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教育的支持下：西班牙裔服务机构（HSI计划），该曲目2项目旨在提高计算机科学中当前的本科保留率，尤其是对于来自代表性不足/服务不足社区的学生。长期以来，这个系统的问题主要是由于其复杂的性质。学术保留率，尤其是在诸如计算机科学之类的苛刻的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。此外，学生的潜水员经验和需求使一定程度的解决方案无效。机构需要尽早确定高风险的学生，以提供针对学生需求的及时支持。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不断跟上这些变化至关重要。解决这些挑战需要一种创新，以数据为中心和以学生为中心的方法。该项目将使用预测建模来确定有准确挣扎的学生。通过尽早确定学生的潜在对抗性因素，该项目将评估迅速和个性化的基于证据的干预措施，以支持学生克服这些挑战。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的心态。这种全面的方法有望改善参与学生的学习成绩和福祉，从而提高保留率。由此产生的设计，实施和测量结果可以指导未来的干预措施以改善学生的保留率，从而增加STEM计划的多样性。这个拟议的项目具有三个特定的目标。首先，它旨在评估基于证据的干预措施对在数学，心理健康，健康和自我有效等关键领域挣扎的学生的影响。机器学习算法将根据学生报告的学术和人口统计数据确定有问题的一年级学生。干预措施将量身定制，以解决确定的风险因素，并将包括对照组进行比较。每种干预措施的有效性将使用定量调查，课程等级和定性访谈来衡量，并使用统计方法（例如ANOVA和回归模型）进行分析。其次，使用结合定量和定性分析的混合方法方法，该项目将基于对学术计划的参与反馈，发布兴趣以及对专业发展的担忧来完善并改善干预措施和预测模型。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，从而有助于开发未来应用的精致方法。所有软件工件和发现都将是开源的，并且可以在线获得。 PI将通过相关会议上的演讲，讲习班和出版物在校园内外展示其进度，并在校园内传播结果。预计该项目的成功将导致在其他学术计划和机构中扩大这些干预措施的扩大，从而改善了HSIS STEM领域的保留和学术成就。 HSI计划旨在增强本科STEM教育并在HSIS的建立能力。 HSI计划支持的项目还将为如何实现这些目标提供新的知识。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准来通过评估来诚实地获得支持。