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Serious Games AnalyticsUsing Pattern Matching to Assess Gameplay

Serious Games Analytics: Using Pattern Matching to Assess Gameplay [In this chapter we describe Analysis of Patterns in Time (APT) and how it can be used to analyze gameplay choices to provide evidence of a play-learner’s understanding of concepts modeled in a game. APT is an empirical approach to observing and coding phenomena as mutually exclusive and exhaustive categories within classifications. These data form a temporal map of joint and sequential patterns. We examine the case of the online Diffusion Simulation Game. An algorithm calculates scores for gameplay data patterns and compares them with scores for patterns based on optimal strategies derived from the game’s conceptual model. We discuss the results of using APT for analysis of game sessions for three play-learners. We describe how APT can be included as part of a serious game to conduct formative assessment and determine appropriate hints, coaching, or other forms of scaffolding during gameplay. We conclude by discussing APT methods for summative assessment.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Serious Games AnalyticsUsing Pattern Matching to Assess Gameplay

Part of the Advances in Game-Based Learning Book Series
Editors: Loh, Christian Sebastian; Sheng, Yanyan; Ifenthaler, Dirk
Serious Games Analytics — Mar 13, 2015

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References (27)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-05833-7
Pages
435 –458
DOI
10.1007/978-3-319-05834-4_19
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter we describe Analysis of Patterns in Time (APT) and how it can be used to analyze gameplay choices to provide evidence of a play-learner’s understanding of concepts modeled in a game. APT is an empirical approach to observing and coding phenomena as mutually exclusive and exhaustive categories within classifications. These data form a temporal map of joint and sequential patterns. We examine the case of the online Diffusion Simulation Game. An algorithm calculates scores for gameplay data patterns and compares them with scores for patterns based on optimal strategies derived from the game’s conceptual model. We discuss the results of using APT for analysis of game sessions for three play-learners. We describe how APT can be included as part of a serious game to conduct formative assessment and determine appropriate hints, coaching, or other forms of scaffolding during gameplay. We conclude by discussing APT methods for summative assessment.]

Published: Mar 13, 2015

Keywords: Pattern matching; Gameplay strategies; Assessment; Models; Scaffolding

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