Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos

ilustraciones, diagramas

Autores:: Zapata Medina, Daniel

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
dc.title.translated.eng.fl_str_mv	Metric-driven and a data mining technique method to support detection of students at risk of school dropout
title	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
spellingShingle	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 370 - Educación::379 - Asuntos de política pública en educación Minería de datos Data mining Dropouts Deserción escolar Preprocesamiento de datos educativos Métricas Técnicas de minería de datos Fusión a nivel de clasificador School dropout Educational data preprocessing Metrics Data mining techniques Late fusion
title_short	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
title_full	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
title_fullStr	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
title_full_unstemmed	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
title_sort	Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos
dc.creator.fl_str_mv	Zapata Medina, Daniel
dc.contributor.advisor.none.fl_str_mv	Espinosa Bedoya, Albeiro Jiménez Builes, Jovani Alberto
dc.contributor.author.none.fl_str_mv	Zapata Medina, Daniel
dc.contributor.researchgroup.spa.fl_str_mv	GIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 370 - Educación::379 - Asuntos de política pública en educación
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 370 - Educación::379 - Asuntos de política pública en educación Minería de datos Data mining Dropouts Deserción escolar Preprocesamiento de datos educativos Métricas Técnicas de minería de datos Fusión a nivel de clasificador School dropout Educational data preprocessing Metrics Data mining techniques Late fusion
dc.subject.lemb.none.fl_str_mv	Minería de datos Data mining Dropouts
dc.subject.proposal.spa.fl_str_mv	Deserción escolar Preprocesamiento de datos educativos Métricas Técnicas de minería de datos Fusión a nivel de clasificador
dc.subject.proposal.eng.fl_str_mv	School dropout Educational data preprocessing Metrics Data mining techniques Late fusion
description	ilustraciones, diagramas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-26T15:06:45Z
dc.date.available.none.fl_str_mv	2021-10-26T15:06:45Z
dc.date.issued.none.fl_str_mv	2021-10-22
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80615
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80615 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Acero, A ;Achury, J C. ;Morales, J C.: University dropout: A prediction model for an engineering program in bogota, Colombia. En:B., Kloot (Ed.):Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections, Research in Engineering Education Network, 2019. – ISBN 9780799226003, p.483–490 Agrusti, F ;Mezzini, M ;Bonavolontá, G: Deep learning approach for predicting university dropout: A case study at roma Tre university. En:Journal of E-Learning and Knowledge Society16 (2020), Nr. 1, p. 44–54 Aguilar-Gonzalez, S ;Palafox, L: Prediction of Student Attrition Using MachineLearning. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11835 LNAI (2019), p.212–222 Ahmad Tarmizi, S S. ;Mutalib, S ;Abdul Hamid, N H. ;Abdul-Rahman, S ;Md Ab Malik, A: A Case Study on Student Attrition Prediction in Higher EducationUsing Data Mining Techniques. En:Communications in Computer and InformationScience1100 (2019), p. 181–192 Ahmed, S A. ;Khan, S I.: A machine learning approach to Predict the Engineering Students at risk of dropout and factors behind: Bangladesh Perspective. En:2019 10th International Conference on Computing, Communication and NetworkingTechnologies, ICCCNT 2019, 2019 Ajit, Kumar J. ;C., K. J.: Dropout Classification through Discriminant FunctionAnalysis: A Statistical Approach. En:International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN :2456-33072 (2017), Nr. 4, p. 572–577 Alban, M ;Mauricio, D: Neural networks to predict dropout at the universities.En:International Journal of Machine Learning and Computing9 (2019), Nr. 2, p.149–153. – ISSN 20103700 Alban, Mayra ;David, Mauricio: Factors to predict dropout at the universities:A case of study in Ecuador. En:IEEE Global Engineering Education Conference,EDUCON2018-April (2018), p. 1238–1242. – ISBN 9781538629574 Alban Taipe, M S. ;Mauricio Sánchez, D: Prediction of university dropout through technological factors: A case study in Ecuador. En:Espacios39 (2018), Nr.52 Alejandro Gonzalez-Campos, Jose ;Manuel Carvajal-Muquillaza, Cristian;Elias Aspee-Chacon, Juan: Modeling of university dropout using Markov chains.En:UNICIENCIA34 (2020), Nr. 1, p. 129–146. – ISSN 1011–0275 Alyahyan, E ;D ̈us ̧teg ̈or, D: Predicting academic success in higher education: literature review and best practices. En:International Journal of Educational Technology in Higher Education17 (2020), Nr. 1. – ISSN 23659440 Ameri, S ;Fard, M J. ;Chinnam, R B. ;Reddy, C K.: Survival analysis based framework for early prediction of student dropouts. En:International Conference onInformation and Knowledge Management, ProceedingsVol. 24-28-Octo, 2016, p. 903–912 Astin, Alexander: What matters in college: four critical years revisited. En:LiberalEducation4 (1993), p. 4 Barros, T M. ;Silva, I ;Guedes, L A.: Determination of dropout student profilebased on correspondence analysis technique. En:IEEE Latin America Transactions17 (2019), Nr. 9, p. 1517–1523 Bean, John P.: Dropouts and turnover: The synthesis and test of a causal model of student attrition. En:Research in Higher Education12 (1980), Nr. 2, p. 155–187. –ISSN 1573–188X Bean, John P. ;Metzner, Barbara S.: A Conceptual Model of Nontraditional Undergraduate Student Attrition. En:Review of Educational Research55 (1985), Nr. 4,p. 485–540 Bedregal-Alpaca, N ;Aruquipa-Velazco, D ;Cornejo-Aparicio, V: Data mining techniques to extract academic behavior profiles and predict university desertion[Técnicas de data mining para extraer perfiles comportamiento académico y predecirla deserción universitaria]. En:RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2020 (2020), Nr. E27, p. 592–604 Bedregal-Alpaca, N ;Cornejo-Aparicio, V ;Zarate-Valderrama, J ;Yanque-Churo, P: Classification models for determining types of academic risk and predicting dropout in university students. En:International Journal of AdvancedComputer Science and Applications11 (2020), Nr. 1, p. 266–272 Beemer, Joshua ;Spoon, Kelly ;He, Lingjun ;Fan, Juan juan ;Levine, Richard A.:Ensemble learning for estimating individualized treatment effects in student success studies. En:International Journal of Artificial Intelligence in Education28 (2018),Nr. 3, p. 315–335 Blaser, R ;Fryzlewicz, P: Random rotation ensembles. En:Journal of MachineLearning Research17 (2016) Breiman, Leo: Random forests. En:Machine learning45 (2001), Nr. 1, p. 5–32 Brown, I ;Mues, C: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. En:Expert Systems with Applications39 (2012),Nr. 3, p. 3446–3453 Cabus, S J. ;De Witte, K: The effectiveness of active school attendance interventions to tackle dropout in secondary schools: a Dutch pilot case. En:EmpiricalEconomics49 (2015), Nr. 1, p. 65–80 Cabus, S J. ;De Witte, K: Why Do Students Leave Education Early? Theory andEvidence on High School Dropout Rates. En:Journal of Forecasting35 (2016), Nr. 8,p. 690–702 Cano, Alberto ;Zafra, Amelia ;Ventura, Sebastián: An interpretable classification rule mining algorithm. En:Information Sciences240 (2013), p. 1–20 Castellanos, M C R. ;Alvarado, L D N. ;Villamil, J E P.: University student desertion analysis using agent-based modeling approach. En: COMPLEXIS 2018 -Proceedings of the 3rd International Conference on Complexity, Future InformationSystems and RiskVol. 