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Backseat Gaming" A Study of Co-Regulated Learning within a Collegiate Male Esports Community
... Teambased titles are characterized by a network of established practices within a community of experienced players, requiring new players to learn within often challenging environments (Huston et al., 2023). These "noobs" (new players) may discontinue playing without guidance (Kleinman et al., 2022) or due to negative experiences (Huston et al., 2023), often before they can engage with the beneficial learning processes within esports teams (Kleinman et al., 2024). Essentially, as with traditional sports, new players may stop playing due to a myriad of contextual factors. ...
... One key route to improvement is through the social learning inherent in playing and reflecting as a team (Kleinman et al., 2024). This socialization and subsequent knowledge creation in esports teams is comparable to how knowledge is created within organizations (Kong et al., 2024). ...
... It took six full weeks of play until December 15 th , when I wrote "I think that's the first time it's really become clear to me how the different roles actually operate". That it took so long is indicative of the challenges inherent in trying to improve within such a complex environment without coaching support (Kleinman et al., 2022) or a consistent set of teammates (Kleinman et al., 2024). Ongoing play/practice led to becoming more familiar with agents, game maps and weapons, which in turn developed my game sense. ...
Purpose
This study aims to explore the potential of a team-based esports game, Valorant, as a platform for developing individuals and teams in organizations.
Design/methodology/approach
This work represents an auto-netnographic exploration of a team-based esports title from a consumer perspective through extensive diaries recorded through the initial 10 weeks of play.
Findings
Three key themes are identified that illuminate the experience of entering a new team-based game experience. The results illustrate that at the core of Valorant is the team experience, which suggests potential for the game as a platform to develop teams and enhance relevant individual and team-based skills in organizational contexts. Organizational teams should benefit from the use of esports as a team development tool, but any such organizational initiative requires a structured approach. Virtual teams may benefit more than their co-located equivalents from the affordances of online team-based play.
Originality/value
This study is one of the few auto-netnographic explorations of a team-based game in esports. Valorant, as an increasingly popular game, remains underexplored in academic literature.
... Coaching is a form of development in which a learner receives support, in the form of training or guidance, from an individual with expertise in the domain [112]. In esports, providing input to a lower-level teammate or friend [78] may be considered coaching. However, in this work, we look specifcally at coaching in the context where "coach" is a designated position of authority, and the coach is recognized to possess greater expertise than their students. ...
... Further, they occurred outside of the domain of Human Computer Interaction (HCI), which makes it difcult to link the fndings to actionable, technological insights. Prior work has demonstrated how HCI best practices, theories, and frameworks can advance the domain of esports player well-being [101], individual and social esports learning [78,79], and esports players' emotional regulation [84]. Here, we build upon the foundation laid by this prior work to explore how HCI can advance another element of the esports domain: coaching. ...
... Not all interactions are toxic, and other research seeks to better understand how player communities communicate and share knowledge [78,85]. Prior work found that players aim to support each other and build connections that extend beyond the game [56]. ...
... All authors reviewed the themes and determined that saturation [26] had been reached. 13 participants is consistent with common standards for similar, qualitative research [27,44,97]. ...
... Primarily, we acknowledge that we interviewed 13 GMs for this study. While we did see saturation in the data, and this number is consistent with common HCI standards [27,44], a larger sample size may yield additional insights. To better understand GMs' best practices more broadly, we aim, in the future, to expand this work with a survey study that can reach a wider audience and build a stronger foundation of knowledge. ...
... Additionally, eSports players often rely on insights from peers and online communities to refine their skills. This social learning process is facilitated by coregulated learning, where players collaboratively regulate their learning activities, leading to enhanced performance and knowledge acquisition (Kleinman et al., 2024). ...
