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Abandono escolar en la educación superior Un mapeo sistemático de las causas y las estrategias de retención Estudio de caso: Universidad de California, San Francisco

dc.contributor.authorZaparan Cardona, Luz Marina
dc.contributor.authorCervantes Lozano, Mariana Isabel
dc.contributor.authorGarcía Gonzalez, Ramon
dc.contributor.authorSwaminathan, Jose
dc.contributor.authorBayona Ibáñez, Eduar
dc.contributor.authorRico Bautista, Dewar
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-09-18T15:38:28Z
dc.date.available2025-09-18T15:38:28Z
dc.date.issued2024-12-22
dc.identifier.issn978-3-031-75235-3en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5568
dc.description.abstractEl abandono universitario es un problema persistente que ha afectado a las instituciones educativas durante muchos años. Este fenómeno surge de una convergencia de factores desfavorables de diversa índole, tales como: académicos, familiares, económicos, sociales e institucionales, que influyen en la decisión de los estudiantes de abandonar sus estudios. Además, el abandono puede ser el resultado de una falta de identificación y atención a áreas problemáticas dentro de la universidad, como deficiencias pedagógicas, falta de flexibilidad en los horarios e insuficiencia en la asistencia psicológica, entre otros aspectos que requieren fortalecimiento. Este problema no solo afecta a los estudiantes, sino también a las instituciones de educación superior. Se han realizado muchos estudios para abordareste fenómeno mediante la implementación de diferentes herramientas con las que se han obtenido resultados favorables. A partir de ahí, se ha llevado a cabo este estudio con el objetivo de recopilar y analizar estos documentos mediante un mapeo sistemático de la bibliografía. Esta investigación se llevó a cabo en cuatro (4) bibliotecas digitales, donde se obtuvieron 3372 artículos de investigación que se sometieron a diferentes filtros, obteniéndose finalmente 50 artículos que responden a cinco (5) preguntas de investigación que se han planteado en el estudio.en_US
dc.language.isoen_USen_US
dc.relationhttps://link.springer.com/chapter/10.1007/978-3-031-75236-0_13en_US
dc.titleAbandono escolar en la educación superior Un mapeo sistemático de las causas y las estrategias de retención Estudio de caso: Universidad de California, San Franciscoen_US
dc.title.translatedStudent Attrition in Higher Education A Systematic Mapping of Causes and Retention Strategiesen_US
dc.contributor.cvlacDewar Rico Bautista [0000255815]en_US
dc.contributor.googlescholarJose Swaminathan [C3QVSY8AAAAJ]en_US
dc.contributor.googlescholarEduar Bayona Ibáñez [0ebwT-0AAAAJ]en_US
dc.contributor.googlescholarDewar Rico Bautista [q_ZtKjsAAAAJ]en_US
dc.contributor.googlescholarDewar Rico Bautista [q_ZtKjsAAAAJ]en_US
dc.contributor.orcidLuz Marina Zaparan Cardona [0009-0001-3693-7650]en_US
dc.contributor.orcidMariana Isabel Cervantes Lozano [0009-0006-1618-4184]en_US
dc.contributor.orcidRamon García Gonzalez [0000-0001-9639-104X]en_US
dc.contributor.orcidJose Swaminathan [0000-0002-9151-0000]en_US
dc.contributor.orcidEduar Bayona Ibáñez [0000-0002-7604-6860]en_US
dc.contributor.orcidDewar Rico Bautista [0000-0002-1808-3874]en_US
dc.contributor.researchgateLuz Marina Zaparan Cardona [Luz-Marina-Zaparan-Cardona-2300731714]en_US
dc.contributor.researchgateMariana Isabel Cervantes Lozano [Mariana-Isabel-Cervantes-Lozano-2300747301]en_US
dc.contributor.researchgateRamon García Gonzalez [Ramon-Garcia-Gonzalez-2300733094]en_US
dc.contributor.researchgateEduar Bayona Ibáñez [Eduar-Bayona-Ibanez]en_US
dc.contributor.researchgateDewar Rico Bautista [Dewar-Rico-Bautista]en_US
dc.contributor.scopusRamon García Gonzalez [7005762284]en_US
dc.contributor.scopusEduar Bayona Ibáñez [58661099500]en_US
dc.contributor.scopusJose Swaminathan [57224920923]en_US
dc.contributor.scopusDewar Rico Bautista [58689228900]en_US
