Adapting Cognitive Task Analysis to Elicit the Skill Chain of a Game

Britton Horn, Seth Cooper, Christoph Sebastian Deterding

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Playing a game is a complex skill that comprises a set of more basic skills which map onto the component mechanics of the game. Basic skills and mechanics typically build and depend on each other in a nested learning hierarchy, which game designers have modelled as skill chains of skill atoms. For players to optimally learn and enjoy a game, it should introduce skill atoms in the ideal sequence of this hierarchy or chain. However, game designers typically construct and use hypothetical skill chains based solely on design intent, theory, or personal observation, rather than empirical observation of players. To address this need, this paper presents an adapted cognitive task analysis method for eliciting the empirical skill chain of a game. A case study illustrates and critically reflects the method. While effective in foregrounding overlooked low-level skills required by a game, its efficiency and generalizability remain to be proven.
Original languageEnglish
Title of host publicationCHI PLAY'17
Number of pages13
ISBN (Print)9781450348980
Publication statusPublished - 1 Aug 2017
EventThe ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play - Parkhuis de Zwijger, Amsterdam, Netherlands
Duration: 15 Oct 201718 Oct 2017


ConferenceThe ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
Abbreviated titleCHI PLAY 2017
Internet address

Bibliographical note

© The Authors, 2017.

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