Narrative categorization in digital game-based learning: Engagement, motivation & learning
Narrative categorization in digital game-based learning: Engagement, motivation & learning
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DOI：
10.1111/bjet.13004
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发表时间：
2020-07-28
影响因子：
6.6
通讯作者：
Wasson, Barbara
中科院分区：
文献类型：
--
作者：
Breien, Fredrik S.;Wasson, Barbara
先前的研究表明，基于数字游戏的学习（DGBL）可以对参与，动机和学习产生积极影响，并且使用叙事可以加强这些效果。一项系统性综述确定了15个DGBL系统，报告了使用叙述的影响。然而，该领域的一个差距是缺乏一个通用的模型来对DGBL中的叙事进行分类和隔离，以分析和比较叙事如何以及在什么条件下对DGBL系统中的学习产生影响。卢多叙事变量模型（LNVM），已被用来隔离和分类的商业视频游戏研究中的叙事是一个候选人，以填补这一空白。本研究探讨了这种模式的DGBL的潜力，并导致了一个扩展的LNVM（eLNVM），可用于隔离和分类的叙事DGBL。在eLNVM上对15个DGBL系统进行了分类，结果表明，DGBL系统具有将其与其他DGBL系统区分开来的积极自我报告效应的特征。此外，还可以确定叙事建模的特征，这些特征与参与度、动机和学习的积极影响相关。本文最后描述了如何eLNVM将在未来的研究中使用。
Previous research shows that digital game-based learning (DGBL) can have positive effects on engagement, motivation and learning, and that using narratives may reinforce these effects. A systematic review identified 15 DGBL systems that report effects from their use of narratives. A gap in the field, however, is the lack of a common model to categorize and isolate narratives in DGBL to enable an analysis and comparison of how, and under what conditions, narratives have effects on learning in DGBL systems. The ludo narrative variable model (LNVM) that has been used to isolate and categorize narratives in research on commercial video games is a candidate to fill this gap. This research has investigated the potential of this model for DGBL and resulted in an extended LNVM (eLNVM) that can be used to isolate and categorize narratives in DGBL. The 15 DGBL systems were categorized on the eLNVM and the results show that there are characteristics of DGBL systems with positive self-reported effects that separate them from other DGBL systems. Furthermore, it was possible to identify characteristics of the narrative modeling that are associated with positive effects on engagement, motivation and learning. The paper concludes with a description of how the eLNVM will be used in future research.