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

网络学习和未来学习技术计划为支持设想学习技术的未来并推进我们对人们如何在技术丰富的环境中学习的了解的努力提供资金。开发和实施（DIP）项目建立在概念验证工作的基础上，展示了拟议的新型学习技术的可能性，团队建立并完善了他们拟议创新的最低可行性示例，使他们能够理解未来应该如何设计和使用这种技术，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，and/or how to design设计for learning学习.一个重要的挑战是帮助教人们如何使用技术和统计，通过分析和建模来理解数值数据。这个项目提炼和研究了“数据科学游戏”的技术：本质上，游戏嵌入在数据分析环境中，只有通过数据建模才能“赢得”游戏。研究将研究学生如何学习分析和建模高中生物，化学和物理数据;游戏如何支持这种学习;以及这些游戏如何适应高中科学教室（在大型城市学区进行测试）。该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、技术和教学支持以允许课堂使用，包括各种类型的数据游戏中的数据表示（平面、分层、树、有向图和关系）。半临床访谈和直接观察学生使用游戏在控制设置将导致更广泛的车间和教室为基础的观察个人和二人组使用有声思维协议。这些数据将使用扎根理论和使用diSessa的知识分析方法和话语分析使用Hmelo-Silver等人进行分析。的皮层图。 此外，教师焦点小组和课堂视频将被用来帮助确定与课堂采用相关的启示。设计和开发工作将由实证研究驱动，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，每次迭代都以用户测试为高潮，最初是学生的夏季研讨会，然后是六所高中教室的两轮课堂测试，最后是开放采用阶段，其中将招募50名教师进行数据收集（但更多教师可能会采用软件和课程）。

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).