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  • Universal tools for measuring games and learning: Dynamic causal nets

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    TornqvistPUB2260.pdf (1.031Mb)
    Author(s)
    Tornqvist, Dominicus
    Wen, Lian
    Tichon, Jennifer
    Griffith University Author(s)
    Wen, Larry
    Year published
    2017
    Metadata
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    Abstract
    How can one measure the learning outcome of playing a serious game? We need an objective measure of the learning contents included in a game. The research is diverse, utilizing vastly different games to teach various different kinds of knowledge and skills. This makes it difficult to compare and generalize studies, lacking any established formal tool of analysis. This problem requires the design of an abstract and objective measurement of the quantity of learning material independent of the learning domain. Based on cognitive research on causal Bayes nets (CBNs), this paper proposes using dynamic causal nets (DCNs) to model ...
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    How can one measure the learning outcome of playing a serious game? We need an objective measure of the learning contents included in a game. The research is diverse, utilizing vastly different games to teach various different kinds of knowledge and skills. This makes it difficult to compare and generalize studies, lacking any established formal tool of analysis. This problem requires the design of an abstract and objective measurement of the quantity of learning material independent of the learning domain. Based on cognitive research on causal Bayes nets (CBNs), this paper proposes using dynamic causal nets (DCNs) to model an abstract knowledge base, which could be mapped to many different domains of learning. We also apply Kolmogorov Complexity (KC) as an approach to measure the content of the abstract knowledge base. This work will establish a theoretical foundation for future research of serious games.
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    Conference Title
    2017 IEEE 5TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH (SEGAH)
    DOI
    https://doi.org/10.1109/SeGAH.2017.7939281
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Learning sciences
    Publication URI
    http://hdl.handle.net/10072/346313
    Collection
    • Conference outputs

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