Purpose-This study aims to find out whether eSports as intrinsic motivation has a positive effect on innovative behavior and whether knowledge sharing plays a mediating role in this relationship. Design/methodology/approach-This study uses a quantitative approach. The research hypotheses were tested using partial least square-structural equation modeling with multi-group analysis using Smart-PLS 4. The sample was 208 respondents from the academic community affiliated with the universities in Indonesia and India. Findings-For India, there is a positive relationship between eSports-driven intrinsic motivation and innovative behavior. Knowledge sharing acted as a mediator. For Indonesia, however, the results indicate a more complex dynamic. Although intrinsic motivation in eSports leads to knowledge-sharing behaviors, the direct relationship between intrinsic motivation and innovative behavior was not significant. Knowledge sharing, therefore, plays a crucial role as a mediator. Originality/value-The study advances the literature by demonstrating how eSports as intrinsic motivation influences innovative behavior through knowledge sharing based on self-determination theory by focusing on academic communities in two different countries.
... Additionally, eSports players often rely on insights from peers and online communities to refine their skills. This social learning process is facilitated by coregulated learning, where players collaboratively regulate their learning activities, leading to enhanced performance and knowledge acquisition (Kleinman et al., 2024). ...
Purpose-This study aims to find out whether eSports as intrinsic motivation has a positive effect on innovative behavior and whether knowledge sharing plays a mediating role in this relationship. Design/methodology/approach-This study uses a quantitative approach. The research hypotheses were tested using partial least square-structural equation modeling with multi-group analysis using Smart-PLS 4. The sample was 208 respondents from the academic community affiliated with the universities in Indonesia and India. Findings-For India, there is a positive relationship between eSports-driven intrinsic motivation and innovative behavior. Knowledge sharing acted as a mediator. For Indonesia, however, the results indicate a more complex dynamic. Although intrinsic motivation in eSports leads to knowledge-sharing behaviors, the direct relationship between intrinsic motivation and innovative behavior was not significant. Knowledge sharing, therefore, plays a crucial role as a mediator. Originality/value-The study advances the literature by demonstrating how eSports as intrinsic motivation influences innovative behavior through knowledge sharing based on self-determination theory by focusing on academic communities in two different countries.
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.
Whereas past CHI-related work has paid attention to various elements of the esports industry, there exists a scarcity of such research on the increasing convergence between esports and gambling. This study, therefore, aimed to shed light on the emerging phenomeno n of esports betting in two ways. First, we theorized about its characteristics according to the 5W1H framework. Second, using semi-structured laddering interviews and Means-End Chain Theory, we assessed the esports betting motivations of young, male and recreational gamblers who interact with monetary and skin betting websites. Our results show that gamblers follow various motivational pathways when using an esports betting platform and construct their motives in relation to platform-specific properties and personal values. As such, we demonstrate that a holistic understanding of esports betting is of utmost importance if we want to explain and investigate its appeal to a worldwide audience.
A large body of research has underscored the importance of the cognitive process of team cognition and its relation to team performance. However, little research has focused on applying such an important teamwork process to computer-mediated collaboration within a fast-paced virtual environment. In this paper, we use esports as a research platform to address this limitation due to its fast-paced nature and its heavy reliance on teamwork. We report the experience and perceptions of 20 players with regard to their descriptions of team cognition within esports. We found that esports players relied on their game experience and understanding of role interdependencies in order to develop team cognition with strangers. We also found that experienced teams utilized a mutual understanding of teammate skills and personalities in order to predict responses and limit the verbal communication required to make quick team decisions. We contribute to CSCW by extending the cognitive understanding of computer-mediated collaboration and by advancing research on team cognition and how it can occur within a fast-paced virtual environment.
With the rise of competitive online gaming and esports, players’ ability to review, reflect upon, and improve their in-game performance
has become important. Post-play visualizations are key for such improvements. Despite the increased interest in visualizations of
gameplay, research specifically informing the design of player-centric visualizations is currently limited. As with all visualizations,
their design should, however, be guided by a thorough understanding of the goals to be achieved and which information is important
and why.
This paper reports on a mixed-methods study exploring the information demands posed by players on post-play visualizations and
the goals they pursue with such visualizations. We focused on three genres that enjoy great popularity within the competitive gaming
scene. Our results provide useful guideposts on which data to focus on by offering an overview of the relevance of different in-game
metrics across genres. Lastly, we outline high-level implications for the design of post-play visualizations.