2018-March, 2018, p. 128–135 Castillo-Sanchez, Mario ;Gamboa-Araya, Ronny ;Hidalgo-Mora, Randall:Factors that influence student dropout and failing grades in a university mathematics course. En: UNICIENCIA 34 (2020), Nr. 1, p. 219–245. – ISSN 1011–0275 Castro R., L F. ;Espitia P., E ;Montilla, A F.: Applying CRISP-DM in a KDD process for the analysis of student attrition. En:Communications in Computer andInformation Science885 (2018), p. 386–401[29]Chai, K E K. ;Gibson, D: Predicting the risk of attrition for undergraduate students with time based modelling. En:Proceedings of the 12th International Conference onCognition and Exploratory Learning in the Digital Age, CELDA 2015, 2015, p. 109–116 Chung, Jae Y. ;Lee, Sunbok: Dropout early warning systems for high school students using machine learning. En:Children and Youth Services Review96 (2019), Nr.November 2018, p. 346–353. – ISSN 01907409 Costa, E B. ;Fonseca, B ;Santana, M A. ;de Ara ́ujo, F F. ;Rego, J: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. En:Computers in HumanBehavior73 (2017), p. 247–256 Cover, T. M.: The Best Two Independent Measurements Are Not the Two Best.En:IEEE Transactions on Systems, Man, and CyberneticsSMC-4 (1974), Nr. 1, p.116–117 Cuji Chacha, B R. ;Gavilanes López, W L. ;Vicente Guerrero, V X. ;Villacis Villacis, W G.: Student Dropout Model Based on Logistic Regression.En:Communications in Computer and Information Science1194 CCIS (2020), p.321–333 da Cunha, J A. ;Moura, E ;Analide, C: Data mining in academic databases to detect behaviors of students related to school dropout and disapproval. En: Advances in Intelligent Systems and Computing445 (2016), p. 189–198 Da Fonseca Silveira, R ;Holanda, M ;De Carvalho Victorino, M ;Ladeira, M: Educational data mining: Analysis of drop out of engineering majors at the UnB - Brazil. En:Proceedings - 18th IEEE International Conference on MachineLearning and Applications, ICMLA 2019, 2019, p. 259–262 Da Silva, Paulo M. ;Lima, Marilia N. ;Soares, Wedson L. ;Silva, Iago R. ;De Fagundes, Roberta A. ;De Souza, Fernado F.: Ensemble regression models applied to dropout in higher education. En:Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019(2019), p. 120–125. ISBN 9781728142531 De Santos, K J O. ;Menezes, A G. ;De Carvalho, A B. ;Montesco, C A E.:Supervised learning in the context of educational data mining to avoid university students dropout. En:Proceedings - IEEE 19th International Conference on AdvancedLearning Technologies, ICALT 2019, 2019, p. 207–208 Delen, D ;Topuz, K ;Eryarsoy, E: Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition. En:EuropeanJournal of Operational Research281 (2020), Nr. 3, p. 575–587. – ISSN 03772217 Departamento Administrativo Nacional de Estadística (DANE).Boletín Técnico de la Investigación de Educación Formal 2018. 2019 Dharmawan, T ;Ginardi, H ;Munif, A: Dropout Detection Using Non-AcademicData. En:Proceedings - 2018 4th International Conference on Science and Technology,ICST 2018, 2018 Ding, C. ;Peng, H.Minimum redundancy feature selection from microarray gene expression data. 2003 Durkheim E. (1951):Suicide: A study in sociology. (J.A. Spaulding G. Simpson,Trans.)Glencoe IL Free Press., 1897 Dwork, C ;Roth, A: The algorithmic foundations of differential privacy. En:Foundations and Trends in Theoretical Computer Science9 (2013), Nr. 3-4, p. 211–487 Eckert, K B. ;Suénaga, R: Analysis of attrition-retention of college students using classification technique in data mining [Análisis de deserción-permanencia de estudiantes universitarios utilizando técnica de clasificación minería de datos]. En: Formación Universitaria8 (2015), Nr. 5, p. 3–12 Elbir, A ;G ̈und ̈uz, E ;Diri, B: Estimating the School Dropout Trend by Using DataMining Methods [Veri Madenciliˇgi Y ̈ontemleri Kullanarak Okul Birakma EˇgilimininTahmin Edilmesi]. En:Proceedings - 2018 Innovations in Intelligent Systems andApplications Conference, ASYU 2018, 2018 Fenton, Norman ;Bieman, James:Software metrics: a rigorous and practical approach. 3. CRC press, 2014. – 618 p. Fernández, J ;Rojas, A ;Daza, G ;Gómez, D ; ́Alvarez, A ;Orozco, ́A: Student desertion prediction using kernel relevance analysis. En:Lecture Notes in ComputerScience (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11047 LNCS (2018), p. 263–270 Fonseca, M T. ;Gazo, P F.: Longitudinal study of the dropout and reentry process in students of social sciences: The case of Business Administration and Management[Estudio longitudinal del proceso de abandono y reingreso de estudiantes de CienciasSociales. El caso de Administraci. En:Educar55 (2019), Nr. 2, p. 401–417 Gamao, A O. ;Gerardo, B D.: Prediction-based model for student dropouts using modified mutated firefly algorithm. En:International Journal of Advanced Trends inComputer Science and Engineering8 (2019), Nr. 6, p. 3461–3469[ Gaviria, Alejandro: Los que suben y los que bajan. En: Educación y Movilidad Social en Colombia. Bogotá: Ediciones Alfa omega y Fedesarrollo(2002) Gu, Q ;Cai, Z ;Zhu, L ;Huang, B: Data Mining on Imbalanced Data Sets. En:2008 International Conference on Advanced Computer Theory and Engineering, 2008.– ISSN 2154–7505, p. 1020–1024 Guyon, Isabelle ;Elisseeff, André: An Introduction to Variable and Feature Selection. En:J. Mach. Learn. Res.3 (2003), mar, Nr. null, p. 1157–1182. – ISSN1532–4435 Hackeling, Gavin:Mastering Machine Learning with scikit-learn. Packt PublishingLtd, 2014 Han, Jiawei ;Kamber, Micheline ;Pei, Jian:Data mining concepts and techniques third edition. 2011. – 83–124 p. Hanchuan Peng;Fuhui Long;Ding, C.: Feature selection based on mutual in-formation criteria of max-dependency, max-relevance, and min-redundancy. En:IEEETransactions on Pattern Analysis and Machine Intelligence27 (2005), Nr. 8, p. 1226–1238 Hasan, M N.: A Comparison of Logistic Regression and Linear Discriminant Analysis in Predicting of Female Students Attrition from School in Bangladesh. En:2019 4th International Conference on Electrical Information and Communication Technology,EICT 2019, 2019 Hegde, V: Dimensionality reduction technique for developing undergraduate student dropout model using principal component analysis through R package. En:2016 IEEEInternational Conference on Computational Intelligence and Computing Research, IC-CIC 2016, 2017 Hegde, V ;Prageeth, P P.: Higher education student dropout prediction and analysis through educational data mining. En:Proceedings of the 2nd InternationalConference on Inventive Systems and Control, ICISC 2018, 2018, p. 694–699 Heredia, D ;Amaya, Y ;Barrientos, E: Student Dropout Predictive Model UsingData Mining Techniques. En:IEEE Latin America Transactions13 (2015), Nr. 9, p.3127–3134 Networks, Computational Intelligence and Machine Learning, ESANN2015 - Proceedings, 2015, p. 319–324 Hernández-Blanco, Antonio ;Herrera-Flores, Boris ;Tomás, David ;Navarro-Colorado, Borja: A Systematic Review of Deep Learning Approaches to Educational Data Mining. En:Complexity2019 (2019), p. 22. – ISSN 10990526 Hernandez Gonzalez, A G. ;Melendez Armenta, R A. ;Morales Rosa-les, L A. ;Garcia Barrientos, A ;Tecpanecatl Xihuitl, J L. ;Algredo,I: Comparative Study of Algorithms to Predict the Desertion in the Students at the Bibliograf ́ıa97ITSM-Mexico. En:IEEE Latin America Transactions14 (2016), Nr. 11, p. 4573–4578.– ISSN 15480992 Hernández-Leal, E J. ;Quintero-Lorza, D P. ;Escobar-Naranjo, J C. ;Ramírez- Gómez, J S. ;Duque-Méndez, N D.: Educational data mining for the analysis of student desertion. En:CEUR Workshop ProceedingsVol. 2231, 2018 Hillmert, S ;Groß, M ;Schmidt-Hertha, B ;Weber, H:Informational environments and college student dropout. 2017. – 27–52 p. Hori, G: Identifying Factors Contributing to University Dropout with Sparse LogisticRegression. En:Proceedings - 2018 7th International Congress on Advanced AppliedInformatics, IIAI-AAI 2018, 2018, p. 430–433 Hutagaol, N ;Suharjito: Predictive modelling of student dropout using ensemble classifier method in higher education. En:Advances in Science, Technology andEngineering Systems4 (2019), Nr. 4, p. 206–211 Iam-On, N ;Boongoen, T: Generating descriptive model for student dropout: a re-view of clustering approach. En:Human-centric Computing and Information Sciences7 (2017), Nr. 1 Iam-On, N ;Boongoen, T: Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. En:International Journal of MachineLearning and Cybernetics8 (2017), Nr. 2, p. 497–510 James, John T. ;Tichy, Karen L. ;Collins, Alan ;Schwob, John: Developing a predictive metric to assess school viability. En:Journal of Catholic Education11(2008), Nr. 4, p. 5 Jiménez, Fernando ;Paoletti, Alessia ;Sánchez, Gracia ;Sciavicco, Guido: Predicting the Risk of Academic Dropout with Temporal Multi-Objective Optimization.En:IEEE Transactions on Learning Technologies12 (2019), Nr. 2, p. 225–236. – ISSN19391382 King, B.M. ;Minium, E.W.:Statistical Reasoning in Psychology and Education.Wiley, 2003. – ISBN 9780471211877 Kiss, B ;Nagy, M ;Molontay, R ;Csabay, B: Predicting dropout using high school and first-semester academic achievement measures. En:ICETA 2019 - 17th IEEE International Conference on Emerging eLearning Technologies and Applications,Proceedings, 2019, p. 383–389 Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. 