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功来服务于国家利益。为此，它将研究学生的成功与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 课堂都采用被动学习方法，并有意无意地营造了抑制协作的竞争性课程氛围。 鼓励协作和主动学习的替代教学方法在许多本科计算机课程中越来越受欢迎。研究普遍表明协作主动学习与提高学生学习成果之间存在积极关系。 然而，主动学习的实施情况各不相同，学生的学习效果也不一致。这种不一致可能反映了竞争性、被动学习对具有特定交叉身份的学生群体的不同影响。 该项目旨在探讨具有交叉身份（包括种族、性别和社会阶层）的学生的主动学习与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，从而扩大那些在计算机领域尚未得到公平代表的群体中的个人的参与。该项目将课程结构和社交性确定为课程氛围的两个主要组成部分。课程结构包括课程时间表、出勤或参与期望以及如何确定成绩等功能。 社交性涉及在支持性社区中形成和工作。该项目利用课程结构和社会性的结构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成分与学生成果之间的相关性，并将用于探索跨交叉学生身份的差异结果。这些知识可以帮助教师选择主动学习策略，从而使学生学习更有效，或者结合结构和社会性元素，在本科计算机教育中开发新的有前途的教学法。 NSF IUSE：EHR 计划支持研究和开发项目，以提高所有学生 STEM 教育的有效性。该项目属于参与学生学习轨道，IUSE 计划通过该项目支持有前途的实践和工具的创建、探索和实施。   该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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.