“Because I'm Bad at the Game!” A Microanalytic Study of Self Regulated Learning in League of Legends

Abstract

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.

Computer gaming and academic learning – Targeting the role of esport in school

“…Esports mirrors sports and academic domains, in that those with greater self-regulation outperform their peers with less developed self-regulatory skills (Trotter, Coulter, Davis, Poulus, & Polman, 2021, Trotter, Obine, & Sharpe, 2023. Kleinman, Gayle, and Seif El-Nasr (2021) suggests that esports may be an engaging domain to train SRL skills, which could then transfer into other domains such as academia. However, to date, no research has explicitly explored this possibility empirically.…”

Self-regulation, stress appraisal, and esport action performance

“…The Miller and Brown (1991) and Zimmerman (2000) models of self-regulation have been previously used to measure selfregulation in esports (Kleinman et al, 2021;Trotter et al, 2021Trotter et al, , 2022. This study has chosen to use the Miller and Brown (1991) model, which includes seven phases of self-regulation (i.e., information input, self-evaluation, instigation to change, search, planning, implementation and plan evaluation) to allow for comparison of this studies results, with quantitative self-regulation results from a previously measured global population of e'athletes (Trotter et al, 2021).…”

“…Although limited, this evidence suggests that like other sporting domains, self-regulatory skills may play a crucial role in esports performance. Research has also indicated differences in self-regulated learning between experts and novice e’athletes ( Kleinman et al, 2021 ). Specifically, in the preparatory phase, novices were found to prioritize process goals more frequently than experts.…”

“…Specifically, in the preparatory phase, novices were found to prioritize process goals more frequently than experts. Additionally, Kleinman et al (2021) discovered that more advanced League of Legends (LoL) players exhibited greater structure in their practice routines compared to novice LoL players. It was hypothesized that this could be attributed to the design of LoL, as the game itself records players’ progress and provides information, potentially reducing the need for extensive monitoring or self-reflection among LoL players.…”

Systematic Framework for Esports Training: Informing eSports Training with the Learning Sciences

“…In esports, having good meta-cognition means that one could plan, analyze and set goals for in-game performance improvements (Kleinman et al, 2021), identify how and why a mistake was made and find the right way to fix it (Himmelstein et al, 2017), make sense of the performance data to engage in self-tracking and -reflecting behaviors (Rapp, 2018), and search for external resources to develop retainable knowledge (Golub, 2010). Certain game designs facilitate such skills.…”