2007. Kori, K ;Pedaste, M ;T ̃onisson, E ;Palts, T ;Altin, H ;Rantsus, R ;Sell, R;Murtazin, K ;R ̈u ̈utmann, T: First-year dropout in ICT studies. En:IEEE GlobalEngineering Education Conference, EDUCONVol. 2015-April, 2015, p. 437–445 Kuhn, Max ;Johnson, Kjell: Feature engineering and selection: A practical approach for predictive models. CRC Press, 2019 Kumar, Mukesh ;Singh, Arjun J. ;Handa, Disha: Literature Survey on EducationalDropout Prediction, 2017 Kuncheva, Ludmila I.:Combining Pattern Classifiers: Methods and Algorithms, Second Edition.c©2014 John Wiley Sons, Inc., Published 2014 Kuo, J Y. ;Pan, C W. ;Lei, B: Using Stacked Denoising Autoencoder for theStudent Dropout Prediction. En:Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017Vol. 2017-Janua, 2017, p. 483–488 Lacher, F ;Staudacher, A P.: Reducing dropouts from Higher Education Institutions through Lean Six Sigma: An exploratory study. En:Proceedings of the SummerSchool Francesco TurcoVol. 13-15-Sept, 2016, p. 59–64 Lima, J ;Alves, P ;Pereira, M ;Almeida, S: Using academic analytics to predict dropout risk in engineering courses. En:Proceedings of the European Conference one-Learning, ECELVol. 2018-Novem, 2018, p. 316–321 Limsathitwong, K ;Tiwatthanont, K ;Yatsungnoen, T: Dropout prediction system to reduce discontinue study rate of information technology students. En:Proceedings of 2018 5th International Conference on Business and Industrial Research:Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018, 2018, p. 110–114 Lix, Lisa M. ;Keselman, Joanne C. ;Keselman, H J.: Consequences of AssumptionViolations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance F Test. En:Review of Educational Research66 (1996), Nr. 4, p. 579–619 López G, Camilo E. ;Guzmán, Elizabeth L. ;González, Fabio A.: Data mining model to predict academic performance at the Universidad Nacional de Colombia.(2013), p. 86 Maheshwari, E ;Roy, C ;Pandey, M ;Rautray, S S.: Prediction of FactorsAssociated with the Dropout Rates of Primary to High School Students in India UsingData Mining Tools. En:Advances in Intelligent Systems and Computing1013 (2020),p. 242–251 Maldonado, S ;Miranda, J ;Olaya, D ;V ́asquez, J ;Verbeke, W: Redefining profit metrics for boosting student retention in higher education. En:Decision SupportSystems143 (2021) Maningba Augustine, L ;Jeyaseelan, M ;Stephen, A: Factors associated with school dropout: A sociological study among the Maram Naga primitive Tribe, Manipur.En:International Journal of Scientific and Technology Research9 (2020), Nr. 1, p.2215–2219. – ISSN 22778616 Manning, Christopher D. ;Raghavan, Prabhakar ;Sch ̈utze, Hinrich: Introduction to Information Retrieval. USA : Cambridge University Press, 2008. – ISBN 0521865719 Márquez-Vera, C ;Cano, A ;Romero, C ;Noaman, A Y M. ;Mousa Fardoun,H ;Ventura, S: Early dropout prediction using data mining: A case study with high school students. En:Expert Systems33 (2016), Nr. 1, p. 107–124 Márquez-Vera, Carlos ;Romero Morales, Cristóbal ;Ventura Soto, Sebastián: Predicting school failure and dropout by using data mining techniques. En:Revista Iberoamericana de Tecnologías del Aprendizaje8 (2013), Nr. 1, p. 7–14. –ISSN 19328540 Martelo, R J. ;Jiménez-Pitre, I ;Villabona-Gómez, N: Determination of reasons for desertion of undergraduate students through the brainstorming and MIC-MAC techniques [Determinación de factores para deserción de estudiantes en pregrado a través de las técnicas lluvia de ideas y MICMAC]. En:Espacios38 (2017), Nr. 20 Martins, L C B. ;Carvalho, R N. ;Carvalho, R S. ;Victorino, M C. ;Holan-da, M: Early prediction of college attrition using data mining. En:Chen X. Luo B.,Luo F Palade V Wani M A. (Ed.):Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017Vol. 2017-Decem, Institute ofElectrical and Electronics Engineers Inc., 2017. – ISBN 9781538614174, p. 1075–1078 Martins, M P G. ;Migueis, V L. ;Fonseca, D S B. ;Gouveia, P D F.: Prediction of academic dropout in a higher education institution using data mining [Previs ̃ao do abandono académico numa institui ̧c ̃ao de ensino superior com recurso a data mining].En:RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2020 (2020), Nr.E28, p. 188–203 Meedech, Phanupong ;Iam-On, Natthakan ;Boongoen, Tossapon: Prediction ofStudent Dropout Using Personal Profile and Data Mining Approach. En:Lavangna-nanda, Kittichai (Ed.) ;Phon-Amnuaisuk, Somnuk (Ed.) ;Engchuan, Worrawat(Ed.) ;Chan, Jonathan H. (Ed.):Intelligent and Evolutionary Systems. Cham : Springer International Publishing, 2016. – ISBN 978–3–319–27000–5, p. 143–155 Ministerio de Educación Nacional de Colombia MEN.Sistema Nacional deIndicadores Educativos para los Niveles de Preescolar, Básica y Media en Colombia.2014 Ministerio de Educación Nacional de Colombia MEN: Plan Estratégico Institucional 2019-2022 Educación de calidad para un futuro con oportunidades para todos Versión 1.0. 2019. – Informe de Investigación. – 41 p. Ministerio de Hacienda y Crédito Público: Presupuesto General de la Nación2020- Decreto No. 2411 de Diciembre 30 de 2019. 2019. – Informe de Investigación. –7 p. Murakami, K ;Takamatsu, K ;Kozaki, Y ;Kishida, A ;Kenya, B ;Noda, I ;Jyunichiro, A ;Takao, K ;Mitsunari, K ;Nakamura, T ;Nakata, Y: Predicting the Probability of Student Dropout through EMIR Using Data from Current andGraduate Students. En:Proceedings - 2018 7th International Congress on AdvancedApplied Informatics, IIAI-AAI 2018, 2018, p. 478–481 Mutrofin, S ;Ginardi, R V H. ;Fatichah, C ;Kurniawardhani, A: A critical assessment of balanced class distribution problems: The case of predict student dropout. En:Test Engineering and Management81 (2019), Nr. 11-12, p. 1764–1770 Nagy, M ;Molontay, R: Predicting Dropout in Higher Education Based on Secondary School Performance. En:INES 2018 - IEEE 22nd International Conference onIntelligent Engineering Systems, Proceedings, 2018, p. 389–394 do Nascimento, R L S. ;das Neves Junior, R B. ;de Almeida Neto, M A. ;deAraújo Fagundes, R A.: Educational data mining: An application of regressors in predicting school dropout. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10935LNAI (2018), p. 246–257 Navarrete, D M. ;Quina, L M. ;Pedraza, L F. ;Gomez, E Z.: Proposal to diagnose the factors that affect academic desertion of the politécnico empresarial colombiano through the lean six sigma methodology. En:Proceedings - 2019 7th InternationalEngineering, Sciences and Technology Conference, IESTEC 2019, 2019, p. 300–305 Neves, J ;Figueiredo, M ;Vicente, L ;Vicente, H: A case based reasoning view of school dropout screening. En:Lecture Notes in Electrical Engineering376 (2016),p. 953–964 Nuankaew, P: Dropout situation of business computer students, University of Pha-yao. En:International Journal of Emerging Technologies in Learning14 (2019), Nr.19, p. 117–131 Nuankaew, P ;Nuankaew, W ;Phanniphong, K ;Fooprateepsiri, R ;Bussaman, S: Analysis dropout situation of business computer students at University of Phayao. En:Advances in Intelligent Systems and Computing1134 AISC (2020), p.419–432 Oneto, L ;Siri, A ;Luria, G ;Anguita, D: Dropout prediction at university of genoa: A privacy preserving data driven approach. En:ESANN 2017 - Proceedings,25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2017, p. 135–140 Organización para la Cooperación y el Desarrollo Económicos (OC-DE);Brunner, Jos ́e J. (Ed.) ;Gomes, Candido A. (Ed.) ;Fordham, Elizabeth(Ed.) ;Phair, Rowena (Ed.) ;Pons, Anna (Ed.) ;Zapata, Juliana (Ed.):Revision de politicas nacionales de educacion: La educación en Colombia 2016.c©2016 