Recently, increased attention has been directed to the brain to better understand how motor skill expertise develops. One promising technique purported to accelerate motor skill improvement is transcranial direct current stimulation (tDCS). While simple fine motor tasks involving the hands and fingers are most frequently used to investigate the role of tDCS on motor skill learning, less work has examined the role of tDCS on complex sensori-motor tasks applicable to occupational, sport, and daily living activities. Esports require a high degree of sensori-motor control and have become one of the most popular forms of digital entertainment worldwide. Currently, no research has quantified the development of motor skill expertise in esports or whether tDCS can enhance skill improvement. The current study aimed to first differentiate the sensorimotor performance of a key gameplay skill among esports players of different skill levels. Secondly, we quantified the training effect on performance. Finally, we investigated the effect of tDCS on performance improvements. We hypothesised that esport players would perform superiorly compared to novice gamers, that all groups would be able to improve their performance through training, and that tDCS would enhance training induced performance improvements. We found that performance on a single fundamental esport skill can differentiate expertise among novice and skilled players, that training can significantly improve performance among all expertise levels and that tDCS preferentially accelerates the performance improvements of novice players. The implications of this work, specifically regarding the temporal application of tDCS during complex motor skill learning and rehabilitation, are discussed.
Who is a gamer? What kind of people are perceived to be gamers? And finally – who perceives themselves as a gamer? In this article the authors attempt to answer these three questions from a multinational perspective.
Background. Games are one of the most frequently encountered forms of entertainment and constitute an ever-increasing part of many people’s day-to-day lives. With the rising popularity of video games, there is a need to conduct research concerning gamer identity and to find out who perceives themselves as a gamer. The aim of this study is to compare the results of the survey conducted in two different countries to better understand the characteristics of players that self-identified as gamers.
Methods. The quantitative study was conducted in two countries – Poland and the United States – in order to research gamer identity. The questionnaire consisted of questions about the self-identification as a gamer, time spent playing video games, types of games played, and the platforms used. It was conducted among 223 students who play video games.
Results. The results show that there are both similarities and differences in the meaning of gamer identity between Poland and the United States. People who consider themselves gamers generally spend more time playing games than non-gamers regardless of the country. However, some differences can be spotted between Poland and the U.S. concerning types of games played, platforms used, and different styles of playing video games.
Limitations and further research. The main problem in the study was the limited age range in the sample. In the future it seems valuable to include people of different age groups to broaden the study of self-identified gamer identity. This is an open access article and clicking on the DOI above will take you to the full text.
Today eSports gaming is enjoying growing popularity in the world and much attention from various research areas, including CSCW. eSports gaming is a highly competitive environment commonly associated with negative emotions such as anxiety and stress. However, little attention has been paid to emotion regulation in eSports gaming. In this study, we empirically investigated how players experience emotion and regulate emotions in League of Legends, one of the largest eSports games today. We identify four emotive factors, as well as emotion regulation strategies that players deploy to manage the emotions of their selves, teammates, and opponents. We further report on how they use emotion regulation in emotional self-care and emotional leadership. Building upon this set of findings, we discuss how the competitive eSports gaming context conditions emotion regulation in League of Legends, foreground emotion regulation expertise in competitive gaming, and derive implications for designing emotion regulation technologies. CCS Concepts: • Human-centered computing → Collaborative and social computing → Empirical studies in collaborative and social computing • Human-centered computing → Human computer interaction (HCI) → Empirical studies in HCI
Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies.
