Coding Science Internships: Authentic Learning Experiences to Support Students' Science and Programming Practices and Broaden Participation in Computer Science

该项目将推进学生和教师创新技术体验（ITEST）计划的努力，以更好地理解和促进实践，通过开发，广泛实施和系统地调查两个为期10天的计算机编程教学序列，提高学生在科学，技术，工程或数学（STEM）领域追求职业生涯的动机和能力。 新的教学序列将通过核心中学科学教室的模拟实习整合计算机科学和科学学习经验，旨在提高学生对计算机编程和计算思维的态度和能力。最终，干预措施旨在支持更广泛地参与计算机科学（CS）领域的研究和职业，特别强调女性。该项目旨在通过以下方式实现这一目标：（1）使多达4000名11-14岁的青少年参与其中（6-8年级）模拟实习的学生反映了实践科学家的协作和计算工作，并且可以嵌入学校的核心科学课程;（2）提供一个更具包容性的计算机科学工作模式，可以扩大学生对计算机编程的性质和价值的认识，并鼓励更广泛的学生，特别是女性，（3）收集证据，可以促进和加深该领域对学生的计算机科学知识和实践如何在科学学习经验的背景下发展的理解;以及4）确定可能使学生参与CS的具体因素，设计和实践，改善学生对STEM和CS相关职业的倾向，这可能会提高教师和地区支持CS教育的能力。该项目扩展了以前的工作，旨在将编码和计算思维纳入学校在以下方面：明确关注协作话语和协作解决问题，包括在数字介导的讨论论坛;后端数据记录模拟实习的数字环境中的学生互动，以分析学生的理解如何在科学和计算机科学的交叉点发展;通过教育课程及时进行教师学习，以支持系统能力和更广泛的影响;以及明确将学校、地区和国家政策层面的利益攸关方纳入设计过程的研究和开发模式，以便了解如何成功和可持续地实施干预措施和类似措施。该项目还得到了STEM+计算项目（STEM+C）的支持，以推进关于学生的计算机科学知识和实践如何在科学学习经验的背景下发展的研究，并改善学生对STEM和CS相关职业的倾向。该项目的混合方法研究议程将由四个问题指导：1）CS实习的具体设计特点和教学策略对扩大学生参与CS最重要？2)CS实习的哪些方面对支持CS和科学整合的可持续性最重要？3)哪些因素、设计特征和实践对于支持STEM中富有成效的学生参与和教师促进协作和话语（包括面对面和数字化）最重要？4）当学生能够操纵科学模型背后的代码时，学生理解的哪些方面可能会被揭示？在第一年，该项目将试点和迭代开发两个编码科学实习中的第一个，其方法论以基于设计的实施研究为基础。研究活动将包括观察试点课程和教师访谈。同样在第一年，该项目也将开始检查学生如何发展计算思维和计算机编程实践，通过与学生在试点教室认知访谈。在第二年，该项目将开始广泛实施和第一年开发的实习系统的调查，并开始迭代开发第二实习，这将是广泛实施的第三年。与广泛实施有关的研究活动在2 - 3年级将包括学生（包括测量CS倾向的量表，以及CS和科学实践）和教师（包括测量CS教学的科学和技术教学内容知识和能力信念的量表）的事前措施;学生的日常参与调查;和教师的日常干预可行性和感知价值调查。除了方差和协方差分析外，该项目还将采用中介分析来检查有助于任何观察到的学习成果的关键变量之间的相互作用。第2 - 3年还将广泛收集通过与数字资源（例如，“点击流”和元数据，提交数据和数字讨论论坛内的话语）的互动产生的学生数据。应用于这些海量数据集的学习分析方法（包括机器学习、贝叶斯网络建模和潜在狄利克雷分配）旨在为学生应用于科学的计算思维发展以及可能的学习轨迹提供更微妙的见解。计算机科学和科学实践的综合发展。

This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by developing, broadly implementing, and systematically investigating two 10-day computer programming instructional sequences. The new instructional sequences will integrate computer science and science learning experiences through simulated internships for core middle school science classrooms, and are designed to increase student dispositions toward, and capacity for, computer programming and computational thinking. Ultimately, the intervention is designed to support broader participation in computer science (CS) fields of study and careers, with particular emphasis on females. This project seeks to accomplish this goal by: (1) immersing up to 4000 11-14 year old youth (Grades 6-8) students in simulated internships that mirror the collaborative and computational work of practicing scientists, and that can be embedded within a school's core science curriculum; (2) offering a more inclusive model of computer science work that can expand students' perception of the nature and value of computer programming and encourage a broader range of students, and females in particular, to identify as possible programmers; (3) gathering evidence that can advance and deepen the field's understanding of how students' computer science knowledge and practices develop within the context of science learning experiences; and 4) identifying specific factors, designs, and practices likely to engage students in CS, improve student dispositions toward STEM and CS-related occupations, and that are likely to improve the capacity of teachers and districts to support CS education. The project extends prior work aimed at incorporating coding and computational thinking into the school in the following ways: an explicit focus on collaborative discourse and collaborative problem solving, including that within digitally-mediated discussion forums; backend data logging of student interactions within the simulated internships' digital environments in order to analyze how student understanding develops at the intersection of science and computer science; just-in-time teacher learning via an educative curriculum to support system capacity and broader impact; and a research and development model that explicitly incorporates school, district, and state policy level stakeholders in the design process, in order to build an understanding of how the intervention, and those like it, can be successfully and sustainably implemented. The project is also supported by the STEM+Computing program (STEM+C) to advance research on how students' computer science knowledge and practices develop within the context of science learning experiences and improve student dispositions toward STEM and CS-related occupations.The mixed-methods research agenda for this project will be guided by four questions: 1) What specific design features and instructional strategies of the CS Internships are most important for broadening student participation in CS?; 2) What aspects of the CS Internships are most important to support sustainability of CS and science integration?; 3) What factors, design features, and practices are most important for supporting productive student engagement in, and teacher facilitation of, collaboration and discourse (both in-person and digital) in STEM?; and 4) What aspects of student understanding may be revealed when students are able to manipulate the code behind scientific models? In Year 1, the project will pilot and iteratively develop the first of two Coding Science Internships, with methodology grounded in design-based implementation research. Research activities will include observations of piloted lessons and teacher interviews. Also in Year 1, the project will also begin to examine how students develop computational thinking and computer programming practices, through cognitive interviews with students in pilot classrooms. In Year 2, the project will begin broad implementation and systematic investigation of the internship developed in Year 1, and begin iteratively developing the second internship, which will be broadly implemented in Year 3. Research activities related to broad implementation in Years 2 & 3 will include pre-post measures for students (including scales measuring disposition toward CS, and CS and science practices), and teachers (including scales measuring science and technological pedagogical content knowledge and competency beliefs for CS instruction); daily engagement surveys for students; and daily intervention feasibility and perceived value surveys for teachers. In addition to analyses of variance and covariance, the project will employ mediation analysis to examine interactions among key variables contributing to any observed learning gains. Years 2 & 3 will also feature extensive capture of student data generated through interactions with the digital resources (e.g, "clickstream" and metadata, submission data and discourse within the digital discussion forums). Learning analytics methods (including machine learning, Bayesian network modeling, and Latent Dirichlet Allocation) applied to these massive data sets will be aimed at providing more subtle insight into student development of computational thinking as it applies to science, and possible learning trajectories for the integrated development of computer science and science practices.