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 modeled 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.
For citizen science games (CSGs) to be successful in advancing scientific research, they must effectively train players. Designing tutorials for training can be aided through developing a skill chain of required skills and their dependencies, but skill chain development is an intensive process. In this work, we hypothesized that free recall may be a simpler yet effective method of directly eliciting skill chains. We elicited 23 skill chains from players and developers and augmented our reflexive thematic analysis with 11 semi-structured interviews in order to determine how players and developers conceptualize skill trees and whether free recall can be used as an alternative to more resource-intensive cognitive task analyses. We provide three main contributions: (1) a comparison of skill chain conceptualizations between players and developers and across prior literature; (2) insights to the process of free recall in eliciting CSG skill chains; and (3) a preliminary toolkit of CSG skill-based design recommendations based on our findings. We conclude CSG developers should: give the big picture up front; embrace social learning and paratext use; reinforce the intended structure of knowledge; situate learning within applicable, meaningful contexts; design for discovery and self-reflection; and encourage practice and learning beyond the tutorial. Free recall was ineffective for determining a traditional skill chain but was able to elicit the core gameplay loops, tutorial overviews, and some expert insights.
In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots — automated player simulations based on models of players with varying sets of skills — to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.