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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Aparicio Colino, AnaCorresponding AuthorSanchez-Avila, CarmenAuthor

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October 7, 2025
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Gender and Academic Indicators in First-Year Engineering Dropout: A Multi-Model Approach

Publicated to: IEEE Access. 13 155532-155546 - 2025-01-01 13(), DOI: 10.1109/access.2025.3605776

Authors:

Aparicio Colino, Ana; Arroyo-Barriguete, Jose Luis; Hernandez, Adolfo; Sanchez-Avila, Carmen
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Affiliations

Santalucia Chair Analyt Educ, Madrid 28015, Spain - Author
Univ Complutense Madrid, Dept Financial & Actuarial Econ & Stat, Madrid 28040, Spain - Author
Univ Politecn Madrid, Dept Appl Math ICTs, Madrid 28040, Spain - Author
Univ Pontificia Comillas, Dept Quantitat Methods, Madrid 28015, Spain - Author
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Abstract

Student attrition during the first academic year remains a critical issue in engineering education, with implications for equity and institutional effectiveness. This study explores early dropout patterns across five engineering degree programs using data from 3889 first-year students spanning seven academic cohorts at one public and one private university in Spain. A multi-method analytical strategy-comprising logistic regression, artificial neural networks, and propensity score matching-was used to examine how gender and academic performance relate to early attrition. Findings challenge prevailing assumptions in the literature by showing that gender is not a statistically significant factor in first-year dropout, a result consistent across methods and after adjusting for confounders. In contrast, academic variables-such as entrance exam scores, failure rates, and exam absenteeism-exhibited strong associations with attrition. Institutional context also shaped dropout patterns: academic failure played a particularly salient role in the private university, while disengagement, measured through no-show rates, was more relevant in the public institution. Notably, an exception emerged in the Mathematical Engineering program, where gender moderated the link between academic failure and dropout, pointing to potential interaction effects in specific curricular settings. These results underscore the importance of understanding the conditional and context-dependent nature of early dropout, supporting targeted interventions grounded in academic, rather than demographic, indicators.
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Keywords

Academic performanceAttritionDropoutEducation computingEngineeringEngineering educationFailure analysisFemaleFirst year engineering studentFirst year engineering studentsFirst-year engineeringGenderGender equalityIdentityImpactLearning systemsLogistic regressionMachine learningMachine-learningMajorsNeural networksNeural-networksNeuralsenNeuralsensPersistencePropensity scorePropensity score matchingSelf-efficacyStudent retentionStudentsUniversity dropout

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 18 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/91190/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 80
  • Downloads: 112
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 5 - Gender Equality, with a probability of 49% according to the mBERT algorithm developed by Aurora University.
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (APARICIO COLINO, ANA) and Last Author (SANCHEZ AVILA, MARIA DEL CARMEN).

the author responsible for correspondence tasks has been APARICIO COLINO, ANA.

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Awards linked to the item

This work was supported in part by the Santalucia Chair of Analytics for Education at the Universidad Pontificia Comillas, Spain. This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols wasgranted by the Ethics Committee of Universidad Pontificia Comillas under Application No. 53/23-24.
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