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

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.