Serious games analytics: Theoretical framework

“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 i...

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Main Authors: Loh, C., Sheng, Y., Ifenthaler, Dirk
Format: Book Chapter
Published: 2015
Online Access:http://hdl.handle.net/20.500.11937/34345
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author Loh, C.
Sheng, Y.
Ifenthaler, Dirk
author_facet Loh, C.
Sheng, Y.
Ifenthaler, Dirk
author_sort Loh, C.
building Curtin Institutional Repository
collection Online Access
description “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 specific 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—specific 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 identified as Serious Games Analytics—to properly measure, assess, and improve performance with serious games.
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spelling curtin-20.500.11937-343452017-09-13T15:12:58Z Serious games analytics: Theoretical framework Loh, C. Sheng, Y. Ifenthaler, Dirk “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 specific 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—specific 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 identified as Serious Games Analytics—to properly measure, assess, and improve performance with serious games. 2015 Book Chapter http://hdl.handle.net/20.500.11937/34345 10.1007/978-3-319-05834-4_1 restricted
spellingShingle Loh, C.
Sheng, Y.
Ifenthaler, Dirk
Serious games analytics: Theoretical framework
title Serious games analytics: Theoretical framework
title_full Serious games analytics: Theoretical framework
title_fullStr Serious games analytics: Theoretical framework
title_full_unstemmed Serious games analytics: Theoretical framework
title_short Serious games analytics: Theoretical framework
title_sort serious games analytics: theoretical framework
url http://hdl.handle.net/20.500.11937/34345