dc.description.abstractenglishUniversity dropout is a persistent problem that has affected educational institutions for many years. This phenomenon arises from a convergence of various unfavorable factors, such as academic, family, economic, social, and institutional factors, which influence students' decisions to abandon their studies. In addition, dropout can be the result of a lack of identification and attention to problem areas within the university, such as pedagogical deficiencies, lack of flexibility in schedules, and insufficient psychological support, among other aspects that need to be strengthened. This problem affects not only students but also higher education institutions. Many studies have been conducted to address this phenomenon through the implementation of different tools, which have yielded favorable results. Based on this, the present study was conducted with the aim of compiling and analyzing these documents through a systematic mapping of the literature. This research was carried out in four (4) digital libraries, where 3,372 research articles were obtained and subjected to different filters, ultimately yielding 50 articles that respond to five (5) research questions posed in the study.en_US
dc.subject.proposalEducación superioren_US
dc.subject.proposalAbandono escolaren_US
dc.subject.proposalEstrategias de retenciónen_US
dc.subject.proposalEducación universitariaen_US
dc.subject.keywordsHigher educationen_US
dc.subject.keywordsStudent dropouten_US
dc.subject.keywordsRetention strategiesen_US
dc.subject.keywordsUniversity educationen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTDIVen_US
dc.relation.referencesBaranyi, M., Nagy, M., Molontay, R.: Interpretable deep learning for university dropout prediction. In: Proceedings of the 21stAnnual Conference on Information Technology Education [Internet], pp. 13–9.ACM, NewYork,NY,USA (2020). https://doi.org/10.1145/3368308.341 5382en_US
dc.relation.referencesBedregal-Alpaca, N., Tupacyupanqui-Jaén, D., Cornejo-Aparicio, V.: Analysis of the academic performance of Systems Engineering students, possibilities of dropping out and proposals for their retention. Ingeniare Chilean engineering magazine [Internet] 28(4), 668–83 (2020). http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-330520200 00400668&lng=en&nrm=iso&tlng=enen_US
dc.relation.referencesBerka, P., Marek, L.: Bachelor’s degree student dropouts: Who tends to stay and who tends to leave? Stud. Educ. Eval. 70, 100999 (2021)en_US
dc.relation.referencesBonaldo, L., Pereira, L.N.: Dropout: Demographic Profile of Brazilian University Students. Procedia - Social and Behavioral Sciences [Internet] 228, 138–43 (2016). https://linkinghub. elsevier.com/retrieve/pii/S1877042816309466en_US
dc.relation.referencesBurgos, C., Campanario, M.L., de la Peña, D., Lara, J.A., Lizcano, D., Martínez, M.A.: Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput. Electr. Eng. 66, 541–556 (2018)en_US
dc.relation.referencesChindoy Chasoy, B.Y., Diaz Pedroza, K.Y., Rosado Gómez, A.A.: Development of software to predict academic performance using data mining techniques and tools. Journal of Physics: Conference Series [Internet] 1708(1), 012037 (2020). https://doi.org/10.1088/1742- 6596/1708/1/012037en_US
dc.relation.referencesChristou, V., Tsoulos, I., Loupas, V., Tzallas, A.T., Gogos, C., Karvelis, P.S., et al.: Performance and early drop prediction for higher education students using machine learning. Expert Syst. Appl. 225, 120079 (2023)en_US
dc.relation.referencesda Cruz, R.C., Juliano, R.C., Monteiro Souza, F.C., Correa Souza, A.C.: A score approach to identify the risk of students dropout: an experiment with Information Systems Course. In: Proceedings of the XIX Brazilian Symposium on Information Systems [Internet], pp. 120–7. ACM, New York, NY, USA (2023). https://doi.org/10.1145/3592813.3592896en_US
dc.relation.referencesElmehdi,H.M., ZawamDalah, E., Bukhatir,A., Ibrahem, A.M.: Retention at the University of Sharjah: Factors and Strategies. In: 2020 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1–6. IEEE (2020)en_US
dc.relation.referencesFeng, G., Fan,M.: Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization. Expert Syst. Appl. 237, 121555 (2024)en_US
dc.relation.referencesFior, C.A., et al.: IMPACT OF SELF-EFFICACY AND ACADEMIC PERFORMANCE IN THEDROPOUT OF HIGHER EDUCATION STUDENTS. School and Educational Psychology [Internet] 26 (2022). http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-855 72022000100322&tlng=enen_US
