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Creators/Authors contains: "Seif_El-Nasr, Magy"

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  1. ; ; ; ; (, CHI 2024: Proceedings of the CHI Conference on Human Factors in Computing Systems)
  2. ; ; ; ; ; ; ; ; (, ACM)
    Full Text Available 
  3. ; ; ; ; ; ; ; ; ; ; et al (, ACM)
    Free, publicly-accessible full text available April 25, 2026
  4. ; ; ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    Computational support for learning in the domain of esports has seen a great deal of attention in recent years as an effective means of helping players learn and reap the benefits of play. However, previous work has not examined the tools from a learning theory perspective to assess if learning is prompted and supported in the right place and time. As a first step towards addressing this gap, this paper presents the results of two studies: a review of existing computational tools, and an online survey of esports' players' learning needs supplemented with qualitative interviews. Using Zimmerman's Cyclical Phase Model of Self-Regulated Learning as a lens, we identify patterns in the types of support offered by existing tools and players' support interests during different learning phases. We identify 11 opportunities for future research and development to better support self-regulated learning in esports. 
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