Broadening Participation Research Project: Research for Social Justice - Broadening Participation through Data Science

北卡罗来纳州中央大学的研究人员提议研究将社会正义项目纳入数据科学课程以提高学生？数据科学的理解，并提高代表性不足的少数民族学生的STEM坚持。 假设STEM可以作为解决社会不平等的工具，这是目标人群感兴趣的话题。该项目还旨在产生一个由15个经过严格审查和开发的数据科学项目组成的项目库，这些项目将社会正义问题与代表性不足的学生的利益相结合。该项目将记录将项目纳入课程的过程，并将通过网站提供材料、辅导员指南和相关资源，以使研究成果能够复制和扩展。 研究人员将使用混合方法设计为基础的方法来建立教学创新的证据基础，并检查实施过程的有效性。情境教学和基于问题的学习框架的广泛框架将指导以下研究问题：（1）在多大程度上基于社会正义的数据科学项目的应用影响HBCU及其附属的早期大学高中的入学和保留结果？(2)基于社会正义的数据科学项目的应用如何影响已知的社会认知因素，以调解和缓和STEM入学，持久性和成功？以及（3）这种途径模型的关键特征是什么，将指导其他STEM项目在机构中的复制工作？ 基于社会正义项目的数据科学内容将通过现有的基于项目的课程提供，这是所有学生都需要的。逐次逼近模型或迭代设计-开发-测试过程将指导数据的收集和分析，以评估项目材料的可用性、高质量和认可程度。 该项目预计将提供一个有效的模型，以解决在STEM领域的代表性不足的经验证据。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

Researchers at North Carolina Central University propose to examine the impact of incorporating social justice projects in data science courses to improve students? understanding of data science and increase STEM persistence among underrepresented minority students. The assumption is that STEM can serve as a vehicle for addressing social inequities, a topic of interest to the targeted population. The project aims also to produce a pool of fifteen well-vetted and rigorously developed data science projects that infuse social justice issues aligned with the interests of underrepresented students. The project will document the process of integrating the projects into the curriculum and will make accessible through a website the materials, facilitator guides, and accompanying resources to enable replication and scalability of the research outcomes. The researchers will use a mixed-methods design-based approach to build the evidence base for the pedagogical innovation and examine the efficacy of the implementation process. The broad framework of Contextual Teaching and Learning and problem-based Learning frameworks will guide the following research questions: (1) To what extent does the application of data science projects grounded in social justice influence enrollment and retention outcomes at an HBCU and its affiliated Early College High School? (2) How does the application of data science projects grounded in social justice impact the socio-cognitive factors known to mediate and moderate STEM enrollment, persistence, and success? and (3) What are the key features of this pathway model that will guide replication efforts by other STEM programs at institutions? The content of data science grounded in social justice projects will be delivered through an existing project-based course that is required for all students. The Successive Approximation Model or iterative design-develop-test process will guide the collection and analysis of data to assess the degree to which the project materials are usable, of high quality, and endorsed. The project is expected to provide empirical evidence of an effective model to address underrepresentation in STEM fields.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.