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- Publisher
- Springer International Publishing
- Copyright
- © Springer International Publishing Switzerland 2015
- ISBN
- 978-3-319-05833-7
- Pages
- 101 –134
- DOI
- 10.1007/978-3-319-05834-4_5
- Publisher site
- See Chapter on Publisher Site
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.]
Published: Mar 13, 2015
Keywords: In situ data; Expert-novice; Action sequence; Performance metrics; Expertise performance index; Similarity measure