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，这些数据大多会蒸发到乙醚中。如果在课堂环境中，学生玩游戏的数据仍可供他们访问以进行分析，情况会怎样？在马萨诸塞州阿默斯特大学的数据游戏项目和KCP Technologies正在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。学生们玩一款短视频游戏，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款视频游戏分别嵌入了TinkerPlot和Fathom这两个广泛应用于5-8年级和8-14年级的数据分析学习环境中。游戏数据实时以图形形式出现，允许在短时间内进行几个周期的策略改进。游戏的设计使这些循环提高了对特定数据建模和/或数学概念的理解。课程将嵌入关键课程出版社的LessonLink中，以促进它们融入标准课程。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丰富数据环境中的数学内容的能力的研究。教学实验方法被用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，这个概念是否具有从信号和噪声的角度解释数据的重复测量环境的同样的负担？(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引入？高中？(3)学生对数据的理解、解释和互动如何随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、学生作业和学生访谈，收集自课后数据游戏俱乐部和初中和高中数学教室。使用扎根的理论方法对数据进行分析。

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.