DIP: Data Science Games - Student Immersion in Data Science Using Games for Learning in the Common Online Data Analysis Platform

DIP：数据科学游戏 - 学生沉浸在数据科学中，在通用在线数据分析平台中使用游戏进行学习

• 批准号: 1530578
• 负责人: William Finzer
• 金额: $ 134.88万
• 依托单位: Concord Consortium
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1530578&HistoricalAwards=false
• 关键词: DIP; Data; Science; Games; Student

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. One important challenge is helping teach people how to use technology and statistics to understand numerical data through analysis and modeling. This project refines and studies technology for 'data science games': essentially, a game is embedded in a data analysis environment, in which the game can only be 'won' by doing data modeling. Research will examine how students learn to analyze and model data in high school biology, chemistry, and physics; how the game can support this learning; and how such games can fit into high school science classrooms (as tested in a large urban school district). This project uses design-based research methodology to understand how students engage with and learn about data science (specifically, concepts of center, spread, distribution, and inference) and to identify social, technological, and pedagogical supports to allow classroom use, including various types of data representations in the data games (flat, hierarchical, tree, digraph, and relational). Semi-clinical interviews and direct observation of students using the games in controlled settings will lead to broader workshop- and classroom-based observations of individuals and dyads using think-aloud protocols. This data will be analyzed both using grounded theory and using diSessa's knowledge analysis method and analysis of discourse using Hmelo-Silver et al.'s CORTDRA diagrams. In addition, teacher focus groups and classroom video will be used to help identify affordances related to classroom adoption. The design and development work will be driven by the empirical research, and will utilize Squire's game-based learning design principles, starting with the existing CODAP (Common Online Data Analysis Platform) software. Four design iterations will take place, each culminating in user testing, initially with a summer workshop of students, then with two rounds of classroom testing in six high school classrooms, and finally in an open adoption phase in which 50 teachers will be recruited for data collection (but more teachers may adopt the software and curricula).

网络学习和未来学习技术计划为支持展望学习技术的未来并推动我们了解人们如何在技术丰富的环境中学习的努力提供资金。开发和实施(DIP)项目建立在概念验证工作的基础上，这些工作展示了拟议的新型学习技术的可能性，团队建立和完善了拟议创新的最低可行实例，使他们能够了解未来应如何设计和使用此类技术，并使他们能够回答有关人们如何学习、如何促进或评估学习和/或如何为学习而设计的问题。一个重要的挑战是帮助人们通过分析和建模来教会人们如何使用技术和统计学来理解数字数据。本项目对《数据科学游戏》的技术进行了提炼和研究：从本质上讲，游戏是嵌入到数据分析环境中的，在这个环境中，游戏只有通过进行数据建模才能获得胜利。研究将考察学生如何在高中生物、化学和物理中学习分析和建模数据；游戏如何支持这种学习；以及此类游戏如何适合高中科学课堂(在大型城市学区进行测试)。本项目使用基于设计的研究方法来了解学生如何参与和学习数据科学(特别是中心、传播、分布和推理的概念)，并确定社会、技术和教学支持，以便于课堂使用，包括数据游戏中的各种数据表示(平面、层次、树、有向图和关系)。对在受控环境中使用游戏的学生进行半临床访谈和直接观察，将导致使用有声思考协议对个人和双胞胎进行更广泛的基于研讨会和课堂的观察。这些数据将使用扎根理论和迪塞萨的知识分析方法进行分析，并使用希梅洛-西尔弗等人的S CORTDRA图表进行语篇分析。此外，还将使用教师焦点小组和课堂视频来帮助确定与课堂采用相关的负担能力。设计和开发工作将由经验研究驱动，并将利用Squire基于游戏的学习设计原则，从现有的CODAP(通用在线数据分析平台)软件开始。将进行四次设计迭代，每次最终都是用户测试，首先是学生暑期讲习班，然后是在六个高中教室进行两轮课堂测试，最后是开放采用阶段，将招聘50名教师进行数据收集(但可能会有更多教师采用软件和课程)。