Ministerio de Educación Nacional para esta version en español, 2016. – 336 p.. – ISBN9789264250598 Orong, M Y. ;Caroro, R A. ;Durias, G D. ;Cabrera, J A. ;Lonzon, H ;Ricalde, G T.: A predictive analytics approach in determining the predictors of student attrition in the higher education institutions in the Philippines. En:ACMInternational Conference Proceeding Series, 2020, p. 222–225 Orong, M Y. ;Sison, A M. ;Medina, R P.: A new crossover mechanism for genetic algorithm with rank-based selection method. En:Proceedings of 2018 5th InternationalConference on Business and Industrial Research: Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018, 2018, p.83–88 Orooji, M ;Chen, J: Predicting louisiana public high school dropout through imbalanced learning techniques. En:Proceedings - 18th IEEE International Conference onMachine Learning and Applications, ICMLA 2019, 2019, p. 456–461 Palacios-Pacheco, X ;Villegas-Ch, W ;Luján-Mora, S: Application of data mining for the detection of variables that cause university desertion. En:Communications in Computer and Information Science895 (2019), p. 510–520. – ISBN9783030055318 Paura, L ;Arhipova, I: Student dropout rate in engineering education study pro-gram. En:Engineering for Rural DevelopmentVol. 2016-Janua, 2016, p. 641–646 Peguero, A A. ;Hong, J S.: Are violence and disorder at school placing adolescents within immigrant families at higher risk of dropping out? En:Journal of SchoolViolence18 (2019), Nr. 2, p. 241–258 Peralta, B ;Poblete, T ;Caro, L: Automatic feature selection for desertion and graduation prediction: A chilean case. En:Proceedings - International Conference of the Chilean Computer Science Society, SCCC, 2017 Pérez, A ;Grand ́on, E E. ;Caniupán, M ;Vargas, G: Comparative Analysis ofPrediction Techniques to Determine Student Dropout: Logistic Regression vs DecisionTrees. En:Proceedings - International Conference of the Chilean Computer ScienceSociety, SCCCVol. 2018-Novem, 2018 Perez, B ;Castellanos, C ;Correal, D: Applying Data Mining Techniques toPredict Student Dropout: A Case Study. En:2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, 2018 Pradeep, A ;Das, S ;Kizhekkethottam, J J.: Students dropout factor prediction using EDM techniques. En:Proceedings of the IEEE International Conference onSoft-Computing and Network Security, ICSNS 2015, 2015 Rana, S ;Gupta, S K. ;Venkatesh, S: Differentially private random forest with high utility. En:Proceedings - IEEE International Conference on Data Mining, ICDMVol. 2016-Janua, 2016, p. 955–960 Rea, Louis M. ;Parker, Richard A.:Designing and conducting survey research: A comprehensive guide. John Wiley & Sons, 2014 Reason, Robert D.: Student variables that predict retention: Recent research and new developments. En:NASPA journal46 (2009), Nr. 3, p. 482–501 Restrepo, E G Y. ;Ferreira, F ;Boticario, J G. ;Marcelino-Jesus, E ;Sarraipa, J ;Jardim-Goncalves, R: Enhanced affective factors management forHEI students dropout prevention. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)9753 (2016), p. 675–684 Rodriguez Maya, N E. ;Jimenez Alfaro, A J. ;Reyes Hernandez, L A. ;Suarez Carranza, B A. ;Ruiz Garduno, J K.: Data mining: a scholar dropout predictive model. En:2017 IEEE Mexican Humanitarian Technology Conference(MHTC), 2017, p. 89–93 Rodríguez-Mu ̃niz, L J. ;Bernardo, A B. ;Esteban, M ;Díaz, I: Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques? En:PLoS ONE14 (2019), Nr. 6 Rokach, Lior ;Maimon, Oded:Data mining with decision trees: theory and applications. Vol. 81. World scientific, 2014 Rovira, S ;Puertas, E ;Igual, L: Data-driven system to predict academic gradesand dropout. En:PLoS ONE12 (2017), Nr. 2 Sajjadi, S ;Shapiro, B ;Mckinlay, C ;Sarkisyan, A ;Shubin, C ;Osoba, E:Finding bottlenecks: Predicting student attrition with unsupervised classifier. En:2017Intelligent Systems Conference, IntelliSys 2017Vol. 2018-Janua, 2018, p. 1166–1172 Salazar-Fernandez, J P. ;Sepúlveda, M ;Munoz-Gama, J: Influence of student diversity on educational trajectories in engineering high-failure rate courses that lead to late dropout. En:IEEE Global Engineering Education Conference, EDUCONVol.April-2019, 2019, p. 607–616 Sangodiah, A ;Beleya, P ;Muniandy, M ;Heng, L E. ;Ramendran Spr, C:Minimizing student attrition in higher learning institutions in Malaysia using support vector machine. En:Journal of Theoretical and Applied Information Technology71(2015), Nr. 3, p. 377–385 Sansone, D: Beyond Early Warning Indicators: High School Dropout and MachineLearning. En:Oxford Bulletin of Economics and Statistics81 (2019), Nr. 2, p. 456–485 Sari, E Y. ;Kusrini;Sunyoto, A: Optimization of weight backpropagation with particle swarm optimization for student dropout prediction. En:2019 4th InternationalConference on Information Technology, Information Systems and Electrical Enginee-ing, ICITISEE 2019, 2019, p. 423–428 Selvan, M P. ;Navadurga, N ;Prasanna, N L.: An efficient model for predicting student dropout using data mining and machine learning techniques. En:InternationalJournal of Innovative Technology and Exploring Engineering8 (2019), Nr. 9 SpecialIssue 2, p. 750–752 Serra, A ;Perchinunno, P ;Bilancia, M: Predicting student dropouts in higher education using supervised classification algorithms. En:Lecture Notes in ComputerScience (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10962 LNCS (2018), p. 18–33 Shiratori, N: Modeling Dropout Behavior Patterns Using Bayesian Networks inSmall-Scale Private University. En:Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, 2017, p. 170–173 Shiratori, N: Derivation of Student Patterns in a Preliminary Dropout State andIdentification of Measures for Reducing Student Dropouts. En:Proceedings - 20187th International Congress on Advanced Applied Informatics, IIAI-AAI 2018, 2018, p.497–500 Silva, J ;Castro Sarmiento, A ;Mar ́ıa Santo domingo, N ;M ́arquez Blanco, N ;Cadavid Basto, W ;Hernández P, H ;Navarro Beltr ́an, J ;de laHoz Hernández, J ;Romero, L: Data mining to identify risk factors associated with university students dropout. En:Communications in Computer and InformationScience1071 (2019), p. 44–52 Simon, D ;Fonseca, D ;Necchi, S ;Vanesa-Sánchez, M ;Campanyá, C: Architecture and Building Engineering Educational Data Mining. Learning Analytics for detecting academic dropout [Miner ́ıa de datos educativos en los grados de Arquitectura y Arquitectura Técnica. Uso de anal ́ıtica de aprendizaje para la detección del abandono. En:Iberian Conference on Information Systems and Technologies, CISTIVol. 2019-June, 2019 Solis, M ;Moreira, T ;Gonzalez, R ;Fernandez, T ;Hernandez, M: Perspectives to Predict Dropout in University Students with Machine Learning. En:2018IEEE International Work Conference on Bio inspired Intelligence, IWOBI 2018 - Proceedings, 2018 Song, Yan-Yan ;Ying, L U.: Decision tree methods: applications for classification and prediction. En:Shanghai archives of psychiatry27 (2015), Nr. 2, p. 130–135 Spady, William G.: Dropouts from higher education: An interdisciplinary review and synthesis. En:Interchange1 (1970), Nr. 1, p. 64–85. – ISSN 1573–1790 Sultana, Jabeen ;Usha Rani, M. ;Farquad, M. A.: Student’s performance pre-diction using deep learning and data mining methods. En:International Journal ofRecent Technology and Engineering8 (2019), Nr. 1 Special Issue 4, p. 1018–1021. –ISSN 22773878 Sultana, S ;Khan, S ;Abbas, M A.: Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. En:International Journal of Electrical Engineering Education54 (2017),Nr. 2, p. 105–118 Timaran Pereira, R ;Caicedo Zambrano, J: Application of decision trees for detection of student dropout profiles. En:Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017Vol. 2017-Decem, 2017,p. 528–531 Timbal, M A.: Analysis of Student-at-Risk of Dropping out (SARDO) Using deci-sion tree: An Intelligent predictive model for reduction. En:International Journal ofMachine Learning and Computing9 (2019), Nr. 3, p. 273–278 Tinto, Vincent: Dropout from higher education: A theoretical synthesis of recent research. En:Review of educational research45 (1975), Nr. 1, p. 89–125 Tinto, Vincent:Leaving college: Rethinking the causes and cures of student attrition.Chicago : University of Chicago Press, 5801 S. Ellis Avenue, Chicago, IL 60637, 1987.