Competitive gaming, better known as electronic sports (esports), is rapidly growing in popularity. We systematically reviewed the available literature regarding the psychological aspects of esports using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) evidence-based reporting checklist and a Population, Intervention, Comparator, and Outcomes (PICO) framework with the following inclusion criteria: (i) published between 1994 and 2018; (ii) empirical investigation (as the current state of research is dense with positions and opinions but has few empirical investigations); and (iii) focussed on esports games that are associated with either cognitive performance or game performance. The goal of our research was twofold: to present a summary of the empirical evidence addressing the psychological characteristics of both cognitive and game performance in esports, and to integrate esports in the field of sport psychology. More specifically, our goals were to highlight the interplay of psychological aspects of performance and esports and to clearly define the theoretical foundations of the psychological aspects of esports performance. Underlining the differences from video gaming will inform future research directions and stimulate the development of high-quality practice in the applied field of sports and exercise psychology.
Educational recommender systems (ERSs) aim to adaptively recommend a broad range of personalised resources and activities to students that will most meet their learning needs. Commonly, ERSs operate as a "black box" and give students no insight into the rationale of their choice. Recent contributions from the learning analytics and educational data mining communities have emphasised the importance of transparent, understandable and open learner models (OLMs) that provide insight and enhance learners' understanding of interactions with learning environments. In this paper, we aim to investigate the impact of complementing ERSs with transparent and understandable OLMs that provide justification for their recommendations. We conduct a randomised control trial experiment using an ERS with two interfaces ("Non-Complemented Interface" and "Complemented Interface") to determine the effect of our approach on student engagement and their perception of the effectiveness of the ERS. Overall, our results suggest that complementing an ERS with an OLM can have a positive effect on student engagement and their perception about the effectiveness of the system despite potentially making the system harder to navigate. In some cases, complementing an ERS with an OLM has the negative consequence of decreasing engagement, understandability and sense of fairness.
Making effective use of data generated from players interacting with games (often via playtesting to improve game quality) is a challenging task since the datasets are often mixed and very large. To address this, various visualization techniques have been introduced to help game developers cope with the data. However, there is a gap in research concerning the impact of different visual designs on the interpretation of gameplay data. In this paper, we propose four alternative visual designs of an aggregated visualization and assess how professional game developers interpret the data differently due to the changes in the visual designs. Our results provide an understanding and a supporting argument about the impact of the visual properties transparency and shading (both positive and negative) on the interpretation of the represented data. This is an important contribution to the field of Games User Research given the current move towards data-informed decision making and the popularity of data visualizations.
In the 2017-2018 school year, 38 student teams from 25 high schools across Orange County, California, competed in weekly tournament play of the multiplayer arena battle video game League of Legends, culminating in a championship playoff in one of the first region-wide high school esports leagues in the US. Our team performed a formative evaluation of this League's first year, with special attention to logistical concerns as well as any potential alignments between esports and learning. Our team engaged student League players (n=39) and teachers (n=11) who managed their school's teams in focus groups and observation at six schools; we conducted focus groups with experienced player-coaches (n=5) who supported teams, and one-on-one interviews with parents (n=10). The paper identifies organic learning opportunities during league play such as critical analysis, communication, research skills, and social emotional learning; techniques to clearing administrative hurdles; and structural socioeconomic equity issues that the leadership team may not have anticipated. The paper incorporates recommendations for these areas and critically situates them for future deployment with attention to maximizing learning potential with equity and access for all.
Collegiate esports' rising popularity has created a host of new educational and research opportunities, ranging from understanding and modeling these communities to expanding programs beyond college into high-school learning environments.
Online Collectible Card Games (OCCGs) are enormously popular worldwide. Previous studies found that the social aspects of physical CCGs are crucial for player engagement. However, we know little about the different types of sociability that OCCGs afford. Nor to what extent they influence players' social experiences. This mixed method online survey study focuses on a representative OCCG, Hearthstone [24] to 1) identify and define social design features and examine the extent to which players' use of these features predict their sense of community; 2) investigate participants' attitudes towards and experiences with the game community. The results show that players rarely use social features, and these features contribute differently to predicting players' sense of community. We also found emergent toxic behaviors, afforded by the social features. Findings can inform the best practices and principles in the design of OCCGs, and contribute to our understanding of players' perceptions of OCCG communities.
Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human-AI interaction design principles.
Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-game avatar, known as heroes, selected from a roster of more than 100. The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero. Heroes are designed with different strengths and weaknesses to promote team cooperation in a game. Intuitively, heroes in a strong team should complement each other's strengths and suppress those of opponents. Hero drafting is therefore a challenging problem due to the complex hero-to-hero relationships to consider. In this paper, we propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team's prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have a significantly higher win rate against teams constructed by other baseline and state-of-the-art strategies.
eSports is a rapidly growing phenomenon in competitive gaming. Expertise is an interesting topic to study in relation to eSports, as eSports athletes are considered among the most highly-skilled players of their particular games. Examining expertise not only advances the understanding of what skills compose professional play but enables a deeper study of learning in games; before learning processes are studied, it is important to detail what learning these processes should produce. This study examines expertise through the application of thematic analysis to a series of interviews with professional players of the eSports game, Overwatch. The goal of this study is to identify which skills are perceived to be important to professional-level play by professional players. Two overarching themes were identified, game sense and mechanics. A number of sub-themes were identified as important, including survival, anticipation / prediction, communication, thoughtfulness, aim, ability usage, movement and positioning, and team-based mechanical synergies.
Although its history dates to the beginning of the videogame era, in the recent years eSports has rapidly been maturing into a professional scene that has captured a large and growing audience: the 2017 world finals of one of the most successful pro gaming platforms reached a peak of 75.5 million unique viewers, compared to the NBA finals, which averaged 20 million spectators. Several studies already supported eSports as a legitimate form of sports. In this paper, we focus on identifying the human aspects of eSports and we discuss their relevance in competitive games. To this end, we discuss interviews with sports and eSports professionals, and we analyze the results of a survey distributed to athletes who are pursuing their career in traditional sports and in pro gaming.
We use eSports data to construct an empirical model to measure the effect of diversity on team performance. Different kinds of diversities are considered, diversity of culture, diversity of language and diversity of skill. Our main results are that cultural diversity is beneficial for team performance: the absence of diversity reduces performance by 30%. However, language and experience diversity negatively affect results. Taking the difference in the results into account, we conclude that firms should not thoughtlessly maximize team diversity: different kinds of diversity have different integration and communication costs.
Objective: This research investigates the effect of “critical” team members and team familiarity on team performance in the Multi-player Online Battle Arena gaming environment, League of Legends™. Background: A critical team member is any member of a team whose presence (or absence) can have a dramatic impact on the team’s ability to reach their objective, while team familiarity can be viewed as the knowledge team members have about one another and the knowledge team members have about the tasks that must be accomplished. Methods: Data visualization techniques and logistic regression is used to explore team data collected from publicly accessible sources for the online game League of Legends, which is one of the most popular games in the world. Results: The proportion of time a team’s “Carry” is incapacitated (the “critical” team member) during a given match has a direct impact on how the team performs. Conclusions: The results show that critical team positions exist on teams, and can have a significant effect on achieving the team’s goals. In addition, there is a need for the development of tools, techniques and measures to bring “Big Data” to bear in the study of teamwork. Application: This research illustrates the feasibility of exploring online gaming data for new insights into team performance.
Self-regulated learning (SRL) includes the cognitive, metacognitive, behavioral, motivational, and emotional/affective aspects of learning. It is, therefore, an extraordinary umbrella under which a considerable number of variables that influence learning (e.g., self-efficacy, volition, cognitive strategies) are studied within a comprehensive and holistic approach. For that reason, SRL has become one of the most important areas of research within educational psychology. In this paper, six models of SRL are analyzed and compared; that is, Zimmerman; Boekaerts; Winne and Hadwin; Pintrich; Efklides; and Hadwin, Järvelä and Miller. First, each model is explored in detail in the following aspects: (a) history and development, (b) description of the model (including the model figures), (c) empirical support, and (d) instruments constructed based on the model. Then, the models are compared in a number of aspects: (a) citations, (b) phases and subprocesses, (c) how they conceptualize (meta)cognition, motivation and emotion, (d) top–down/bottom–up, (e) automaticity, and (f) context. In the discussion, the empirical evidence from the existing SRL meta-analyses is examined and implications for education are extracted. Further, four future lines of research are proposed. The review reaches two main conclusions. First, the SRL models form an integrative and coherent framework from which to conduct research and on which students can be taught to be more strategic and successful. Second, based on the available meta-analytic evidence, there are differential effects of SRL models in light of differences in students’ developmental stages or educational levels. Thus, scholars and teachers need to start applying these differential effects of the SRL models and theories to enhance students’ learning and SRL skills.
