Examining the Effects of Course Climate, Active Learning, and Intersectional Identities on Undergraduate Student Success in Computing

该项目旨在通过提高本科生在计算机科学课程中的成绩来服务于国家利益。为了做到这一点，它将研究学生成功与其计算机科学课程的课程氛围和教学实践之间的关系。它将进一步分析具有交叉身份的学生的这些关系，例如第一代大学生的女性。许多STEM课堂采用被动学习方法，有意无意地创造了抑制合作的竞争课程氛围。鼓励协作和主动学习的替代教学方法正在许多本科生计算机课程中获得吸引力。研究普遍表明，协作性主动学习与提高学生的学习效果之间存在正相关关系。然而，主动学习的实施情况各不相同，对学生学习的结果也一直不一致。这种不一致可能反映了竞争性的、被动的学习对具有特定交叉身份的学生群体的不同影响。本项目旨在探讨不同种族、性别和社会阶层的学生的主动学习和合作课程氛围之间的关系。这项研究产生的知识有可能提高学生在计算机科学方面的成功，从而扩大个人的参与，这些群体还没有在计算中得到公平的代表。这个项目确定了课程结构和社会性是课程氛围的两个主要组成部分。课程结构包括课程时间表、出勤率或参与度预期以及如何确定分数等功能。社会性包括形成支持性的社区并在其中工作。该项目使用课程结构和社会性的概念来捕捉教师在采用主动学习方法时选择和结合的教学组成部分。然后，将确定这些主动学习组成部分和学生结果之间的相关性，并将用于探索不同学生身份的不同结果。这些知识可能会帮助教师选择积极的学习策略，从而导致更有效的学生学习，或者将结构元素和社会性元素结合起来，在本科计算机教育中开发新的有前途的教学方法。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。这个项目是在参与学生学习的轨道上进行的，国际自然保护联盟项目通过这个轨道支持有前景的实践和工具的创建、探索和实施。  这个奖项反映了国家科学基金会的法定使命，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。

This project aims to serve the national interest by improving undergraduate student success in computer science courses. To do so, it will study relationships between student success and the course climate and teaching practices of their computer science courses. It will further analyze these relationships for students with intersecting identities, such as women who are first generation college students. Many STEM classrooms feature passive learning approaches and intentionally or unintentionally establish create competitive course climates that inhibit collaboration. Alternative teaching approaches that encourage collaboration and active learning are gaining traction in many undergraduate computing programs. Studies have generally indicated a positive relationship between collaborative active learning and increased student learning outcomes. However, implementation of active learning varies and the results on student learning have been inconsistent. It is possible that the inconsistencies may reflect differential impacts of competitive, passive learning on groups of students with specific intersecting identities. This project aims to explore the relationship between active learning and cooperative course climate on students with intersecting identities, including race, gender, and social class. The knowledge generated by this research has the potential to improve student success in computer science and thus broaden participation of individuals from groups that are not yet equitably represented in computing.This project identifies course structure and sociality as two major components of course climate. Course structure includes features such as the course timeline, expectations for attendance or participation, and how grades are determined. Sociality involves forming and working in supportive communities. The project uses the constructs of course structure and sociality to capture pedagogical components that faculty select and combine in their adoption active learning approaches. Correlations will then be identified between these active learning components and student outcomes, and will be used to explore differential outcomes across intersectional student identities. This knowledge may help instructors select active learning strategies that would lead to more effective student learning, or combine elements of structure and sociality to develop new promising pedagogies in undergraduate computing education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the IUSE program supports the creation, exploration, and implementation of promising practices and tools.   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.