Collaborative Research: Data Games--Tools and Materials for Learning Data Modeling

协作研究：数据游戏——学习数据建模的工具和材料

• 批准号: 0918735
• 负责人: William Finzer
• 金额: $ 135.91万
• 依托单位: KCP Technologies
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0918735&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Games; Tools

Students playing computer games generate large quantities of rich, interesting, highly variable data that mostly evaporates into the ether when the game ends. What if in a classroom setting, data from games students played remained accessible to them for analysis? In software and curriculum materials being developed by the Data Games project at UMass Amherst and KCP Technologies, data generated by students playing computer games form the raw material for mathematics classroom activities. Students play a short video game, analyze the game data, conjecture improved strategies, and test their strategies in another round of the game. The twenty video games are embedded in TinkerPlots and Fathom, two data analysis learning environments widely used in grades 5-8 and 8-14 respectively. The game data appear in graphs in real time, allowing several cycles of strategy improvement in a short time. The games are designed so that these cycles improve understanding of specific data modeling and/or mathematics concepts. Lessons will be embedded in LessonLink from Key Curriculum Press to facilitate their integration into standard curricula. The three-year project expands research in students' understanding of data modeling and their ability to learn mathematical content embedded in data-rich contexts. A teaching experiment methodology is used to investigate four research questions: (1) To what extent do students view the data as the result of a production process and does this conception have the same sort of affordances as repeated-measures contexts for interpreting data in terms of signals and noise? (2) How do students view data, especially when they encounter data that do not fit into rows and columns? What data structures are appropriate to introduce in middle school? High school? (3) How do students' understandings, interpretations, and interactions with data change as a function of size of the data set? (4) To what extent do the mechanisms the project builds for web-enabled collaboration and data sharing enhance classroom activities? Data, including video, student work, and student interviews, are collected from after-school Data Game clubs and middle and high school mathematics classrooms. Data are analyzed using a grounded theory approach.

玩电脑游戏的学生产生了大量丰富、有趣、高度可变的数据，这些数据在游戏结束时大多蒸发到以太中。如果在课堂环境中，学生玩游戏的数据仍然可以被他们用于分析，那会怎么样？在马萨诸塞大学阿默斯特分校和KCP技术公司的数据游戏项目开发的软件和课程材料中，学生玩电脑游戏产生的数据构成了数学课堂活动的原材料。学生们玩一个简短的视频游戏，分析游戏数据，推测改进的策略，并在另一轮游戏中测试他们的策略。这20款视频游戏嵌入到TinkerPlots和Fathom中，这两个数据分析学习环境分别广泛用于5-8年级和8-14年级。游戏数据以真实的时间出现在图表中，允许在短时间内进行几个周期的策略改进。游戏的设计是为了让这些循环提高对特定数据建模和/或数学概念的理解。课程将被嵌入在关键课程出版社的LessonLink中，以促进其融入标准课程。这个为期三年的项目扩展了学生对数据建模的理解以及他们学习嵌入在丰富数据背景中的数学内容的能力。采用教学实验方法探讨了四个研究问题：（1）学生在多大程度上将数据视为生产过程的结果，以及这种概念是否与解释数据的重复测量上下文具有相同的启示信号和噪音？(2)学生如何查看数据，特别是当他们遇到不适合行和列的数据时？什么样的数据结构适合在中学引入？高中？(3)学生的理解、解释和与数据的交互如何随着数据集的大小而变化？(4)该项目为网络协作和数据共享建立的机制在多大程度上增强了课堂活动？数据，包括视频，学生的工作，和学生的采访，收集从课后的数据游戏俱乐部和初中和高中数学教室。数据分析采用扎根理论的方法。