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。要做到这一点，它将研究学生的成功和他们的计算机科学课程的课程气氛和教学实践之间的关系。 它将进一步分析这些关系的学生与交叉身份，如妇女谁是第一代大学生。 许多STEM教室采用被动学习方法，并有意或无意地建立竞争性课程氛围，抑制合作。 鼓励协作和主动学习的替代教学方法在许多本科计算课程中越来越受欢迎。研究普遍表明，积极合作的学习和提高学生的学习成果之间的积极关系。 然而，主动学习的实施情况各不相同，对学生学习的结果也不一致。这种不一致性可能反映了竞争性的被动学习对具有特定交叉身份的学生群体的不同影响。 本研究旨在探讨具有种族、性别、社会阶层等多重身份认同的学生，其主动学习与合作课程气氛之间的关系。这项研究所产生的知识有可能提高学生在计算机科学的成功，从而扩大个人的参与，从团体尚未公平代表在computing.This项目确定课程结构和社会性作为课程气候的两个主要组成部分。课程结构包括课程时间轴、出勤或参与的期望以及如何确定成绩等功能。 社会性包括在支持性社区中形成和工作。该项目使用的课程结构和社会性的结构，以捕捉教师选择和联合收割机在他们采用主动学习方法的教学成分。然后，这些积极的学习组件和学生的成绩之间的相关性将被确定，并将被用来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积极的学习策略，这将导致更有效的学生学习，或联合收割机的结构和社会性的元素，以开发新的有前途的教学在本科计算教育。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。该项目是在学生学习的轨道，通过该IUSE计划支持创建，探索和实施有前途的做法和工具。   该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

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.