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

玩电脑游戏的学生会产生大量丰富、有趣、高度可变的数据，当游戏结束时，这些数据大多会消失在网络中。如果在课堂环境中，学生玩的游戏数据仍然可供他们分析呢？在麻省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个视频游戏分别嵌入在5-8年级和8-14年级广泛使用的两种数据分析学习环境TinkerPlots和Fathom中。游戏数据实时显示在图表中，允许在短时间内进行多次策略改进循环。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。课程将从关键课程出版社中嵌入到LessonLink中，以促进其与标准课程的整合。这个为期三年的项目扩展了学生对数据建模的理解，以及他们在数据丰富的环境中学习数学内容的能力。使用教学实验方法来调查四个研究问题：(1)学生在多大程度上将数据视为生产过程的结果，这个概念是否具有与重复测量上下文相同的启示，用于根据信号和噪声解释数据？(2)学生如何看待数据，特别是当他们遇到不适合行和列的数据时？哪些数据结构适合在中学中介绍？高中吗?(3)学生对数据的理解、解释和互动如何随着数据集规模的变化而变化？(4)项目为网络协作和数据共享建立的机制在多大程度上改善了课堂活动？数据包括视频、学生作业和学生访谈，收集自课后数据游戏俱乐部和初高中数学教室。数据分析使用接地理论的方法。

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.