dc.relation.referencesGupta, K., Gupta, K., Dwivedi, P., Chaudhry, M.: Binary Classification of Students’ Dropout Behavior in Universities using Machine Learning Algorithms. In: 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 709–14 IEEE (2024)en_US
dc.relation.referencesGuzmán-Castillo, S., et al.: Implementation of a Predictive Information System for University Dropout Prevention. In: Procedia Computer Science (2022)en_US
dc.relation.referencesHegde, V., Prageeth, P.P.: Higher education student dropout prediction and analysis through educational data mining. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC) [Internet], pp. 694–9. IEEE (2018). https://ieeexplore.ieee.org/document/ 8398887/en_US
dc.relation.referencesKayanda, A.M., Machuve, D.: A web-based data visualization tool regarding school dropouts and user assessment. Engineering, Technology & Applied Science Research [Internet] 10(4), 5967–73 (2020). http://etasr.com/index.php/ETASR/article/view/3411en_US
dc.relation.referencesKemper, L., Vorhoff, G.,Wigger, B.U.: Predicting student dropout: A machine learning approach. European Journal of Higher Education [Internet] 10(1), 28–47 (2020). https://doi.org/ 10.1080/21568235.2020.1718520en_US
dc.relation.referencesKim, D., Kim, S.: Sustainable education: analyzing the determinants of university student dropout by nonlinear panel data models. Sustainability. 10(4), 954 (2018)en_US
dc.relation.referencesKrüger, J.G.C., Britto, A. de S., Barddal, J.P.: An explainable machine learning approach for student dropout prediction. Expert Systems with Applications, 233 (2023)en_US
dc.relation.referencesKuzilek, J., Zdrahal, Z., Fuglik, V.: Student success prediction using student exam behavior. Futur. Gener. Comput. Syst. 125, 661–671 (2021)en_US
dc.relation.referencesLam-On, N., Boongoen, T.: Using cluster ensemble to improve classification of student dropout in Thai university. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 (2014)en_US
dc.relation.referencesLlauró, A., Fonseca, D., Romero, S., Aláez, M., Lucas, J.T., Felipe, M.M.: Identification and comparison of the main variables affecting early university dropout rates according to knowledge area and institution. Heliyon. 9(6), e17435 (2023)en_US
dc.relation.referencesMaldonado, S., Miranda, J., Olaya, D., Vásquez, J., Verbeke, W.: Redefining profit metrics for boosting student retention in higher education. Decis. Support Syst. 143, 113493 (2021)en_US
dc.relation.referencesMarbouti, F.,Diefes-Dux, H.A.,Madhavan, K.:Models for early prediction of at-risk students in a course using standards-based grading. Computers and Education, 103 (2016)en_US
dc.relation.referencesMartins, M.v., Baptista, L., Machado, J., Realinho, V.: Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education. Applied Sciences 13(8), 4702 (2023)en_US
dc.relation.referencesMatz, S.C., Bukow, C.S., Peters, H., Deacons, C., Stachl, C.: Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports 13(1) (2023)en_US
dc.relation.referencesMoreira da Silva, D.E., Solteiro Pires, E.J., Reis, A., de Moura Oliveira, P.B., Barroso, J.: Forecasting Students Dropout: A UTAD University Study. Future Internet 14(3) (2022)en_US
dc.relation.referencesMubarak, A.A., Cao, H., Hezam, I.M.: Deep analytical model for student dropout prediction in massive open online courses. Comput. Electr. Eng. 93, 107271 (2021)en_US
dc.relation.referencesCarrero Arango, M. L., & González Rodríguez, M. F. (2017). La educación rural en Colombia: experiencias y perspectivas. Praxis Pedagógica, 16(19), 79–89. https:// doi.org/10.26620/uniminuto.praxis.16.19.2016.79-89en_US
dc.relation.referencesOlaya, D., Vásquez, J., Maldonado, S., Miranda, J., Verbeke, W.: Uplift Modeling for preventing student dropout in higher education. Decision Support Systems 134 (2020)en_US
dc.relation.referencesPaura, L., Arhipova, I.: Cause Analysis of Students’ Dropout Rate in Higher Education Study Program. Procedia - Social and Behavioral Sciences 109 (2014)en_US
dc.relation.referencesPecuchova, J., Drlik, M.: Predicting students at risk of early dropping out from course using ensemble classification methods. Procedia Computer Science 225, 3223–3232 (2023)en_US
