@conference{sangster16hfes,
title = {Big Data Meets Team Expertise in a Dynamic Task Environment},
author = {Sangster, Matthew-Donald D. and Mendonca, David J. and Gray, Wayne D.},
year = {2016},
date = {2016-09-21},
booktitle = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
volume = {60},
number = {1},
pages = {158-162},
publisher = {Sage },
abstract = {Objective; This research employs large-scale data from a massively multiplayer online game to examine the links between the composition, processes and outcomes of teams operating in high tempo, data-rich environments. Background: Research on the performance of teams-- particularly over long time scales--is often expensive and time-consuming. But Big Data from competitive, team-based games can mitigate these costs. Methods: Data visualization techniques are used to explore team data harvested from publicly accessible sources for the online game League of Legends™, one of the most popular such games in the world. Results: The exploratory results suggest potentially complex relationships between team composition, processes and outcomes, and in particular how team composition and process may unfold over longer time spans. Conclusions: The results point to the potentially substantial benefits of large-scale studies of teamwork, and--in parallel--to the need for the development of tools, techniques and measures to bring Big Data to bear in teamwork studies. Application: This work demonstrates the feasibility of exploring online gaming data for new insights into team and individual performance.},
keywords = {dynamic task environment, League of Legends, LoL, team, team expertise},
pubstate = {published},
tppubtype = {conference}
}
Objective; This research employs large-scale data from a massively multiplayer online game to examine the links between the composition, processes and outcomes of teams operating in high tempo, data-rich environments. Background: Research on the performance of teams-- particularly over long time scales--is often expensive and time-consuming. But Big Data from competitive, team-based games can mitigate these costs. Methods: Data visualization techniques are used to explore team data harvested from publicly accessible sources for the online game League of Legends™, one of the most popular such games in the world. Results: The exploratory results suggest potentially complex relationships between team composition, processes and outcomes, and in particular how team composition and process may unfold over longer time spans. Conclusions: The results point to the potentially substantial benefits of large-scale studies of teamwork, and--in parallel--to the need for the development of tools, techniques and measures to bring Big Data to bear in teamwork studies. Application: This work demonstrates the feasibility of exploring online gaming data for new insights into team and individual performance.
@inbook{gray18strugmann,
title = {The Essence of Interaction in Boundedly Complex, Dynamic Task Environments},
author = {Gray, Wayne D. and Destefano, Marc and Lindstedt, John K. and Sibert, Catherine and Sangster, Matthew-Donald D.},
editor = {Gluck, K. A. and Laird, J. E.},
isbn = {978-0-262-03882-9},
pages = {147-165},
publisher = {The MIT Press},
address = {Cambridge, Massachusetts},
chapter = {10},
series = {Strungmann Forum Reports},
abstract = {Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or due to the actions of agents in that environment. In dynamic environments, the agent's choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain ``best'' as the task environment changes. In this paper, we summarize work in progress which is bringing the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.},
keywords = {dynamic task environment, interactive behavior, Tetris},
pubstate = {published},
tppubtype = {inbook}
}
Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or due to the actions of agents in that environment. In dynamic environments, the agent's choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain ``best'' as the task environment changes. In this paper, we summarize work in progress which is bringing the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.
