Computer Science Connections: Using Data Science to Broaden Participation in Middle School

计算机科学联系：利用数据科学扩大中学的参与

• 批准号: 2122485
• 负责人: Yvonne Kao
• 金额: $ 30万
• 依托单位: WestEd
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122485&HistoricalAwards=false
• 关键词: Computer; Science; Connections; Using; Data

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project will help middle school computer science (CS) students connect more deeply with CS content and promote inclusion and equitable participation in CS classrooms through data science. Data science is a new field that applies computer science and statistics to data sets with the goal of extracting useful information. Data science skills have become necessary to make sense of complex social and scientific problems. Data science has mainly been taught in graduate and undergraduate programs, but more and more groups are aiming to teach data science in elementary, middle, and high school. Data science instruction can give computer science students the opportunity to solve authentic and socially relevant problems, increasing interest and motivation, particularly for learners who are members of historically underrepresented groups in science, technology, engineering, and mathematics (STEM). Authentic and socially relevant problems also provide opportunities for students to engage in classroom discourse, a strategy that has demonstrated benefits for English learners in mathematics classes.Computer Science Connections is a researcher-practitioner partnership (RPP) between WestEd and the Oakland Unified School District (OUSD). The partnership will use a design research approach to modify the data science content within OUSD’s middle school computer science curriculum to include more socially relevant problems and to incorporate classroom discourse strategies. The OUSD team will design the new data science unit and establish a professional learning community (PLC) to prepare teachers to teach it. Using a combination of focus groups, surveys, and observations, the WestEd team will conduct research on teachers’ and students’ perceptions of data science and how it is currently taught in OUSD middle schools. The team will also evaluate the effectiveness of the new data science unit in OUSD CS classrooms and how well the data science PLC prepares teachers to teach data science. The success of the partnership will be evaluated by an external advisory panel with expertise in computer science education, math education, data science education, and teacher professional development. The RPP will provide information to the field on the successes and challenges of engaging in this work and a program that is evidence-based through rigorous efficacy research. This project is funded by the CS for All: Research and RPPs program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。该项目将帮助中学计算机科学(CS)学生更深入地联系CS内容，并通过数据科学促进CS课堂的包容性和公平参与。数据科学是将计算机科学和统计学应用于数据集，目的是提取有用信息的一个新领域。数据科学技能已经成为理解复杂的社会和科学问题的必要技能。数据科学主要在研究生和本科课程中教授，但越来越多的团体正致力于在小学、初中和高中教授数据科学。数据科学教学可以为计算机科学专业的学生提供解决真实的和与社会相关的问题的机会，提高他们的兴趣和动机，特别是对于那些在科学、技术、工程和数学(STEM)领域属于历史上代表性不足的群体的学习者。真实的和社会相关的问题也为学生提供了参与课堂话语的机会，这一策略已经证明对数学课上的英语学习者有好处。计算机科学连接是WestEd和奥克兰联合学区(OU.S.)之间的研究人员-实践者合作伙伴关系(RPP)。这一合作伙伴关系将使用设计研究方法修改俄勒冈州联合大学的中学计算机科学课程中的数据科学内容，以纳入更多与社会相关的问题，并纳入课堂话语策略。OU.S.团队将设计新的数据科学单元，并建立一个专业学习社区(PLC)，为教师传授数据科学做好准备。利用焦点小组、调查和观察相结合的方式，WestEd团队将对教师和学生对数据科学的看法以及目前在俄亥俄州立大学中学教授数据科学的方式进行研究。该团队还将评估OUDCS教室中新的数据科学单元的有效性，以及数据科学PLC为教师讲授数据科学做好准备的情况。合作伙伴关系的成功将由一个拥有计算机科学教育、数学教育、数据科学教育和教师专业发展专业知识的外部咨询小组进行评估。RPP将向外地提供有关从事这项工作的成功和挑战的信息，并通过严格的疗效研究提供一个以证据为基础的计划。该项目由CS for All：Research and RPPS计划资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Using Student and Teacher Feedback to Modify CS Curriculum

利用学生和教师的反馈来修改计算机科学课程

• DOI: 10.1145/3545947.3576364
• 发表时间: 2023
• 期刊: SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education
• 作者: Echeverria, Fernando;Kao, Yvonne;Hubbard Cheuoua, Aleata
• 通讯作者: Hubbard Cheuoua, Aleata