing the skills of expert players, with less attention paid to how players acquire these skills and the specific challenges they face in the process.
  • Related studies in sports suggest that goal setting, teamwork, and reflection are critical for skill improvement, but it remains uncertain whether these findings can be directly applied to esports.

Solution

• What methods or solutions did the authors propose?
  • Conducted a qualitative interview study with 17 esports players to analyze their activities and challenges in learning and mastering esports.
  • Proposed six high-level recommendations for designing and developing computational support tools.
• What is innovative about this solution?
  • Combined player interviews with qualitative analysis to systematically summarize, for the first time, the specific activities and challenges faced by non-expert players in esports learning.
  • Emphasized a player-centered design philosophy for computational tools, laying a solid foundation for future tools that address players' learning needs.
• What are the implementation steps and key technologies used?
  • Designed and conducted an interview study to collect player feedback on goals, practice activities, challenges, and ideas for computational support tools.
  • Used iterative thematic analysis to parse interview data and identify key activities and difficulties faced by players.

Research Findings

• What specific results were achieved?
  • Defined four key activities players engage in while learning and mastering esports:
    1. Practice: Improving skills through in-game practice and training modes.
    2. Leveraging others' knowledge: Gaining knowledge through training partners, team members, and observing others' gameplay.
    3. Tracking performance: Monitoring progress using in-game and external data.
    4. Reflecting on gameplay and setting goals: Reviewing gameplay to identify mistakes and set future goals.
  • Summarized four major challenges faced by players during the learning process:
    1. Team coordination and collaboration: Difficulty in communication and coordination.
    2. Choosing the next action: Struggles with real-time decision-making.
    3. Tracking game state: Inability to effectively interpret complex game information.
    4. Tracking skills and improvement: Difficulty in objectively evaluating performance and identifying mistakes.
  • Proposed six high-level recommendations for future tool design and development:
    1. Provide data-driven performance evaluation.
    2. Use explainable AI (XAI) to enhance trust.
    3. Develop AI opponents that better mimic human behavior.
    4. Real-time data tracking to supplement direct player communication.
    5. State-tracking features to enhance situational awareness.
    6. Apply scaffolding and co-regulated learning to decision-support systems.
• What advantages does it have compared to existing solutions?
  • Clarified the details of player learning activities and challenges, enabling computational support tools to better address players' needs.
  • Proposed deeper and more human-centered tool design principles compared to high-level understanding.
• What were the experimental or evaluation results?
  • Quantified and validated the coded themes within the sample of 17 players, finding that these activities and challenges were present across all participants.
  • Explored potential computational support solutions for the identified challenges in detail.
• Limitations and Future Directions
  • The sample size of participants was limited; further research could expand the participant pool to uncover patterns related to professional skill levels.
  • The data analysis results may not fully generalize to all esports categories, as no participants represented sports simulation games.
  • Future work aims to compare findings with existing computational support tools to evaluate their practical effectiveness and explore whether the research can be extended to other dynamic domains (e.g., disaster response).