– 246 p.. – ISBN 0–226–80446–1 Tinto, Vincent ;Cullen, John: Dropout in Higher Education: A Review and Theoretical Synthesis of Recent Research. (1973) Tomczak, Maciej ;Tomczak, Ewa: The need to report effect size estimates revisited.An overview of some recommended measures of effect size. En:Trends in sport sciences1 (2014), Nr. 21, p. 19–25 Uribe Mallarino, Consuelo ;Ramirez Moreno, Jaime: Clase media y movilidad social en Colombia. En:Revista colombiana de sociología42 (2019), Nr. 2, p. 10 Urrutia Montoya, Miguel: La educación como factor de movilidad social. En:Cuadernos de Economía12 (1975), Nr. 37, p. 21–32 Valveny Ernest, González Sabaté Jordi, Baldrich Caselles R.Clasificación de imágenes: cómo reconocer el contenido de una imagen. 2010 Vásquez, Jonathan ;Miranda, Jaime: Student Desertion: What Is and How CanIt Be Detected on Time? En:Data Science and Digital Business. Springer, 2019, p.263–283 Velásquez, Leonardo ;Hitpass, Bernhard: El nivel de Actividad en el ProcesoEducativo como Indicador de Riesgo de Deserción Estudiantil medido en tiempo real con apoyo de tecnología BAM, 2014 Vila, D ;Cisneros, S ;Granda, P ;Ortega, C ;Posso-Yépez, M ;García-Santillán, I: Detection of desertion patterns in university students using data mining techniques: A case study. En:Communications in Computer and Information Science895 (2019), p. 420–429. – ISBN 9783030055318 Viloria, A ;García Guliany, J ;Niebles N ́uz, W ;Hernández Palma, H ;Niebles Núz, L: Data Mining Applied in School Dropout Prediction. En:Journal ofPhysics: Conference SeriesVol. 1432, 2020 Viloria, A ;Lezama, O B P.: Mixture structural equation models for classifying university student dropout in Latin america. En:Procedia Computer ScienceVol. 160,2019, p. 629–634 Viloria, A ;Padilla, J G. ;Vargas-Mercado, C ;Hernández-Palma, H ;Llinas, N O. ;David, M A.: Integration of data technology for analyzing university dropout. En: Procedia Computer ScienceVol. 155, 2019, p. 569–574 Wan Yaacob, W F. ;Mohd Sobri, N ;Nasir, S A M. ;Wan Yaacob, W F.;Norshahidi, N D. ;Wan Husin, W Z.: Predicting Student Drop-Out in HigherInstitution Using Data Mining Techniques. En:Journal of Physics: Conference SeriesVol. 1496, 2020 Wardono;Mariani, S ;Afifa, N N.: Application of decomposition methods for forecasting the percentage of students who drop out of school to predict the amount of scholarship needed in central java indonesia. En:Journal of Theoretical and AppliedInformation Technology98 (2020), Nr. 2, p. 327–337 Witten, Ian H. ;Frank, Eibe ;Hall, Mark A.:Data Mining: Practical machine learning tools and techniques. 3. Morgan Kaufmann, 2011. – 665 p. Xu, L. ;Krzyzak, A. ;Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. En:IEEE Transactions on Systems,Man, and Cybernetics22 (1992), Nr. 3, p. 418–435 Zea, L D F. ;Reina, Y F P. ;Molano, J I R.: Machine Learning for the Identification of Students at Risk of Academic Desertion. En:Communications in Computer andInformation Science1011 (2019), p. 462–473. – ISBN 9783030207977 Zhao, Z. ;Anand, R. ;Wang, M.: Maximum Relevance and Minimum RedundancyFeature Selection Methods for a Marketing Machine Learning Platform. (2019), p.442–452 S ̧ara, N.-B. ;Halland, R ;Igel, C ;Alstrup, S: High-school dropout prediction using machine learning: A Danish large-scale study. En:23rd European Symposium onArtificial Neural
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Espinosa Bedoya, Albeiro749aa8775c497b18160b8a0a5d502335Jiménez Builes, Jovani Albertofb896955cb168e6c8b283aeda5447192Zapata Medina, Daniel7e748db0d676c7df9e22a1b111e12b33GIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial2021-10-26T15:06:45Z2021-10-26T15:06:45Z2021-10-22https://repositorio.unal.edu.co/handle/unal/80615Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLa deserción escolar es una problemática social de alto impacto para el sistema educativo, dificultando la movilidad social y la construcción de la equidad en un país. En la última década, con el auge de los datos estudiantiles, las investigaciones de este fenómeno en la educación ha sido abordado desde la minería de datos educativos y una de las principales problemáticas es su detección temprana, sobre todo en países emergentes y subdesarrollados donde el abandono escolar es cada vez más frecuente. Las instituciones educativas requieren detectar oportunamente el riesgo de deserción de estudiantes y así apoyar al estudiantado en su permanencia dentro del sistema educativo. Lo anterior, propició una revisión sistemática de literatura en el área computacional, demostrando que en los últimos cinco años se han desarrollado varios métodos para la detección temprana del riesgo de deserción escolar, y a su vez ha generado nuevos desafíos en la identificación de los factores mayormente influyentes, el planteamiento de nuevos métodos eficientes e interpretables que puedan ser implementados y la necesidad de representación y selección adecuada de características. Además, con este tipo de datos, es necesario una profunda tarea de preprocesamiento debido a la heterogeneidad de las variables. Sin embargo, no se trata sólo de encontrar las causas de la deserción, sino también reunir otras características que permitan generar estrategias para persuadir al estudiantado en su interés y decisión de permanecer en el sistema educativo. El propósito de esta tesis fue desarrollar un método basado en métricas para transformar las características iniciales, aportando al preprocesamiento y entendimiento profundo de los datos (análisis estadístico), de esta forma, apoyar una selección y representación óptima y adecuada de características, para luego llevarlas como entradas de clasificadores expertos en un tipo específico de características. Seguidamente, se utilizó la fusión a nivel de clasificador para obtener una respuesta más generalizada, ya que distintos clasificadores se equivocarán en muestras diferentes. Con esto, mejorar el rendimiento del clasificador y fácil interpretación de los resultados de los algoritmos de aprendizaje automático. La validación en términos de precisión, sensibilidad e interpretabilidad del método propuesto en la presente tesis se realizó en comparación con una técnica de minería de datos y las características iniciales, lo que permitió comprobar la capacidad de detección de la deserción escolar utilizando la transformación de características a partir de métricas, logrando un 82% de precisión y 64% de recall, estos resultados demuestran el aumento significativo con respecto al 71% de precisión y 57% de recall alcanzado con las características iniciales sin el uso de métricas. Por lo anterior, se recomienda su potencial aplicación en la analítica de datos educativos, que permita la predicción temprana del riesgo de deserción y la generación de estrategias que posibiliten persuadir al estudiantado de permanecer en la institución educativa. (Texto tomado de la fuente)School dropout is a social problem with a high impact on the education system, hindering social mobility and the construction of equity in a country. In the last decade, with the rise of student data, research on this phenomenon in education has been approached from educational data mining and one of the main problems is its early detection, especially in emerging and underdeveloped countries where school dropout is increasingly frequent. Educational institutions need to detect students at risk of dropping out of school in a timely and thus support students in their permanence within the educational system. This led to a systematic literature review in the computational area, showing that in the last five years several methods have been developed for the early detection of dropout risk, and in turn has generated new challenges in the identification of the most influential factors, the approach of new efficient and interpretable methods that can be implemented and the need for adequate representation and selection of fatures. In addition, with this type of data, a deep preprocessing task is necessary due to the heterogeneity of the variables. However, it is not only a matter of finding the causes of