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This progress report documents outcomes from the ECS Scholars program, an NSF S-STEM project at Baylor University supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need in engineering, computer science, and related fields. Over five years the program funded four-year scholarships to two cohorts of eleven students pursuing B.S. degrees, supported by orientation, seminars, cohort-building, peer and alumni mentoring, undergraduate research, and professional development. A distinguishing feature of the project was its use of EAB's Navigate platform for tracking student progress, coordinating support, and applying predictive analytics. Through year four, twenty-two of thirty-one supported students remained on track to graduate with a strong average GPA, with all active scholars placed in internship experiences. The report focuses on the project team's practical experience with Navigate, finding that the platform's predictive analytics offered limited timely value—hampered by data-entry friction and lag, particularly for early first-year attrition—while a hybrid approach combining faculty-generated progress reports with historical grade data proved more effective. Drawing on qualitative interviews framed around engineering identity, the paper closes by proposing the integration of large language models with Navigate to streamline data collection and surface longitudinal patterns in student engagement.
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S-STEM First Year Progress: Baylor Engineering and Computer Science Scholar's Program
This paper reports first-year progress of the Engineering and Computer Science (ECS) Scholars Program at Baylor University, an NSF S-STEM project supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need pursuing B.S. degrees in Engineering, Electrical and Computer Engineering, Mechanical Engineering, and Computer Science. Over five years the project funds four-year scholarships for 22 students across two cohorts of 11, paired with orientation, a seminar series, required faculty mentoring, peer and alumni mentoring, undergraduate research support, professional development, and tutoring. A distinguishing feature is the use of EAB's Navigate platform for tracking student progress, coordinating care, and applying predictive analytics to inform timely interventions. The project also investigates how student perceptions of success factors align with those attributed by faculty/mentors and predictive analytics, and whether such "big data" tools are perceived as helpful. First-year accomplishments are reported across recruitment (adapted for COVID-19), Navigate setup, scholar orientation and activities, and a qualitative research study examining student fit, institutional fit, and student identity, with progress mapped to each project objective.
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- Award ID(s):
- 1930502
- PAR ID:
- 10399131
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Subject(s) / Keyword(s):
- S-STEM STEM education student retention low-income students high-achieving students engineering education computer science education predictive analytics EAB Navigate faculty mentoring undergraduate research student success COVID-19 scholarships
- Format(s):
- Medium: X
- Location:
- Minneapolis, MN
- Sponsoring Org:
- National Science Foundation
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This paper presents updated findings on the NSF S-STEM-funded ECS Scholars Program, which supports high-achieving, low-income students in Engineering and Computer Science. The program provides scholarships, faculty mentoring, research and internship opportunities, professional development, and social support, all aimed at promoting academic success and STEM identity formation. Over three years, 31 students participated; nine left the program—six due to academic performance, two to pursue non-STEM majors, and one for another scholarship. Most departures occurred early, but retention improved significantly for those who continued beyond their first year. Currently, 19 of 22 students are on track for on-time graduation, with all expected to graduate within 4.5 years. In the first cohort, 10 of 11 students graduated within four years, all securing professional placement. A critical part of our research involved EAB's Navigate platform, initially adopted for its predictive analytics to guide early interventions. Although the predictive analytics feature did not deliver the early, actionable data we anticipated, it provided insights for refining our approach. We shifted focus to tracking student engagement, which proved valuable for developing a strong STEM identity. A key element in developing a data-driven approach for student success is creating methods that are easy for faculty and staff to use. Last year, we submitted an ASEE paper that proposed using generative AI to reduce challenges in data collection and analysis. This paper reports on what we feel is a significant contribution by describing a "proof of concept" system that leverages generative AI to track course-level attendance and student engagement providing instructors with summarized insights via email. The system is designed to be easily implemented for instructors, offering a practical tool for monitoring student engagement. This approach illustrates how generative AI enables us to explore solutions that were previously challenging due to the complexities of coding and data management. By simplifying data collection, generative AI has reduced technical barriers, allowing us to focus on practical tools that support student success. As the ECS Scholars Program concludes, we have effectively supported the academic and professional growth of our students. Emphasizing the development of engineering and computer science identity has been a key finding that will guide our future efforts to sustain the program. The combination of Navigate and generative AI shows promise in tracking student engagement and streamlining data collection, offering insights for future initiatives aimed at supporting underrepresented students in STEM.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.18260/1-2--42065