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.
Team cohesion is a widely known predictor of performance and collaborative satisfaction. However, how it develops and can be assessed, especially in fast-paced ad hoc dynamic teams, remains unclear. An unobtrusive and objective behavioural measure of cohesion would help identify determinants of cohesion in these teams. We investigated team communication as a potential measure in a mixed-methods study with 48 teams (n=135) in the digital game League of Legends. We first established that cohesion shows similar performance and satisfaction in League of Legends. teams as in non-game teams and confirmed a positive relationship between communication word frequency and cohesion. Further, we conducted an in-depth exploratory qualitative analysis of the communication sequences in a high-cohesion and a low-cohesion team. High cohesion is associated with sequences of apology->encouragement, suggestion->agree/acknowledge, answer->answer, and answer->question, while low-cohesion is associated with sequences of opinion/analysis->opinion/analysis, disagree->disagree, command->disagree, and frustration->frustration. Our findings also show that cohesion is important to team satisfaction independently of the match outcomes. We highlight that communication sequences are more useful than frequencies to determine team cohesion via player interactions.
The esports market has been growing exponentially has been growing exponentially with much interest from industry and academia. Perhaps because of this growth, there is a lack of agreement on what esports actually encompasses. We conducted a systematic review of 461 peer reviewed, full papers that provide a definition of esports. Findings highlighted the growth of the esports field across different domains, and increasing global interest in esports, but a lack of consensus regarding definition of the term. Through thematic analysis we identified nine dimensions across esports definitions. We critically assess these dimensions in terms of their representativeness and utility in describing the multifaceted nature of esports. Our work may help create a shared understanding of what esports is- and is not-capturing a diversity of experiences within organized competitive gaming and supporting continued research growth in this increasingly important domain.
Decisions made by human-AI teams (e.g., AI-advised humans) are increasingly common in high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team performance depends on more than just the accuracy of the AI system: Since the human and the AI may have different expertise, the highest team performance is often reached when they both know how and when to complement one another. We focus on a factor that is crucial to supporting such complementary: the human’s mental model of the AI capabilities, specifically the AI system’s error boundary (i.e. knowing “When does the AI err?”). Awareness of this lets the human decide when to accept or override the AI’s recommendation. We highlight two key properties of an AI’s error boundary, parsimony and stochasticity, and a property of the task, dimensionality. We show experimentally how these properties affect humans’ mental models of AI capabilities and the resulting team performance. We connect our evaluations to related work and propose goals, beyond accuracy, that merit consideration during model selection and optimization to improve overall human-AI team performance.
The current study aimed to qualitatively investigate the perceived determinants of success in professional esports athletes. Guided by the bioecological model (Bronfenbrenner & Morris, 2006), thematic analysis was used to explore elite esports athletes’ perceptions of success determinants. Semi-structured interviews were conducted with seven elite esports athletes. The interviews were used to investigate players’ psychology when playing well, training in elite esports, and working in esports teams. Deductive and inductive analyses were conducted to capture the core themes of success in esports within the bioecological model. When playing well, elite esports athletes experienced high levels of confidence, uninterrupted focus, and flow states. Elite esport athletes use mental strategies to help regulate their emotions and remain mindful in the presence of “tilt”. Furthermore, breathing techniques and tactical breaks were used when players felt nervous or when trying to “reset” from a stressful situation. When working in elite esport teams, players reported using strategies to improve team cohesion and that the biggest challenge was interpersonal disagreements. The results contribute to the growing body of literature highlighting the psychological similarities between elite esports and traditional sports athletes and serve as an index for future research into high-performance in esports.