dropout, but also of gathering other features that allow us to generate strategies to persuade students in their interest and decision to remain in the educational system. The purpose of this thesis was to develop a metric-based method for transform the initial features, contributing to the preprocessing and deep understanding of the data (statistical analysis), thus supporting an optimal and adequate selection and representation of features, and then taking them as inputs to expert classifiers on a specific type of features. Next, classifier-level fusion was used to obtain a more generalized answer, since different classifiers will be wrong on different samples. With this, improving the classifier performance and easy interpretation of the results of the machine learning algorithms. The validation in terms of accuracy, sensitivity and interpretability of the method proposed in this thesis was performed in comparison with a data mining technique and the initial features, which allowed testing the ability to detect school dropout using the reworking of features from metrics, achieving 82% precision and 64% recall, these results demonstrate the significant increase with respect to 71% precision and 57% recall achieved with the initial features without the use of metrics. Therefore, its potential application in educational data analytics is recommended to allow early prediction of the risk of dropout and the generation of strategies that make it possible to persuade students to remain in the educational institution.MaestríaMagíster en Ingeniería - Ingeniería de SistemasInteligencia artificialÁrea Curricular de Ingeniería de Sistemas e Informáticaxviii, 105 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación370 - Educación::379 - Asuntos de política pública en educaciónMinería de datosData miningDropoutsDeserción escolarPreprocesamiento de datos educativosMétricasTécnicas de minería de datosFusión a nivel de clasificadorSchool dropoutEducational data preprocessingMetricsData mining techniquesLate fusionMétodo para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datosMetric-driven and a data mining technique method to support detection of students at risk of school dropoutTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaAcero, A ;Achury, J C. ;Morales, J C.: University dropout: A prediction model for an engineering program in bogota, Colombia. En:B., Kloot (Ed.):Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections, Research in Engineering Education Network, 2019. – ISBN 9780799226003, p.483–490Agrusti, F ;Mezzini, M ;Bonavolontá, G: Deep learning approach for predicting university dropout: A case study at roma Tre university. En:Journal of E-Learning and Knowledge Society16 (2020), Nr. 1, p. 44–54Aguilar-Gonzalez, S ;Palafox, L: Prediction of Student Attrition Using MachineLearning. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11835 LNAI (2019), p.212–222Ahmad Tarmizi, S S. ;Mutalib, S ;Abdul Hamid, N H. ;Abdul-Rahman, S ;Md Ab Malik, A: A Case Study on Student Attrition Prediction in Higher EducationUsing Data Mining Techniques. En:Communications in Computer and InformationScience1100 (2019), p. 181–192Ahmed, S A. ;Khan, S I.: A machine learning approach to Predict the Engineering Students at risk of dropout and factors behind: Bangladesh Perspective. En:2019 10th International Conference on Computing, Communication and NetworkingTechnologies, ICCCNT 2019, 2019Ajit, Kumar J. ;C., K. J.: Dropout Classification through Discriminant FunctionAnalysis: A Statistical Approach. En:International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN :2456-33072 (2017), Nr. 4, p. 572–577Alban, M ;Mauricio, D: Neural networks to predict dropout at the universities.En:International Journal of Machine Learning and Computing9 (2019), Nr. 2, p.149–153. – ISSN 20103700Alban, Mayra ;David, Mauricio: Factors to predict dropout at the universities:A case of study in Ecuador. En:IEEE Global Engineering Education Conference,EDUCON2018-April (2018), p. 1238–1242. – ISBN 9781538629574Alban Taipe, M S. ;Mauricio Sánchez, D: Prediction of university dropout through technological factors: A case study in Ecuador. En:Espacios39 (2018), Nr.52Alejandro Gonzalez-Campos, Jose ;Manuel Carvajal-Muquillaza, Cristian;Elias Aspee-Chacon, Juan: Modeling of university dropout using Markov chains.En:UNICIENCIA34 (2020), Nr. 1, p. 129–146. – ISSN 1011–0275Alyahyan, E ;D ̈us ̧teg ̈or, D: Predicting academic success in higher education: literature review and best practices. En:International Journal of Educational Technology in Higher Education17 (2020), Nr. 1. – ISSN 23659440Ameri, S ;Fard, M J. ;Chinnam, R B. ;Reddy, C K.: Survival analysis based framework for early prediction of student dropouts. En:International Conference onInformation and Knowledge Management, ProceedingsVol. 24-28-Octo, 2016, p. 903–912Astin, Alexander: What matters in college: four critical years revisited. En:LiberalEducation4 (1993), p. 4Barros, T M. ;Silva, I ;Guedes, L A.: Determination of dropout student profilebased on correspondence analysis technique. En:IEEE Latin America Transactions17 (2019), Nr. 9, p. 1517–1523Bean, John P.: Dropouts and turnover: The synthesis and test of a causal model of student attrition. En:Research in Higher Education12 (1980), Nr. 2, p. 155–187. –ISSN 1573–188XBean, John P. ;Metzner, Barbara S.: A Conceptual Model of Nontraditional Undergraduate Student Attrition. En:Review of Educational Research55 (1985), Nr. 4,p. 485–540Bedregal-Alpaca, N ;Aruquipa-Velazco, D ;Cornejo-Aparicio, V: Data mining techniques to extract academic behavior profiles and predict university desertion[Técnicas de data mining para extraer perfiles comportamiento académico y predecirla deserción universitaria]. En:RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2020 (2020), Nr. E27, p. 592–604Bedregal-Alpaca, N ;Cornejo-Aparicio, V ;Zarate-Valderrama, J ;Yanque-Churo, P: Classification models for determining types of academic risk and predicting dropout in university students. En:International Journal of AdvancedComputer Science and Applications11 (2020), Nr. 1, p. 266–272Beemer, Joshua ;Spoon, Kelly ;He, Lingjun ;Fan, Juan juan ;Levine, Richard A.:Ensemble learning for estimating individualized treatment effects in student success studies. En:International Journal of Artificial Intelligence in Education28 (2018),Nr. 3, p. 315–335Blaser, R ;Fryzlewicz, P: Random rotation ensembles. En:Journal of MachineLearning Research17 (2016)Breiman, Leo: Random forests. En:Machine learning45 (2001), Nr. 1, p. 5–32Brown, I ;Mues, C: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. En:Expert Systems with Applications39 (2012),Nr. 3, p. 3446–3453Cabus, S J. ;De Witte, K: The effectiveness of active school attendance interventions to tackle dropout in secondary schools: a Dutch pilot case. En:EmpiricalEconomics49 (2015), Nr. 1, p. 65–80Cabus, S J. ;De Witte, K: Why Do Students Leave Education Early? Theory andEvidence on High School Dropout Rates. En:Journal of Forecasting35 (2016), Nr. 8,p. 690–702Cano, Alberto ;Zafra, Amelia ;Ventura, Sebastián: An interpretable classification rule mining algorithm. En:Information Sciences240 (2013), p. 1–20Castellanos, M C R. ;Alvarado, L D N. ;Villamil, J E P.: University student desertion analysis using agent-based modeling approach. En: COMPLEXIS 2018 -Proceedings of the 3rd International Conference on Complexity, Future InformationSystems and RiskVol. 