HSI Implementation and Evaluation Project: Increasing Computer Science Undergraduate Retention through Predictive Modeling and Early, Personalized Academic Interventions

HSI 实施和评估项目：通过预测建模和早期个性化学术干预提高计算机科学本科生的保留率

• 批准号: 2345378
• 负责人: Sergio Gago Masague
• 金额: $ 50万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345378&HistoricalAwards=false
• 关键词: HSI; Implementation; Evaluation; Project; Increasing

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to increase the current undergraduate retention rates in Computer Science, especially for students from underrepresented/underserved communities. This systematic problem has long prevailed primarily due to its complex nature. Academic retention, especially in demanding STEM fields like Computer Science, is influenced by many factors, including academic preparedness, socioeconomic backgrounds, mental health, and campus support systems. Additionally, students’ diverse experiences and needs make a one-size-fits-all solution ineffective. Institutions need to identify at-risk students early to provide prompt support tailored to students’ needs. In addition, as technology and educational methods continue to develop, it is essential to adjust retention strategies to keep up with these changes constantly. Addressing these challenges requires an innovative, data-driven, and student-centric approach. This project will use predictive modeling to identify students at risk of struggling academically. By identifying students’ potential adversarial factors early, the project will evaluate prompt and personalized evidence-based interventions to support students in overcoming these challenges. The proposed interventions include small group math tutoring, wellness workshops, and peer and faculty mentorship. This comprehensive approach is expected to improve academic performance and well-being among participating students thereby increasing retention rates. The resulting design, implementation, and measured outcomes can guide future interventions to improve student retention, thus increasing diversity in STEM programs.This proposed project has three specific aims. Firstly, it aims to assess the impact of evidence-based interventions on students who are at risk of struggling in key areas such as math, mental health, wellness, and self-efficacy. Machine learning algorithms will identify first-year students at risk of probation based on student-reported academic and demographic data. Interventions will be tailored to address identified risk factors, and a control group will be included for comparison purposes. The effectiveness of each intervention will be measured using quantitative surveys, course grades, and qualitative interviews and analyzed using statistical methods such as ANOVA and regression models. Secondly, using a mixed-method approach that combines quantitative and qualitative analyses, this project will refine and improve interventions and predictive models based on participant feedback on the academic program, vocational interests, and concerns about professional development. Finally, the project will evaluate these interventions' sustainability and broader impact, contributing to developing refined methods for future application. All software artifacts and findings will be open-source and available online. The PIs will present their progress and disseminate results on and off campus through presentations, workshops, and publications at relevant conferences. The success of this project is expected to lead to an expanded adoption of these interventions across other academic programs and institutions, thus improving retention and academic success in STEM fields at HSIs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.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.

在“改善本科 STEM 教育：西班牙裔服务机构”（HSI 计划）的支持下，该轨道 2 项目旨在提高当前计算机科学专业的本科生保留率，特别是对于来自代表性不足/服务不足社区的学生。这一系统性问题长期以来一直普遍存在。由于其复杂性，学业保留率，尤其是在计算机科学等要求较高的 STEM 领域，受到许多因素的影响，包括学术准备、社会经济背景、心理健康和校园支持系统。一刀切的解决方案无效。机构需要及早识别有风险的学生，以便根据学生的需求提供及时的支持。此外，随着技术和教育方法的不断发展，必须调整保留策略以保持学生的安全。应对这些挑战需要采用创新的、数据驱动的、以学生为中心的方法，该项目将使用预测模型来识别面临学业困难的学生。评估及时和个性化的循证干预措施，以支持拟议的干预措施包括小组数学辅导、健康研讨会以及同伴和教师指导，预计这种综合方法将提高参与学生的学习成绩和福祉，从而提高设计和实施的保留率。 ，并且测量的结果可以指导未来的干预措施，以提高学生的保留率，从而增加 STEM 项目的多样性。该拟议项目有三个具体目标，首先，它旨在评估基于证据的干预措施对面临困境的学生的影响。数学、心理健康、保健等关键领域机器学习算法将根据学生报告的学业和人口统计数据来识别有缓刑风险的一年级学生，并针对已识别的风险因素进行定制干预措施，并纳入对照组以进行比较。每个干预措施的效果将通过调查、课程成绩、定性访谈以及使用方差分析和回归模型等统计方法进行分析来衡量。 其次，该项目将使用定量和定性分析相结合的定量混合方法来细化和改进干预措施。以及基于参与者对学术反馈的预测模型最后，该项目将评估这些干预措施的可持续性和更广泛的影响，有助于开发未来应用的改进方法。将通过相关会议上的演讲、研讨会和出版物展示他们的进展并在校内外传播结果。该项目的成功预计将导致这些干预措施在其他学术项目和机构中得到广泛采用，从而提高保留率和学术水平。在 STEM 领域取得成功HSI 计划旨在加强本科生 STEM 教育和能力建设，HSI 计划支持的项目也将产生有关如何实现这些目标的新知识。通过评估，该奖项被认为值得支持。基金会的智力价值和更广泛的影响审查标准。