Online social games, played within social networks or games requiring social interaction with peers, are revolutionizing the nature of video-games due to their social aspect and the ability of users to compare their performance with their friends or people in their network. Social comparison features, such as leaderboards, individual scores, achievement badges and level maps, are commonly used in online games to enforce the social interaction of players. However, one of the biggest challenges that the social game industry is currently facing is the ability to increase user enjoyment, and keep its players engaged in the games. To probe more deeply into whether and how players’ continuance intention is influenced by social comparison processes, we combine two theoretical lenses: social comparison theory and self-efficacy theory. We conducted real-world data collection to measure the impact of social comparisons in player perceived enjoyment, online social gaming self-efficacy and game continuance. The results indicate that upward identification and downward contrast are the most influential comparison elements in game continuance. The results of these two comparisons have significant implications for both the theoretical application of social comparison in online settings and for the practical implications of future game design.
Visualizations have become a relevant instrument in games user research and analytics as well as for players to facilitate the analysis of behavioral data, including the evaluation of spatial game metrics. A fundamental task in this respect is to understand how different metrics vary across the game environment and how they relate to each other. In this paper, we evaluate a visualization based on hexagonal binning paired with visual glyphs to represent multivariate information. To study how much information can be encoded in a reasonable manner we created variants with varying complexity and measured correctness and efficiency for different types of tasks through an online study (N = 197). Our results indicate that players appreciated the clear display of multivariate data but that correctness and efficiency were also largely influenced by the visual coding and the task at hand. Based on gained insights, we discuss implications for the design of such and similar gameplay visualizations.
This article advances a tentative theory of skill in relation to eSports. This conjectural theory of skill rests on hypothesizes informed by assumptions from watching 100+ hours of eSport events on Twitch, YouTube, and AfreecaTV and is supported by discussions, reflections and evaluations with eSport players. The case material of this article includes the games Clash Royale (CR), StarCraft 2 (SC2), Counter-Strike: Global Offensive (CS:GO), and online battle arenas (mobas) such as League of Legends (LOL) and Defense of the Ancient 2 (DOTA2). This inferred theory of skill is conjoined and composed of seven strands, which provide a framework for explaining how to understand skill in eSport. These seven strands are presented under the headings: (1) knowledge about game objects, (2) insights into game systems, (3) understanding metagaming, (4), yomi: ‘reading’ the opponent, (5) ability to execute, (6) emotional discipline, and finally (7) team coherency (depending upon whether the skill is associated with a one-versus-one or team-versus-team game). In conclusion, the proposed theory of skill will expose its explanatory power and reach by providing an example of how it can both be implemented to create profiles of players’ skill, and how these profiles in turn can serve as guides to help players improve their skill levels in eSport.
Learner modeling systems so far formulated model learning in three main ways: a learner’s “position” within a lattice of declarative and procedural knowledge about highly structured disciplines such as geometry or physics, a learner’s path through curricular tasks compared to milestones, or profiles of a learner’s achievements on a set of tasks relative to mastery criteria or a peer group. Opening these models to learners identifies for them factors and relations among factors. Open learner models tacitly invite learners to regulate learning. However, contemporary learner models omit data about how learners have and should process information to learn, understand, consolidate and transfer new knowledge and skills. What to do with information opened to learners is opaque. I propose incorporating trace data about learning processes in learner models. Trace data allow generating learning analytics that inform self-regulating learners about potentially productive adaptations to processes they have used to learn. In a context of big data, such elaborated learner models are positioned to collaborate with self-regulating learners. Together, they can coordinate symbiotically, creating a platform for the system to improve its models of learners and for learners to more productively self-regulate learning.