2018-March, 2018, p. 128–135Castillo-Sanchez, Mario ;Gamboa-Araya, Ronny ;Hidalgo-Mora, Randall:Factors that influence student dropout and failing grades in a university mathematics course. En: UNICIENCIA 34 (2020), Nr. 1, p. 219–245. – ISSN 1011–0275Castro R., L F. ;Espitia P., E ;Montilla, A F.: Applying CRISP-DM in a KDD process for the analysis of student attrition. En:Communications in Computer andInformation Science885 (2018), p. 386–401[29]Chai, K E K. ;Gibson, D: Predicting the risk of attrition for undergraduate students with time based modelling. En:Proceedings of the 12th International Conference onCognition and Exploratory Learning in the Digital Age, CELDA 2015, 2015, p. 109–116Chung, Jae Y. ;Lee, Sunbok: Dropout early warning systems for high school students using machine learning. En:Children and Youth Services Review96 (2019), Nr.November 2018, p. 346–353. – ISSN 01907409Costa, E B. ;Fonseca, B ;Santana, M A. ;de Ara ́ujo, F F. ;Rego, J: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. En:Computers in HumanBehavior73 (2017), p. 247–256Cover, T. M.: The Best Two Independent Measurements Are Not the Two Best.En:IEEE Transactions on Systems, Man, and CyberneticsSMC-4 (1974), Nr. 1, p.116–117Cuji Chacha, B R. ;Gavilanes López, W L. ;Vicente Guerrero, V X. ;Villacis Villacis, W G.: Student Dropout Model Based on Logistic Regression.En:Communications in Computer and Information Science1194 CCIS (2020), p.321–333da Cunha, J A. ;Moura, E ;Analide, C: Data mining in academic databases to detect behaviors of students related to school dropout and disapproval. En: Advances in Intelligent Systems and Computing445 (2016), p. 189–198Da Fonseca Silveira, R ;Holanda, M ;De Carvalho Victorino, M ;Ladeira, M: Educational data mining: Analysis of drop out of engineering majors at the UnB - Brazil. En:Proceedings - 18th IEEE International Conference on MachineLearning and Applications, ICMLA 2019, 2019, p. 259–262Da Silva, Paulo M. ;Lima, Marilia N. ;Soares, Wedson L. ;Silva, Iago R. ;De Fagundes, Roberta A. ;De Souza, Fernado F.: Ensemble regression models applied to dropout in higher education. En:Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019(2019), p. 120–125. ISBN 9781728142531De Santos, K J O. ;Menezes, A G. ;De Carvalho, A B. ;Montesco, C A E.:Supervised learning in the context of educational data mining to avoid university students dropout. En:Proceedings - IEEE 19th International Conference on AdvancedLearning Technologies, ICALT 2019, 2019, p. 207–208Delen, D ;Topuz, K ;Eryarsoy, E: Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition. En:EuropeanJournal of Operational Research281 (2020), Nr. 3, p. 575–587. – ISSN 03772217Departamento Administrativo Nacional de Estadística (DANE).Boletín Técnico de la Investigación de Educación Formal 2018. 2019Dharmawan, T ;Ginardi, H ;Munif, A: Dropout Detection Using Non-AcademicData. En:Proceedings - 2018 4th International Conference on Science and Technology,ICST 2018, 2018Ding, C. ;Peng, H.Minimum redundancy feature selection from microarray gene expression data. 2003Durkheim E. (1951):Suicide: A study in sociology. (J.A. Spaulding G. Simpson,Trans.)Glencoe IL Free Press., 1897Dwork, C ;Roth, A: The algorithmic foundations of differential privacy. En:Foundations and Trends in Theoretical Computer Science9 (2013), Nr. 3-4, p. 211–487Eckert, K B. ;Suénaga, R: Analysis of attrition-retention of college students using classification technique in data mining [Análisis de deserción-permanencia de estudiantes universitarios utilizando técnica de clasificación minería de datos]. En: Formación Universitaria8 (2015), Nr. 5, p. 3–12Elbir, A ;G ̈und ̈uz, E ;Diri, B: Estimating the School Dropout Trend by Using DataMining Methods [Veri Madenciliˇgi Y ̈ontemleri Kullanarak Okul Birakma EˇgilimininTahmin Edilmesi]. En:Proceedings - 2018 Innovations in Intelligent Systems andApplications Conference, ASYU 2018, 2018Fenton, Norman ;Bieman, James:Software metrics: a rigorous and practical approach. 3. CRC press, 2014. – 618 p.Fernández, J ;Rojas, A ;Daza, G ;Gómez, D ; ́Alvarez, A ;Orozco, ́A: Student desertion prediction using kernel relevance analysis. En:Lecture Notes in ComputerScience (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11047 LNCS (2018), p. 263–270Fonseca, M T. ;Gazo, P F.: Longitudinal study of the dropout and reentry process in students of social sciences: The case of Business Administration and Management[Estudio longitudinal del proceso de abandono y reingreso de estudiantes de CienciasSociales. El caso de Administraci. En:Educar55 (2019), Nr. 2, p. 401–417Gamao, A O. ;Gerardo, B D.: Prediction-based model for student dropouts using modified mutated firefly algorithm. En:International Journal of Advanced Trends inComputer Science and Engineering8 (2019), Nr. 6, p. 3461–3469[Gaviria, Alejandro: Los que suben y los que bajan. En: Educación y Movilidad Social en Colombia. Bogotá: Ediciones Alfa omega y Fedesarrollo(2002)Gu, Q ;Cai, Z ;Zhu, L ;Huang, B: Data Mining on Imbalanced Data Sets. En:2008 International Conference on Advanced Computer Theory and Engineering, 2008.– ISSN 2154–7505, p. 1020–1024Guyon, Isabelle ;Elisseeff, André: An Introduction to Variable and Feature Selection. En:J. Mach. Learn. Res.3 (2003), mar, Nr. null, p. 1157–1182. – ISSN1532–4435Hackeling, Gavin:Mastering Machine Learning with scikit-learn. Packt PublishingLtd, 2014Han, Jiawei ;Kamber, Micheline ;Pei, Jian:Data mining concepts and techniques third edition. 2011. – 83–124 p.Hanchuan Peng;Fuhui Long;Ding, C.: Feature selection based on mutual in-formation criteria of max-dependency, max-relevance, and min-redundancy. En:IEEETransactions on Pattern Analysis and Machine Intelligence27 (2005), Nr. 8, p. 1226–1238Hasan, M N.: A Comparison of Logistic Regression and Linear Discriminant Analysis in Predicting of Female Students Attrition from School in Bangladesh. En:2019 4th International Conference on Electrical Information and Communication Technology,EICT 2019, 2019Hegde, V: Dimensionality reduction technique for developing undergraduate student dropout model using principal component analysis through R package. En:2016 IEEEInternational Conference on Computational Intelligence and Computing Research, IC-CIC 2016, 2017Hegde, V ;Prageeth, P P.: Higher education student dropout prediction and analysis through educational data mining. En:Proceedings of the 2nd InternationalConference on Inventive Systems and Control, ICISC 2018, 2018, p. 694–699Heredia, D ;Amaya, Y ;Barrientos, E: Student Dropout Predictive Model UsingData Mining Techniques. En:IEEE Latin America Transactions13 (2015), Nr. 9, p.3127–3134 Networks, Computational Intelligence and Machine Learning, ESANN2015 - Proceedings, 2015, p. 319–324Hernández-Blanco, Antonio ;Herrera-Flores, Boris ;Tomás, David ;Navarro-Colorado, Borja: A Systematic Review of Deep Learning Approaches to Educational Data Mining. En:Complexity2019 (2019), p. 22. – ISSN 10990526Hernandez Gonzalez, A G. ;Melendez Armenta, R A. ;Morales Rosa-les, L A. ;Garcia Barrientos, A ;Tecpanecatl Xihuitl, J L. ;Algredo,I: Comparative Study of Algorithms to Predict the Desertion in the Students at the Bibliograf ́ıa97ITSM-Mexico. En:IEEE Latin America Transactions14 (2016), Nr. 11, p. 4573–4578.– ISSN 15480992Hernández-Leal, E J. ;Quintero-Lorza, D P. ;Escobar-Naranjo, J C. ;Ramírez- Gómez, J S. ;Duque-Méndez, N D.: Educational data mining for the analysis of student desertion. En:CEUR Workshop ProceedingsVol. 2231, 2018Hillmert, S ;Groß, M ;Schmidt-Hertha, B ;Weber, H:Informational environments and college student dropout. 2017. – 27–52 p.Hori, G: Identifying Factors Contributing to University Dropout with Sparse LogisticRegression. En:Proceedings - 2018 7th International Congress on Advanced AppliedInformatics, IIAI-AAI 2018, 2018, p. 430–433Hutagaol, N ;Suharjito: Predictive modelling of student dropout using ensemble classifier method in higher education. En:Advances in Science, Technology andEngineering Systems4 (2019), Nr. 4, p. 206–211Iam-On, N ;Boongoen, T: Generating descriptive model for student dropout: a re-view of clustering approach. En:Human-centric Computing and Information Sciences7 (2017), Nr. 1Iam-On, N ;Boongoen, T: Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. En:International Journal of MachineLearning and Cybernetics8 (2017), Nr. 2, p. 497–510James, John T. ;Tichy, Karen L. ;Collins, Alan ;Schwob, John: Developing a predictive metric to assess school viability. En:Journal of Catholic Education11(2008), Nr. 4, p. 5Jiménez, Fernando ;Paoletti, Alessia ;Sánchez, Gracia ;Sciavicco, Guido: Predicting the Risk of Academic Dropout with Temporal Multi-Objective Optimization.En:IEEE Transactions on Learning Technologies12 (2019), Nr. 2, p. 225–236. – ISSN19391382King, B.M. ;Minium, E.W.:Statistical Reasoning in Psychology and Education.Wiley, 2003. – ISBN 9780471211877Kiss, B ;Nagy, M ;Molontay, R ;Csabay, B: Predicting dropout using high school and first-semester academic achievement measures. En:ICETA 2019 - 17th IEEE International Conference on Emerging eLearning Technologies and Applications,Proceedings, 2019, p. 383–389Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. 