Previous work demonstrated that esports players often leverage insights from other players and communities to learn and improve. However, little research examined social learning in esports, over time, in granular detail. Understanding the role of others in the esports learning process has implications for the design of computational support systems that can help esports players learn and make the games more accessible. Therefore, we perform an exploration of this topic using Co-Regulated Learning as a theoretical lens. In doing so, we hope to enrich existing knowledge on social learning in esports, provide insights for the future development of computational support, and a road-map for future work. Through an interview study of an esports community consisting of 14, college-aged, male players, we uncovered 10 themes regarding how Co-Regulated learning occurs within their teams. Based on these, we discuss three main takeaways and their implications for future research and development.

Emotion regulation and coping strategies are key to resilience, problem solving and eventually well-being in everyday life, but investigating or influencing these without adjustable and ecologically valid environments still poses a major challenge. With respect to video games, stressful events can frequently appear and, depending on the individual’s emotion regulation, lead to frustrating experiences, dissatisfied players or even churn – or to a sense of accomplishment, mastery or positive tension. In the greater endeavor of establishing games for studying, controlling and reinforcing resilience, we developed an alternate reality game that unifies the advantages of a fully customizable game environment with the close connection to real-life interactions. To estimate how emotion regulation strategies could be initiated as well as investigated, this work first induces different kinds of established stressors (time pressure, social encounters and being unchangeably stuck) into ordinary gameplay, quantifies the players’ physiological and psychological responses and addresses the participants’ strategies on dealing with these situations. In this paper we present results of the study showing that the induced stressors were effective in constituting stressful situations comparable to real-life experiences; and each of these conditions resulted in different types of physiological and perceived responses and behaviors, e.g. continuous stress in social encounters versus instantaneous stress when notified about time pressure; or frustrating experiences when being forcibly stuck versus stress that participants underwent but were able to work against. With this platform to study how people cope with stress, instituted within a multi-modal mixed-methods evaluation, we contribute to games beyond entertainment towards educating resilience.

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.