Learning curves: analysing pace and challenge in four successful puzzle games

Linehan, Conor and Bellord, George and Kirman, Ben and Morford, Zachary H. and Roche, Bryan (2014) Learning curves: analysing pace and challenge in four successful puzzle games. CHI PLAY '14 Proceedings of the first ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play . pp. 181-190. ISSN UNSPECIFIED

Full content URL: http://dl.acm.org/citation.cfm?id=2658537.2658695&...

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Learning curves: analysing pace and challenge in four successful puzzle games
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Abstract

The pace at which challenges are introduced in a game has long been identified as a key determinant of both the enjoyment and difficulty experienced by game players, and their ability to learn from game play. In order to understand how to best pace challenges in games, there is great value in analysing games already demonstrated as highly engaging. Play-through videos of four puzzle games (Portal, Portal 2 Co-operative mode, Braid and Lemmings), were observed and analysed using metrics derived from a behavioural psychology understanding of how people solve problems. Findings suggest that; 1) the main skills learned in each game are introduced separately, 2) through simple puzzles that require only basic performance of that skill, 3) the player has the opportunity to practice and integrate that skill with previously learned skills, and 4) puzzles increase in complexity until the next new skill is introduced. These data provide practical guidance for designers, support contemporary thinking on the design of learning structures in games, and suggest future directions for empirical research.

Additional Information:Presented at CHI Play 2014 : the first ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, Toronto, Canada 19-22 October 2014
Keywords:learning curve, educational game, Education, complexity, flow, behavioural psychology, Videogames design
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
X Education > X990 Education not elsewhere classified
Divisions:College of Science > School of Computer Science
ID Code:15794
Deposited On:24 Oct 2014 09:55

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