Serious Games Analytics

“Advances in Game-Based Learning” Book Series by Springer

Serious Games Analytics (book cover)

Serious Games Analytics (book cover)

“Serious Games Analytics”

By Loh, C.S., Sheng, Y., & Ifenthaler, D. [Eds.] (2015). Springer International Publishing Switzerland.
[Note: More than 70 authors contributed 19 chapters in six sections to the book. Serious Games Analytics is now available on Springer and Amazon.]

Serious games is an emerging field where the games are supposed to be created using sound learning theories and instructional design principles to maximize learning and training success. In 2013, Springer published an edited volume entitled Game Analytics: Maximizing the Value of Player Data (Canossa, Seif El-Nasr, & Drachen, 2013). On the surface, it would appear that game analytics is applicable to serious games also. However, the emphasis of the two industries are really quite different because the motivation for game analytics (GA) is monetization (i.e., maximizing the monetary value of user-generated data); whereas the motivation for serious games analytics (SEGA) is to convert user-generated data into actionable insights (i.e., analytics) for performance measurement, assessment, and improvement.

But how would stakeholders know what play-learners have done in the game environment, and if the actions performance brings about learning? With current serious games, play-learners could be:

  • Playing the game for fun – too much entertainment and little learning
  • ‘Gaming’ the system – take advantage of loopholes in the system to fake ‘progress’ in meeting certain requirement
  • Really learning (but mixed with lots of playing or distraction…)

Because few existing serious game systems actually contain proper progress tracking to show evidence of performance improvement (i.e., analytics), stakeholders are unsure of what really happen in the systems. Without proper theories and methodologies to guide development, current assessment system (if any) are mostly in the form of log files, leaving the real analysis to specialized data analysts. Unfortunately, since most learning organizations do not have the luxury to employ specialized data analyst, most of the log files are not being put to good use. Moreover, because there is no concerted effort to standardize the data collected, there is no way to know if the data collected are even useful, or compatible across systems.

Recent related works in games analytics have come from the field of computer science (Moura, Seif El-Nasr, & Shaw, 2011), software engineering, human-computer interactions (e.g., Bellotti, Kapralos, Lee, & Moreno-Ger, 2013), and instructional technology (Loh, 2006, 2012; Loh & Sheng, 2013). It is to be expected that serious games analytics and game analytics would share much similarities in telemetric data collection (e.g., Zoeller, 2013), tracing players’ actions and behaviors for profiling and modeling (e.g., Dixit & Youngblood, 2008; Thawonmas & Iizuka, 2008), and the visualization of player-actions (e.g., Medler, John, & Lane, 2011; Scarlatos & Scarlatos, 2010; Wallner, 2013).

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As of April 2016, the book chapters have been downloaded more than 6000 time on Springer according to Bookmetrix!

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