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Análisis de la Deserción de Estudiantes Universitarios usando Técnicas de Minería de Datos
Open accessAbstract
ResumenSe realiza un estudio para determinar cuales son y cual es la importancia de las variables que llevan a un estudiante a abandonar sus estudios universitarios, usando técnicas de minería de datos.La deserción de los estudiantes de educación superior genera una serie de inconvenientes que afectan a los estudiantes y las universidades.Los resultados obtenidos a partir de los datos proporcionados por las carreras de Ingeniería de la Universidad Católica del Norte en Antofagasta y Coquimbo (Chile) determinan que las variables que mejor explican la deserción de un estudiante son, las razones socioeconómicas y el puntaje de ingreso a la universidad (PSU).Según el árbol de decisión construido se concluye que la retención se sitúa en un 78,3%.La calidad de los clasificadores permite asegurar que sus predicciones son correctas, con niveles estadísticos de curva ROC de 76%, 75% y 83% de acierto para los clasificadores de red bayesiana, árbol de decisión y red neuronal respectivamente.
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In the citing paper
“AI applications can contribute to forecasting the student dropout rate and determining the factors that affect their performance”
From Artificial intelligence applications in Latin American higher education: a systematic review (2022) · cites this paper as Miranda & Guzmán, 2017
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In the citing paper
“AI applications can contribute to forecasting the student dropout rate and determining the factors that affect their performance”
From Artificial intelligence applications in Latin American higher education: a systematic review (2022) · cites this paper as Miranda & Guzmán, 2017
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Research works printed but not linked: 8
- Vol. -2017 desarrollo esta investigación tuvo como limitación el acceso a la información psicosocial del estudiante, la cual aparece como una variable importante en el marco referencial, esta podría aportar a estudiar la deserción desde la perspectiva vocacional, sumado a las dimensiones económicas y académicas estudiadas en este artículo. Esta podría ser una interesante corriente a seguir por una futura investigación. autores agradecen a la Universidad Católica del Norte por permitir el uso de sus datos académicos. REFERENCIAS Bayes, M. y M. Price, An essay towards solving a problem in the doctrine of chances. By the late rev. Mr. Bayes, frs communicated by Mr. price, in a letter to john canton, amfrs. pp. 370-418, Philosophical Transactions (1763)
- Bean, J. P., Dropouts and turnover: The synthesis and test of a causal model of student attrition, Research in higher education, 12 (2), 155-187 (1980)
- Bean, J., Interaction effects based on class level in an explanatory model of college student dropout syndrome, American educational Research journal, 22 (1), 35-64 (1985)
- Chacon, F., Spicer, D. y A. Valbuena, Analytics in support of student retention and success, Research Bulletin 3. Louisville, CO: Educause, Center for Applied Research (2012)
- Delen, D., A comparative analysis of machine learning techniques for student retention management, Decision Support Systems, 49 (4), 498-506 (2010)
- Elmasri, R., S.B. Navathe, V.C. Castillo, B.G. Espiga y G.Z. Pérez, Fundamentos de sistemas de bases de datos. Addison-Wesley (2002)
- Sánchez, A., M.A. Gutiérrez, C. Meneses, Using data mining to support the university decision process: A case in a chilean university, AMCIS 2005 Proceedings, p. 350 (2005)
- Tinto, V., Dropout from higher education: A theoretical synthesis of recent research, Review of educational research, pp. 89-125 (1975)
Cited by
Artificial intelligence applications in Latin American higher education: a systematic review
Salas-Pilco, S. Z., & Yang, Y. (2022) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00326-w · Cited by 330
“In this context, AI applications can contribute to forecasting the student dropout rate and determining the factors that affect their performance (Bedregal-Alpaca et al., 2020; da Fonseca Silveira et al., 2019; Miranda & Guzmán, 2017; Santos et al., 2020).”
Background
Metadata
- Authors
- Miranda, M. A., & Guzmán, J.
- Affiliations
- Universidad Católica del Norte (CL)
- ORCID
- Miranda, Mauricio A
- Venue
- Formación universitaria
- Published
- 2017
- Volume
- 10
- Issue
- 3
- Pages
- 61-68
- ISSN
- 0718-5006 (ISSN-L)
- OpenAlex
- W2626747875
- Cited by
- 50
- References
- 0
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 8 · 2021: 7 · 2020: 5 · 2019: 4 · 2018: 4 · 2017: 1
- FWCI
- 9.95
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- Formación universitaria (publishedVersion, cc-by-nc)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
- Americanae (AECID Library) (submittedVersion, cc-by-nc)
- Redalyc (Universidad Autónoma del Estado de México) (submittedVersion)
- Topics
- Social Sciences › Education › Evasion and Academic Success Factors
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- university dropout, student retention, higher education, socioeconomic factors, university admission scores, engineering students, decision tree, Bayesian networks, artificial neural networks, ROC curve, Universidad Católica del Norte
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- CoChat. (2026). Citation check: Análisis de la Deserción de Estudiantes Universitarios usando Técnicas de Minería de Datos. https://app.cochat.ai/papers/10.4067/s0718-50062017000300007
- The paper
- Miranda, M. A., & Guzmán, J. (2017). Análisis de la Deserción de Estudiantes Universitarios usando Técnicas de Minería de Datos. Formación universitaria. https://doi.org/10.4067/s0718-50062017000300007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
