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Serious Games AnalyticsCluster Evaluation, Description, and Interpretation for Serious Games

Serious Games Analytics: Cluster Evaluation, Description, and Interpretation for Serious Games [This chapter describes cluster evaluation, description, and interpretation for evaluating player profiles based on log files available from a game server. Calculated variables were extracted from these logs in order to characterize players. Using circular statistics, we show how measures can be extracted that enable players to be characterized by the mean and standard deviation of the time that they interacted with the server. Feature selection was accomplished using a correlation study of variables extracted from the log data. This process favored a small number of the features, as judged by the results of clustering. The techniques are demonstrated based on a log file data set of the popular online game Minecraft. Automated clustering was able to suggest groups that Minecraft players fall into. Cluster evaluation, description, and interpretation techniques were applied to provide further insight into distinct behavioral characteristics, leading to a determination of the quality of clusters, using the Silhouette Width measure. We conclude by discussing how the techniques presented in this chapter can be applied in different areas of serious games analytics.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Serious Games AnalyticsCluster Evaluation, Description, and Interpretation for Serious Games

Part of the Advances in Game-Based Learning Book Series
Editors: Loh, Christian Sebastian; Sheng, Yanyan; Ifenthaler, Dirk
Cornforth, David J.; Adam, Marc T. P.
Serious Games Analytics — Mar 13, 2015
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References (36)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-05833-7
Pages
135 –155
DOI
10.1007/978-3-319-05834-4_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes cluster evaluation, description, and interpretation for evaluating player profiles based on log files available from a game server. Calculated variables were extracted from these logs in order to characterize players. Using circular statistics, we show how measures can be extracted that enable players to be characterized by the mean and standard deviation of the time that they interacted with the server. Feature selection was accomplished using a correlation study of variables extracted from the log data. This process favored a small number of the features, as judged by the results of clustering. The techniques are demonstrated based on a log file data set of the popular online game Minecraft. Automated clustering was able to suggest groups that Minecraft players fall into. Cluster evaluation, description, and interpretation techniques were applied to provide further insight into distinct behavioral characteristics, leading to a determination of the quality of clusters, using the Silhouette Width measure. We conclude by discussing how the techniques presented in this chapter can be applied in different areas of serious games analytics.]

Published: Mar 13, 2015

Keywords: Cluster evaluation; Cluster description; Cluster interpretation; Player profiles; Cognitive performance

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