University Student Dropout: A Longitudinal Dataset of Demographic, Socioeconomic, and Academic Indicators

[EN] This dataset contains detailed information on student trajectories and dropout factors at a Spanish technological university offering Science, Technology, Engineering, Arts, and Mathematics programs. The data comprise demographic, socioeconomic, and academic variables for all enrolled students,...

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Detalles Bibliográficos
Autores: Igualde-Sáez, Arnau|||0009-0009-0824-6957, García Sabater, José Pedro|||0000-0002-9097-2852, Marin-Garcia, Juan A.|||0000-0001-5416-3938, Puche-García, Sergio, Turró Ribalta, Carlos|||0000-0002-3840-9405, Despujol Zabala, Ignacio|||0000-0003-2776-1638, Alonso Díaz, Marina|||0000-0001-7079-5898, Benlloch-Dualde, José-V.|||0000-0002-4885-2806, Soriano Jiménez, Pedro Pablo, Maheut, Julien|||0000-0002-5796-9053
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/231710
Acceso en línea:https://riunet.upv.es/handle/10251/231710
Access Level:acceso abierto
Palabra clave:Student dropout
Higher education
Dataset
Learning management system
Descripción
Sumario:[EN] This dataset contains detailed information on student trajectories and dropout factors at a Spanish technological university offering Science, Technology, Engineering, Arts, and Mathematics programs. The data comprise demographic, socioeconomic, and academic variables for all enrolled students, including those in bachelor¿s, master¿s, doctoral, and lifelong learning programs, across three complete academic years, excluding periods affected by the SARS-CoV-2 pandemic. The data were collected and standardized from disjointed internal data sources, and fully anonymized. The dataset contains information about 39,364 students, 4989 courses in 163 degrees, and 77 variables related to admission pathways, academic performance indicators, socio-demographic background, digital activity in the Learning Management System, and Wi-Fi access records. Each of the 464,739 records corresponds to a course enrolment per student per year, enabling longitudinal analyses of academic progression and dropout. This data has the potential to be reused to support research on factors influencing student retention, allow for the development of predictive models to identify students at risk of leaving their studies, and offer a resource for comparative studies in higher education.

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