Understanding Human Skill Acquisition through Statistical Modelling of Big Data in eSports

点击翻译按钮获取中文摘要

Expert knowledge and skill play an increasingly important role in society, with an economy which is increasingly knowledge-driven and which involves rapid technological change and longer and longer working lives. Game players develop profound skill over hours, days and even years of practice, generating huge reservoirs of data about gameplay and skill development that can be easily and unobtrusively recorded. The result is an unparalleled opportunity to investigate what aspects of play determine expert skill, and how it is acquired.The topic of the proposed PhD is to test theories of skill acquisition and characterise expert skill in gaming through the statistical interrogation of naturally occurring data in eSports games. The research would be conducted under the joint supervision of Professor Anders Drachen from the University of York and Dr Tom Stafford from the University of Sheffield under the IGGI research theme of using eSports data to understand human psychology. The proposed research fits well with available links to the eSports industry (i.e., DC Labs), available expertise (e.g., Professor Alex Wade), and ongoing analytics at IGGI into performance and skill acquisition through the analysis of player data from major online games (e.g., League of Legends, DOTA 2, Destiny)The cognitive science of skill acquisition has broad applicability and is of practical significance for the eSports industry. Developing measures of skill that are predictive of high level performance can allow professional teams to identify talent more effectively, and allow game developers to engineer more effective matchmaking systems. Understanding what aspects of play accelerate skill acquisition also presents an opportunity to design more effective tutorials, engineer feedback systems that help novice to professional players maximise their rate of learning by suggesting changes to gameplay behaviour, and optimise training behaviours for competition.Beyond gaming, skill acquisition research is of tremendous import for domains such as surgery and military aviation, where technical advancements and performance conditions often outpace the rate at which skilled personnel can adapt to them. Understanding how to optimise learning can significantly impact these arenas, as the success of an operation can depend upon an individual's ability to adapt in good time.Previous research into expertise and skill acquisition has generated much knowledge about practice, feedback, and transfer, but has largely relied on laboratory findings and retrospective accounts of practice behaviour. By applying statistical modelling and data visualisation techniques on large, real-world data sets from eSports, the current research would bypass the drawbacks of laboratory methods to generate original findings about skill learning at high levels of statistical confidence. Taken together, the impact of the proposed research could inform coaching practices and game development in eSports, improve our current understanding about the psychology underpinning skill acquisition, and motivate subsequent scientific inquiry into the domain of learning.Keywordsprediction modelingskill predictionmachine learningsports analyticsperformance analysis