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Tracking precursors of learning analytics over serious game team performance ranking.

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Title: Tracking precursors of learning analytics over serious game team performance ranking.
Authors: Capatina, Alexandru, Bleoju, Gianita, Rancati, Elisa, Hoareau, Emilie
Source: Behaviour & Information Technology. Oct/Nov2018, Vol. 37 Issue 10/11, p1008-1020. 13p. 10 Charts, 4 Graphs.
Subjects: Universities & colleges, Ability, Business, Comparative studies, Conceptual structures, Decision making, Interprofessional relations, Learning strategies, Marketing, Memory, School environment, Transfer of training, Video games, Teams in the workplace, Training, Qualitative research, Task performance, Data analytics
Geographic Terms: Europe
Abstract: The use of serious games to improve collaborative skill transfer and retention has received considerable attention from scholars, web marketing practitioners and business consultants. Team rankings and learning progress in game learning analytics, however, have yet to be empirically examined. Using fuzzy-set qualitative comparative analysis to study the performance of competing teams in a web marketing serious game (Simbound), we highlight a combination of causal conditions (engagement, reach and profitability) affecting team rankings. This paper proposes a conceptual architecture of the forces that influence learning progress within a collaborative learning environment. This learning environment is studied for web marketing boot camps powered by Simbound at three European universities: Grenoble Alpes University (France), University of Milano-Bicocca (Italy) and Dunarea de Jos University of Galati (Romania). Gaining knowledge of cases through game learning analytics is valuable for two reasons: It emphasises the instructor’s role in mobilising players’ engagement, and it tests variability across cases, offering precursors of team performance rankings. This approach to collective skill retention highlights the moderating factors of team performance rankings, whilst purposely calibrating a gameable learning environment. This paper enriches our knowledge of how active experimentation in learning analytics metrics can develop skills for real business competition. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Tracking precursors of learning analytics over serious game team performance ranking.
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  Data: <searchLink fieldCode="AR" term="%22Capatina%2C+Alexandru%22">Capatina, Alexandru</searchLink><br /><searchLink fieldCode="AR" term="%22Bleoju%2C+Gianita%22">Bleoju, Gianita</searchLink><br /><searchLink fieldCode="AR" term="%22Rancati%2C+Elisa%22">Rancati, Elisa</searchLink><br /><searchLink fieldCode="AR" term="%22Hoareau%2C+Emilie%22">Hoareau, Emilie</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Oct/Nov2018, Vol. 37 Issue 10/11, p1008-1020. 13p. 10 Charts, 4 Graphs.
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  Data: The use of serious games to improve collaborative skill transfer and retention has received considerable attention from scholars, web marketing practitioners and business consultants. Team rankings and learning progress in game learning analytics, however, have yet to be empirically examined. Using fuzzy-set qualitative comparative analysis to study the performance of competing teams in a web marketing serious game (Simbound), we highlight a combination of causal conditions (engagement, reach and profitability) affecting team rankings. This paper proposes a conceptual architecture of the forces that influence learning progress within a collaborative learning environment. This learning environment is studied for web marketing boot camps powered by Simbound at three European universities: Grenoble Alpes University (France), University of Milano-Bicocca (Italy) and Dunarea de Jos University of Galati (Romania). Gaining knowledge of cases through game learning analytics is valuable for two reasons: It emphasises the instructor’s role in mobilising players’ engagement, and it tests variability across cases, offering precursors of team performance rankings. This approach to collective skill retention highlights the moderating factors of team performance rankings, whilst purposely calibrating a gameable learning environment. This paper enriches our knowledge of how active experimentation in learning analytics metrics can develop skills for real business competition. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0144929X.2018.1474949
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 1008
    Subjects:
      – SubjectFull: Universities & colleges
        Type: general
      – SubjectFull: Ability
        Type: general
      – SubjectFull: Business
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Conceptual structures
        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Interprofessional relations
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      – SubjectFull: Learning strategies
        Type: general
      – SubjectFull: Marketing
        Type: general
      – SubjectFull: Memory
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      – SubjectFull: School environment
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      – SubjectFull: Transfer of training
        Type: general
      – SubjectFull: Video games
        Type: general
      – SubjectFull: Teams in the workplace
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      – SubjectFull: Training
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      – SubjectFull: Qualitative research
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      – SubjectFull: Task performance
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      – SubjectFull: Data analytics
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      – SubjectFull: Europe
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      – TitleFull: Tracking precursors of learning analytics over serious game team performance ranking.
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              Text: Oct/Nov2018
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