Serious Games Analytics to Measure Implicit Science Learning
Serious Games Analytics to Measure Implicit Science Learning
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严肃游戏分析来衡量内隐科学学习
DOI：
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发表时间：
2015
期刊：
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通讯作者：
R. Baker
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文献类型：
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作者：
Elizabeth Rowe;J. Asbell;R. Baker
以证据为中心的游戏设计（ECgD）是一种越来越受欢迎的模型，用于隐形游戏评估，采用教育数据挖掘技术来测量严肃（和其他）游戏中的学习（GlassLab，基于游戏的评估中的心理测量考虑。游戏研究所。2014年7月1日检索自http：//www.instituteofplay.org/work/projects/glasslab-research/）。在ECgD中，在如何预先定义学习结果和措施之间存在着持续的紧张关系，以及在游戏中可以检测到多少重要但未预料到的学习。EdGE研究团队正在采用一种紧急方法来开发一种基于游戏的评估机制，该机制从玩家在精心制作的游戏中的行为开始，并检测可能表明对显着现象的隐式理解的模式。隐性知识是显性知识的基础（波兰尼，隐性维度。芝加哥大学出版社，芝加哥，IL，1966），但在教育中很大程度上被忽视，因为难以测量学习者尚未正式化的知识。本章介绍了我们的方法来衡量内隐科学学习的游戏，冲动，旨在促进牛顿力学的内隐理解使用相结合的视频分析，游戏日志分析，并与前后评估结果的比较。这项研究表明，它是可能的，可靠地检测策略，表现出隐含的理解基础物理使用数据挖掘技术对用户生成的数据。
Evidence Centered Game Design (ECgD) is an increasingly popular model used for stealth game assessments employing education data mining techniques for the measurement of learning within serious (and other) games (GlassLab, Psychometric considerations in game-based assessment. Institute of Play. Retrieved July 1, 2014, from http://www.instituteofplay.org/work/projects/glasslab-research/). There is a constant tension in ECgD between how pre-defined the learning outcomes and measures need to be, and how much important, but unanticipated, learning can be detected in gameplay. The EdGE research team is employing an emergent approach to developing a game-based assessment mechanic that starts empirically from what the players do in a well-crafted game and detects patterns that may indicate implicit understanding of salient phenomena. Implicit knowledge is foundational to explicit knowledge (Polanyi, The tacit dimension. University of Chicago Press, Chicago, IL,1966), yet is largely ignored in education because of the difficulty measuring knowledge that a learner has not yet formalized. This chapter describes our approach to measuring implicit science learning in the game, Impulse, designed to foster an implicit understanding of Newtonian mechanics using a combination of video analysis, game log analyses, and comparisons with pre-post assessment results. This research demonstrates that it is possible to reliably detect strategies that demonstrate an implicit understanding of fundamental physics using data mining techniques on user-generated data.