Assessment in Game-Based Learning

Ifenthaler, Dirk; Eseryel, Deniz; Ge, Xun

doi:10.1007/978-1-4614-3546-4

Cited by 115 publications

(25 citation statements)

References 349 publications

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“…Since serious games are nothing more than software application, it is easy for computer scientists and software engineers to directly manipulate the variables and functions in serious games to track what play-learners actually do in the game environment. In situ data collection can be made possible via: (a) data dump as log data, (b) game telemetry (popularized in the entertainment game development circles, by Zoeller, 2013 ), and (c) Information Trails (originated from the instructional design circle, by Loh, 2006Loh, , 2012b.…”

Section: In Situ Vs Ex Situ Data Collectionmentioning

confidence: 99%

Serious Games Analytics: Theoretical Framework

Loh

Sheng

Ifenthaler

2015

Serious Games Analytics

122

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"Serious Games" is a unique industry that is concerned with the training/ learning performance assessment of its clients. It is one of three digital technology industries (along with digital games, and online learning) that are rapidly advancing into the arena of analytics. The analytics from these industries all came from the tracing of user-generated data as they interacted with the systems, but differed from one another in the primary purposes for such analytics. For example, the purpose of game analytics is to support the growth of digital (entertainment) games, while that of learning analytics is to support the online learning industries. Although some game and learning analytics can indeed be used in serious games, they lack specifi c metrics and methods that outline the effectiveness of serious games-an important feature to stakeholders. Serious Games Analytics need to provide ( actionable ) insights that are of values to the stakeholders-specifi c strategies/policies to improve the serious games, and to (re)train or remediate play-learners for performance improvement. Since the performance metrics from one industry are unlikely to transfer well into another industry, those that are optimal for use in the Serious Games industry must be properly identifi ed as Serious Games Analytics -to properly measure, assess, and improve performance with serious games.

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Section: In Situ Vs Ex Situ Data Collectionmentioning

confidence: 99%

Serious Games Analytics: Theoretical Framework

Loh

Sheng

Ifenthaler

2015

Serious Games Analytics

122

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“…More than just telemetry, Information Trails (Loh, 2006(Loh, , 2012bLoh, Anantachai, Byun, & Lenox, 2007) is a serious games assessment framework (from the field of Instructional Design and Technology) that takes into consideration the need for in situ data collection, telemetry, data mining, and data visualization for performance assessment. There are two parts to Information Trails: the online Serious Games data collection framework and a separate visualization component called Performance Tracing Report Assistant (PeTRA), as shown in Fig.…”

Section: The Information Trails Assessment Frameworkmentioning

confidence: 99%

“…Once the play-learners' actions have been captured from within the serious games, researchers can then (re)trace what actions players performed within the game, visualize their navigational paths, and make sense of interesting patterns therein (see Loh, 2012b;Scarlatos & Scarlatos, 2010;Thawonmas & Iizuka, 2008).…”

Section: In Situ Data and White Boxmentioning

confidence: 99%

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

Loh

Sheng

2015

Serious Games Analytics

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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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“…How then, do we assess performance in a virtual 'serious-game' environment when we can neither directly (a) measure learning that occurs in the mind, nor (b) observe the actions that occur in a non-physical environment? In other words, the only solution is to find a way to measure the players' performance according to their actions performed within the training environment in situ as evidence of their learning (Loh, 2007;Loh, 2012b;Schmidt & Lee, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…As serious games are ''designed to support knowledge acquisition and/or skill development,'' not only is player-performance assessment an important, if not the most important, aspect of serious game evaluation (Bellotti, Kapralos, Lee, & Moreno-Ger, 2013;Loh, Anantachai, Byun, & Lenox, 2007;Shute et al, 2010), it is also a necessary component for these games to set themselves apart from other entertainment games (Michael & Chen, 2006). Since the advances in technology have made it increasingly easy to trace players' in-game actions and behaviors for performance assessment (Loh, 2012b;Thawonmas & Iizuka, 2008;Wallner, 2013), stakeholders in the training and learning industries have begun asking for ''measurable evidence of training or learning'' to justify their investment and to ensure a good rate of return (Loh, 2011).…”

Section: Introductionmentioning

confidence: 99%