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“面向学生和教师的创新技术体验”（ITEST）项目的努力，通过开发、广泛实施和系统调查两个为期10天的计算机编程教学序列，更好地理解和促进提高学生在科学、技术、工程或数学（STEM）领域追求职业的动机和能力的实践。新的教学顺序将通过模拟中学核心科学教室的实习，将计算机科学和科学学习经验结合起来，旨在提高学生对计算机编程和计算思维的倾向和能力。最终，干预的目的是支持更广泛地参与计算机科学（CS）领域的学习和职业，特别强调女性。该项目旨在通过以下方式实现这一目标：(1)让多达4000名11-14岁的青少年（6-8年级）学生沉浸在模拟实习中，这些实习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，并鼓励更多的学生，特别是女性，确定自己可能成为程序员；(3)收集证据，以促进和深化该领域对学生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；4)确定可能吸引学生参与计算机科学的具体因素、设计和实践，提高学生对STEM和计算机科学相关职业的倾向，并可能提高教师和地区支持计算机科学教育的能力。该项目以以下方式扩展了之前的工作，旨在将编码和计算思维融入学校：明确关注协作话语和协作解决问题，包括在数字媒介的讨论论坛中；在模拟实习的数字环境中记录学生互动的后端数据，以分析学生在科学和计算机科学交叉的理解是如何发展的；教师通过教育课程及时学习，以支持系统能力和更广泛的影响；研究和开发模型明确地将学校、地区和州政策层面的利益相关者纳入设计过程，以便建立对干预措施以及类似干预措施如何成功和可持续实施的理解。该项目还得到了STEM+Computing program （STEM+C）的支持，旨在推进有关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，并提高学生对STEM和cs相关职业的倾向。该项目的混合方法研究议程将以四个问题为指导：1)CS实习的哪些具体设计特征和教学策略对扩大学生参与CS最重要？2)计算机科学实习的哪些方面对支持计算机科学与科学融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学生富有成效地参与STEM，以及教师促进STEM中的协作和话语（面对面和数字化）最重要？4)当学生能够操作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，该项目将试行和迭代发展两个编程科学实习中的第一个，其方法以基于设计的实施研究为基础。研究活动将包括对试点课程的观察和对教师的访谈。同样在一年级，该项目还将通过对试点教室学生的认知访谈，开始研究学生如何发展计算思维和计算机编程实践。在第二年，项目将开始对第一年的实习进行广泛的实施和系统的调查，并开始迭代开发第二个实习，将在第三年进行广泛的实施。与二年级和三年级广泛实施相关的研究活动将包括对学生（包括测量计算机科学倾向、计算机科学与科学实践的量表）和教师（包括测量计算机科学教学内容、知识和能力信念的量表）的岗前测量；学生的日常参与调查；并对教师进行日常干预可行性及感知价值调查。除了方差和协方差分析之外，该项目还将采用中介分析来检查导致任何观察到的学习收益的关键变量之间的相互作用。二年级和三年级还将广泛捕获通过与数字资源（例如，“点击流”和元数据、提交数据和数字论坛中的讨论）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）将旨在为学生的计算思维发展提供更微妙的洞察，因为它适用于科学，以及计算机科学和科学实践综合发展的可能学习轨迹。

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.