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

在改进的本科生STEM教育：拉美裔服务机构(HSI计划)的支持下，该Track 2项目旨在提高目前计算机科学专业的本科生保留率，特别是来自代表性不足/服务不足社区的学生。这一系统性问题长期以来一直盛行，主要是因为它的复杂性。学术留存，特别是在要求苛刻的STEM领域，如计算机科学，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。此外，学生的不同经验和需求使得一刀切的解决方案无效。院校需要及早发现有风险的学生，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，必须不断调整留住策略，以跟上这些变化。应对这些挑战需要一种创新的、数据驱动的、以学生为中心的方法。这个项目将使用预测性建模来识别有学习困难风险的学生。通过及早识别学生潜在的敌对因素，该项目将评估及时和个性化的循证干预措施，以支持学生克服这些挑战。建议的干预措施包括小组数学辅导、健康研讨会以及同行和教职员工指导。这一综合方法预计将改善参与的学生的学习成绩和幸福感，从而提高保留率。由此产生的设计、实施和衡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。首先，它旨在评估循证干预对那些在数学、心理健康、健康和自我效能等关键领域存在挣扎风险的学生的影响。机器学习算法将根据学生报告的学术和人口统计数据来识别面临缓刑风险的一年级学生。干预措施将针对已确定的风险因素进行量身定做，并将包括一组对照以供比较。每项干预措施的有效性将通过定量调查、课程评分和定性访谈进行衡量，并使用方差分析和回归模型等统计方法进行分析。其次，使用定量和定性分析相结合的混合方法，该项目将根据参与者对学术计划、职业兴趣和对职业发展的关注的反馈，精炼和改进干预和预测模型。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，有助于为未来的应用制定完善的方法。所有软件产品和发现都将是开源的，并可在网上获得。PIS将通过在相关会议上的演讲、研讨会和出版物，在校园内外介绍他们的进展和传播结果。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的广泛采用，从而提高HSIS在STEM领域的留住和学术成功。HSI计划旨在加强本科生STEM教育和HSI的能力建设。HSI计划支持的项目也将产生关于如何实现这些目标的新知识。该奖项反映了NSF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。

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.