dc.relation.referencesPhan,M., de Caigny,A., Coussement,K.:Adecision support framework to incorporate textual data for early student dropout prediction in higher education. Decision Support Systems 168 (2023)en_US
dc.relation.referencesPrenkaj, B., Stilo, G., Madeddu, L.: Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3513–4. ACM, New York, NY, USA (2020)en_US
dc.relation.referencesRodrigues, H., et al.: Artificial Intelligence Algorithms to Predict College Students’ Dropout: ASystematic Mapping Study. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, pp. 344–51 (2024)en_US
dc.relation.referencesYes, M.J.: Student Academic and Social Engagement in the Life of the Academy—A Lever for Retention and Persistence in Higher Education. Education Sciences 13(3) (2023)en_US
dc.relation.referencesSrairi, S.: An analysis of factors affecting student dropout: the case of tunisian universities. Int. J. Educ. Reform. 31(2), 168–186 (2022)en_US
dc.relation.referencesTenjo-García, J.S., Figueroa-García, J.C.: Analysis of student dropout in industrial engineering students using computational intelligence techniques. In: 2024 IEEE World Engineering Education Conference (EDUNINE), pp. 1–6. IEEE (2024)en_US
dc.relation.referencesTimaran Pereira, R., Caicedo Zambrano, J.: Application of decision trees for detection of student dropout profiles. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 528–31. IEEE (2017)en_US
dc.relation.referencesTiukhova, E., Vemuri, P., Flores, N.L., Islind, A.S., Óskarsdóttir, M., Poelmans, S., et al.: Explainable learning analytics: assessing the stability of student success prediction models by means of explainable AI. Decis. Support Syst. 182, 114229 (2024)en_US
dc.relation.referencesTruta, C., Parv, L., Topala, I.: Academic engagement and intention to drop out: Levers for sustainability in higher education. Sustainability (Switzerland) 10(12) (2018)en_US
dc.relation.referencesTurhan, D., Scheunemann, A., Schnettler, T., Bäulke, L., Thies, D.O., Dresel, M., et al.: Temporal development of student burnout symptoms: Sociodemographic differences and linkage to university dropout intentions. Contemp. Educ. Psychol. 73, 102185 (2023)en_US
dc.relation.referencesVaarma,M., Li, H.: Predicting student dropouts with machine learning: an empirical study in Finnish higher education. Technol. Soc. 76, 102474 (2024)en_US
dc.relation.referencesVega, H., Sanez, E., de La Cruz, P., Moquillaza, S., Pretell, J.: Intelligent System to Predict University StudentsDropout. International journal of online and biomedical engineering 18(7) (2022)en_US
dc.relation.referencesVenegas-Muggli, J.I.: Higher education dropout of non-traditional mature freshmen: the role of sociodemographic characteristics. Studies in Continuing Education 42(3) (2020)en_US
dc.relation.referencesViloria, A., et al.: Integration of data technology for analyzing university dropout. In: Procedia Computer Science (2019)en_US
dc.relation.referencesViloria, A., Pineda Lezama, O.B.: Mixture structural equation models for classifying university student dropout in Latin America. Procedia Computer Science. 160, 629–634 (2019)en_US
dc.relation.referencesWild, S., Schulze, H.L.: Student dropout and retention: An event history analysis among students in cooperative higher education. Int. J. Educ. Res. 104, 101687 (2020)en_US
dc.relation.referencesXenos, M., Pierrakeas, C., Pintelas, P.: A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the HellenicOpenUniversity.Comput. Educ. 39(4), 361–377 (2002)en_US
dc.relation.referencesYilmaz, T., Sarpkaya, R.: A case study on university dropout: perspectives from educational faculty students and academicians. Res. Educ. Administr. Leaders. 7(3), 519–559 (2022)en_US
dc.relation.referencesZumárraga-Espinosa, M.: Academic resilience, performance and intention to drop out in university students from Quito. Latin American Journal of Social Sciences, Children and Youth. 21(3), 1–34 (2023)en_US
dc.relation.referencesPetersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. from Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering, Italy (2008)en_US
dc.identifier.DOI10.1007/978-3-031-75236-0_13en_US


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