Search arXiv⌕ Search

arXiv · 2503.12321

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

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kira Adaricheva, Jonathan T. Brockman, Gillian Z. Elston, Lawrence Hobbie, Skylar Homan, Mohamad Khalefa, Jiyun V. Kim, Rochelle K. Nelson, Sarah Samad, Oren Segal. 2025-03-16. A novel association and ranking approach identifies factors affecting educational outcomes of STEM majors. https://arxiv.org/abs/2503.12321

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation

LLM evaluations in applied domains tend to reflect models that were already outclassed at time of publication. We observe a publication elicitation gap: the distance between the AI systems generating the results reported in an academic paper and the AI systems that a current reader of that paper would reasonably assume are being referenced. We systematically sweep OpenAlex from 2022-01-01 to 2026-04-01 (n = 112,303 LLM keyword matches). Then, we identify what models were evaluated (n = 18,574 admissible records). We then rank each evaluated LLM against a frontier LLM based on the Epoch AI Capabilities Index (ECI), an aggregate LLM capability score. At time of evaluation, the median paper is evaluating models that are behind frontier LLMs in capability, with a median gap of +10.85 ECI (H1; n = 12,312). This gap is growing, increasing at a rate of +5.53 ECI per year (H2, nominal 95% CI [+5.03, +5.83]). The sign holds even in the absence of any imputation for evaluation date. In papers (n = 728) where the date of evaluation is explicit and the model in question can be resolved to an ECI score, the median gap for H1 is +5.01 ECI. An explicitly stated evaluation date can be found in only 18.4% of full-text papers. After correction, in 52.5% (95% CI: [48.2, 56.9]) of abstracts in our audit, conclusions are stated at the class level ("AI") rather than the model level. For papers about reasoning models, only 3.2% of abstracts and 21.2% of full-text articles disclose the reasoning mode status of the models used (H4). We propose a solution to this problem that is distributed among authors, editors, and funders. First, reporting from authors. VERSIO-AI v1.2 is a proposed 13-item checklist to cover the configuration surface described herein. Second, enforcement from journal editors and peer reviewers. Third, conditioning grants on disclosure and providing API access.

cs.CY↗

The Cross-Section of Stock Returns and AI Exposure

We study 380 trillion tokens of realized AI consumption across more than four hundred LLMs. We build a high-frequency AI factor and show that a long-short strategy based on firms' AI exposure earns significantly positive returns. The average strategy return is larger based on intensive, frontier-oriented AI consumption but smaller based on casual or open-weight usage. Internationally, the return spread is significant in developed countries but insignificant in emerging markets. Examining occupational AI exposure, we find more positive exposure in occupations intensive in nonroutine interactive tasks and more negative exposure in those intensive in nonroutine analytical tasks.

cs.CY↗

The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories

Visibility in media is pivotal for identity development and for broadening societal views of gender and sexuality. Queer representation has increased in recent years, yet damaging stereotypes and tropes persist. Here, we focus on queer portrayal and its perception by audiences in fictional stories (television, film, and literature) by studying characters by their quantified archetypes which are operationalizations of common conceptions such as Hero, Diva, and Outcast. We use the archetypometrics and Fandom's LGBTQIA+ datasets to study samples of fictional characters along the trait differential spanning straight to queer. We find, quantify, and explain a seeming paradox. The characters with the highest queer score present positive primary archetypes and are typically Heroes rather than Fools, Angels rather than Demons, and Adventurers rather than Traditionalists. But evaluation across many stories for the straight-queer trait itself reveals a strong collective-writing bias towards Fool (away from Hero) and no meaningful loading for the other two dimensions. Our analysis offers a population-scale view of the complexities of queer portrayal, while also pointing to risks in blindly training on many-authored story corpora.

cs.CY↗