Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed?
Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed?
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大规模、练习式的严肃数学游戏中的学习曲线分析：哪里需要学习支持？
DOI：
10.1007/978-3-319-93843-1_32
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发表时间：
2018
影响因子：
1.8
通讯作者：
Teomara Rutherford
中科院分区：
文献类型：
--
作者：
Zhongxiu Peddycord;Rachel Harred;Sarah M. Karamarkovich;T. Barnes;Collin Lynch;Teomara Rutherford
本文运用数据驱动的方法来理解学习，并从一款大规模的训练和练习游戏《时空数学》中获得游戏设计见解。为了让严肃游戏蓬勃发展，我们必须开发出有效且可扩展的方法去评估游戏的教育目标。近年来，学习模式已经成熟，并在电子学习平台上得到了应用，但在严肃游戏中还没有得到广泛应用。我们将经验学习曲线分析应用于《ST Math》，假设游戏中的知识组件是如何定义的，并映射到游戏内容中。我们获得了可操作的游戏设计反馈和关于分数学习的教育见解。我们的研究结果揭示了学生无法在数学技能、内容和问题表示之间转移知识的情况。这项工作强调了设计支持学生理解数学概念的游戏的重要性，而不是为了通过游戏而学习特定内容和情境的技能。
This paper applies data-driven methods to understand learning and derives game design insights in a large-scale, drill-and-practice game: Spatial Temporal (ST) Math. In order for serious games to thrive we must develop efficient, scalable methods to evaluate games against their educational goals. Learning models have matured in recent years and have been applied across e-learning platforms but they have not been used widely in serious games. We applied empirical learning curve analyses to ST Math under different assumptions of how knowledge components are defined in the game and map to game contents. We derived actionable game design feedback and educational insights regarding fraction learning. Our results revealed cases where students failed to transfer knowledge between math skills, content, and problem representations. This work stresses the importance of designing games that support students’ comprehension of math concepts, rather than the learning of content- and situation-specific skills to pass games.
影响因子：
2.6
作者：
Bailey DH;Hoard MK;Nugent L;Geary DC
通讯作者：
Geary DC