Becoming a competent player of online games involves complex processes and networks of online and offline life where the player is socialized into social norms and expectations. An important aspect of what constitutes gamers learning trajectories is guidance from experienced players. Games are public spheres where learning is social and distributed and where players often are enabled to learn new and advanced competencies. However, there is little educational research on how these competencies are cultivated and employed within a competitive gaming scene. In the current paper, we analyze the mentor-apprentice relationship between an expert and a novice in the multiplayer FPS CS:GO within an eSports and educational context. By assuming a dialogic approach to meaning making, we will examine how novices and experts uphold and talk the relationship into being and how the peer teaching and learning manifests in the in-game interaction. The ethnographic data was collected in collaboration with a vocational school with an eSports program in Finland in 2017-2018. Students (aged 17-18, all male) playing CS:GO shared screen recordings of their matches and took part in interviews. The participants play in two different teams. Here, we focus on Martin (expert) and John (novice) from team one. Martin was the highest ranked team member, something his team members are aware of and make relevant in interviews and in-game interactions. This position seems to provide him authority and leadership within the team. In the interviews, Martin aligns with being the leader and repeatedly mentions that he coached John to become part of the team. This relationship is also evident in the in-game data where Martin, together with the rest of the team, often provides feedback and support for John. The learning appears to be how to become competent in the game, and there are strong indications of other aspects of learning that relate to sociality and leadership.
The open learner model (OLM) represents the knowledge or skill levels of learners in various ways, encouraging learners to actively participate in thinking about and crafting their own learning. Despite the important roles that OLMs play in higher education to support the learning process and self-regulated learning (SRL) in particular, there are few studies systematically reviewing OLM technology in higher education, and investigating their potential to foster self-regulated learning. Therefore, we carried out a systematic review of a 30-year sample of OLM studies in higher education and identified 64 articles that study the use of OLMs in supporting SRL. Our findings show that OLMs have been mainly used to support learners' cognition and a bit less metacognition and motivation; however, emotional support has been rarely provided. The most supported ones are Appraisal and Performance phases; Preparation of learning is enhanced by OLMs not so often. Although learners can edit or negotiate with their learning model in advanced ways, a simple inspectable OLM is more preferred. Reliance on unobservable nodes is less favored in modeling techniques in OLMs because such methods are highly dependent on expert authoring, thereby time-intensive and costly. Comparison and color-coding are two most-used features in OLMs, where the comparison feature is often used for enhancing learners’ engagement and motivation.
In this paper, we explore the ways in which a collegiate esports team’s play and performance underscore micro-level shifts in learning, domain mastery, and expertise through simultaneously collaborative and competitive gameplay. Specifically, with this aim, we evaluate how esports’ high-stakes team play and organizational activities provide evidence of processes and practices that are important for learning-relevant trajectories in and beyond higher education. Throughout the course of a threegame match in a major collegiate esports tournament, players demonstrated decision-making, reflection and dimensions of individual and collaborative learning. We also found support for improved meta-gaming knowledge – or distributed, communitycentered knowledge around the game – which underscored players’ domain learning and growth. Our findings highlightevidence of perceptual learning, as demonstrated through the players’ flexibility in adapting to increasingly complex challenges. We propose that these findings emphasize the importance of esports as meaningful and noteworthy learning ecologies which need to be more deeply examined in light of historic gender and racial barriers to educational and professional aspirations in gaming.
eSports matches offer fast-paced entertainment for millions of viewers worldwide, but little is known about how to support a positive viewer experience. One of the key challenges related to popular real-time eSports games (e.g., multiplayer online battle arena games or first-person shooters) is empowering viewers to effectively follow rapid gameplay. In our paper, we address this challenge through the design of information dashboards to improve spectator insight and experience in League of Legends, and Counter Strike: Global Offensive. Based on surveys that received a total of 788 responses, we design information dashboards that we evaluate with 18 experienced eSports viewers. Our results show that dashboards contribute to spectator insight and experience, but that careful consideration is necessary to adequately manage in-game complexity and cognitive load of viewers, and establish spectator trust in information dashboards through transparent design. Based on these findings, our paper formulates design goals for spectator dashboards, and outlines key opportunities for future work.