Name
Abstract | ||
|---|---|---|
Playing a game is a complex skill comprising a set of more basic skills. These skills commonly map onto the main mechanics of the game, and build and depend on each other in a nested learning hierarchy, which game designers have modeled as skill chains made of skill atoms. For players to optimally learn and enjoy a game, it should introduce skill atoms in the ideal sequence of this learning hierarchy. 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. This risks creating incomplete or suboptimal progression designs. In response, 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. |
Year | Venue | Keywords |
|---|---|---|
2017 | CHI PLAY'17: PROCEEDINGS OF THE ANNUAL SYMPOSIUM ON COMPUTER-HUMAN INTERACTION IN PLAY | cognitive task analysis, game atoms, learning hierarchy, skill atoms, skill chains |
Field | DocType | Citations |
Basic skills,Task analysis,Simulation,Computer science,Algorithmic game theory,Simulations and games in economics education,Sequential game,Screening game,Hierarchy,Non-cooperative game | Conference | 1 |
PageRank | References | Authors |
0.41 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Britton Horn | 1 | 5 | 0.85 |
| Seth Cooper | 2 | 745 | 58.65 |
| Sebastian Deterding | 3 | 1406 | 99.86 |