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Measuring Expert Performance for Serious Games Analytics: From Data to Insights

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Book cover Serious Games Analytics

Part of the book series: Advances in Game-Based Learning ((AGBL))

Abstract

Advances in technology have made it possible to trace players’ actions and behaviors (as user-generated data) within online serious gaming environments for performance measurement and improvement purposes. Instead of a Black box approach (such as pretest/posttest), we can approach serious games as a White box, assessing performance of play-learners by manipulating the performance variables directly. In this chapter, we describe the processes to obtain user-generated gameplay data in situ using serious games for training—i.e., data tracing, cleaning, mining, and visualization. We also examine ways to differentiate expert-novice performances in serious games, including behavior profiling. We introduce a new Expertise Performance Index, based on string similarities that take into account the “course of actions” chosen by experts and compare that to those of the novices. The Expertise Performance Index can be useful as a metric for serious games analytics because it can rank play-learners according to their competency levels in the serious games.

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Acknowledgments

Research projects from the Virtual Environment Lab (V-Lab) have been made possible in part through funding from the Defense University Research Instrumentation Program (DURIP) from the U.S. Army Research Office. The authors would like to thank Ms. Ariel Yining Loh for her help in editing the chapter.

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  1. Virtual Environment Lab (V-Lab), Southern Illinois University, 625 Wham Drive, Mailcode 4610, Carbondale, IL, 62901-4610, USA

    Christian Sebastian Loh

  2. Department of Counseling, Quantitative Methods, and Special Education, Southern Illinois University, 625 Wham Drive, Mailcode 4618, Carbondale, IL, 62901-4618, USA

    Yanyan Sheng

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  1. Christian Sebastian Loh
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Correspondence to Christian Sebastian Loh .

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Editors and Affiliations

  1. Virtual Environment Lab (V-Lab), Southern Illinois University, Carbondale, Illinois, USA

    Christian Sebastian Loh

  2. Department of Counseling, Quantitative Methods, and Special Education, Southern Illinois University, Carbondale, Illinois, USA

    Yanyan Sheng

  3. University of Mannheim, Mannheim, Germany

    Dirk Ifenthaler

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Loh, C.S., Sheng, Y. (2015). Measuring Expert Performance for Serious Games Analytics: From Data to Insights. In: Loh, C., Sheng, Y., Ifenthaler, D. (eds) Serious Games Analytics. Advances in Game-Based Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-05834-4_5

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