2007.Kori, K ;Pedaste, M ;T ̃onisson, E ;Palts, T ;Altin, H ;Rantsus, R ;Sell, R;Murtazin, K ;R ̈u ̈utmann, T: First-year dropout in ICT studies. En:IEEE GlobalEngineering Education Conference, EDUCONVol. 2015-April, 2015, p. 437–445Kuhn, Max ;Johnson, Kjell: Feature engineering and selection: A practical approach for predictive models. CRC Press, 2019Kumar, Mukesh ;Singh, Arjun J. ;Handa, Disha: Literature Survey on EducationalDropout Prediction, 2017Kuncheva, Ludmila I.:Combining Pattern Classifiers: Methods and Algorithms, Second Edition.c©2014 John Wiley Sons, Inc., Published 2014Kuo, J Y. ;Pan, C W. ;Lei, B: Using Stacked Denoising Autoencoder for theStudent Dropout Prediction. En:Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017Vol. 2017-Janua, 2017, p. 483–488Lacher, F ;Staudacher, A P.: Reducing dropouts from Higher Education Institutions through Lean Six Sigma: An exploratory study. En:Proceedings of the SummerSchool Francesco TurcoVol. 13-15-Sept, 2016, p. 59–64Lima, J ;Alves, P ;Pereira, M ;Almeida, S: Using academic analytics to predict dropout risk in engineering courses. En:Proceedings of the European Conference one-Learning, ECELVol. 2018-Novem, 2018, p. 316–321Limsathitwong, K ;Tiwatthanont, K ;Yatsungnoen, T: Dropout prediction system to reduce discontinue study rate of information technology students. En:Proceedings of 2018 5th International Conference on Business and Industrial Research:Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018, 2018, p. 110–114Lix, Lisa M. ;Keselman, Joanne C. ;Keselman, H J.: Consequences of AssumptionViolations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance F Test. En:Review of Educational Research66 (1996), Nr. 4, p. 579–619López G, Camilo E. ;Guzmán, Elizabeth L. ;González, Fabio A.: Data mining model to predict academic performance at the Universidad Nacional de Colombia.(2013), p. 86Maheshwari, E ;Roy, C ;Pandey, M ;Rautray, S S.: Prediction of FactorsAssociated with the Dropout Rates of Primary to High School Students in India UsingData Mining Tools. En:Advances in Intelligent Systems and Computing1013 (2020),p. 242–251Maldonado, S ;Miranda, J ;Olaya, D ;V ́asquez, J ;Verbeke, W: Redefining profit metrics for boosting student retention in higher education. En:Decision SupportSystems143 (2021)Maningba Augustine, L ;Jeyaseelan, M ;Stephen, A: Factors associated with school dropout: A sociological study among the Maram Naga primitive Tribe, Manipur.En:International Journal of Scientific and Technology Research9 (2020), Nr. 1, p.2215–2219. – ISSN 22778616Manning, Christopher D. ;Raghavan, Prabhakar ;Sch ̈utze, Hinrich: Introduction to Information Retrieval. USA : Cambridge University Press, 2008. – ISBN 0521865719Márquez-Vera, C ;Cano, A ;Romero, C ;Noaman, A Y M. ;Mousa Fardoun,H ;Ventura, S: Early dropout prediction using data mining: A case study with high school students. En:Expert Systems33 (2016), Nr. 1, p. 107–124Márquez-Vera, Carlos ;Romero Morales, Cristóbal ;Ventura Soto, Sebastián: Predicting school failure and dropout by using data mining techniques. En:Revista Iberoamericana de Tecnologías del Aprendizaje8 (2013), Nr. 1, p. 7–14. –ISSN 19328540Martelo, R J. ;Jiménez-Pitre, I ;Villabona-Gómez, N: Determination of reasons for desertion of undergraduate students through the brainstorming and MIC-MAC techniques [Determinación de factores para deserción de estudiantes en pregrado a través de las técnicas lluvia de ideas y MICMAC]. En:Espacios38 (2017), Nr. 20Martins, L C B. ;Carvalho, R N. ;Carvalho, R S. ;Victorino, M C. ;Holan-da, M: Early prediction of college attrition using data mining. En:Chen X. Luo B.,Luo F Palade V Wani M A. (Ed.):Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017Vol. 2017-Decem, Institute ofElectrical and Electronics Engineers Inc., 2017. – ISBN 9781538614174, p. 1075–1078Martins, M P G. ;Migueis, V L. ;Fonseca, D S B. ;Gouveia, P D F.: Prediction of academic dropout in a higher education institution using data mining [Previs ̃ao do abandono académico numa institui ̧c ̃ao de ensino superior com recurso a data mining].En:RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2020 (2020), Nr.E28, p. 188–203Meedech, Phanupong ;Iam-On, Natthakan ;Boongoen, Tossapon: Prediction ofStudent Dropout Using Personal Profile and Data Mining Approach. En:Lavangna-nanda, Kittichai (Ed.) ;Phon-Amnuaisuk, Somnuk (Ed.) ;Engchuan, Worrawat(Ed.) ;Chan, Jonathan H. (Ed.):Intelligent and Evolutionary Systems. Cham : Springer International Publishing, 2016. – ISBN 978–3–319–27000–5, p. 143–155Ministerio de Educación Nacional de Colombia MEN.Sistema Nacional deIndicadores Educativos para los Niveles de Preescolar, Básica y Media en Colombia.2014Ministerio de Educación Nacional de Colombia MEN: Plan Estratégico Institucional 2019-2022 Educación de calidad para un futuro con oportunidades para todos Versión 1.0. 2019. – Informe de Investigación. – 41 p.Ministerio de Hacienda y Crédito Público: Presupuesto General de la Nación2020- Decreto No. 2411 de Diciembre 30 de 2019. 2019. – Informe de Investigación. –7 p.Murakami, K ;Takamatsu, K ;Kozaki, Y ;Kishida, A ;Kenya, B ;Noda, I ;Jyunichiro, A ;Takao, K ;Mitsunari, K ;Nakamura, T ;Nakata, Y: Predicting the Probability of Student Dropout through EMIR Using Data from Current andGraduate Students. En:Proceedings - 2018 7th International Congress on AdvancedApplied Informatics, IIAI-AAI 2018, 2018, p. 478–481Mutrofin, S ;Ginardi, R V H. ;Fatichah, C ;Kurniawardhani, A: A critical assessment of balanced class distribution problems: The case of predict student dropout. En:Test Engineering and Management81 (2019), Nr. 11-12, p. 1764–1770Nagy, M ;Molontay, R: Predicting Dropout in Higher Education Based on Secondary School Performance. En:INES 2018 - IEEE 22nd International Conference onIntelligent Engineering Systems, Proceedings, 2018, p. 389–394do Nascimento, R L S. ;das Neves Junior, R B. ;de Almeida Neto, M A. ;deAraújo Fagundes, R A.: Educational data mining: An application of regressors in predicting school dropout. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10935LNAI (2018), p. 246–257Navarrete, D M. ;Quina, L M. ;Pedraza, L F. ;Gomez, E Z.: Proposal to diagnose the factors that affect academic desertion of the politécnico empresarial colombiano through the lean six sigma methodology. En:Proceedings - 2019 7th InternationalEngineering, Sciences and Technology Conference, IESTEC 2019, 2019, p. 300–305Neves, J ;Figueiredo, M ;Vicente, L ;Vicente, H: A case based reasoning view of school dropout screening. En:Lecture Notes in Electrical Engineering376 (2016),p. 953–964Nuankaew, P: Dropout situation of business computer students, University of Pha-yao. En:International Journal of Emerging Technologies in Learning14 (2019), Nr.19, p. 117–131Nuankaew, P ;Nuankaew, W ;Phanniphong, K ;Fooprateepsiri, R ;Bussaman, S: Analysis dropout situation of business computer students at University of Phayao. En:Advances in Intelligent Systems and Computing1134 AISC (2020), p.419–432Oneto, L ;Siri, A ;Luria, G ;Anguita, D: Dropout prediction at university of genoa: A privacy preserving data driven approach. En:ESANN 2017 - Proceedings,25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2017, p. 135–140Organización para la Cooperación y el Desarrollo Económicos (OC-DE);Brunner, Jos ́e J. (Ed.) ;Gomes, Candido A. (Ed.) ;Fordham, Elizabeth(Ed.) ;Phair, Rowena (Ed.) ;Pons, Anna (Ed.) ;Zapata, Juliana (Ed.):Revision de politicas nacionales de educacion: La educación en Colombia 2016.c©2016 Ministerio de Educación Nacional para esta version en español, 2016. – 336 p.. – ISBN9789264250598Orong, M Y. ;Caroro, R A. ;Durias, G D. ;Cabrera, J A. ;Lonzon, H ;Ricalde, G T.: A predictive analytics approach in determining the predictors of student attrition in the higher education institutions in the Philippines. En:ACMInternational Conference Proceeding Series, 2020, p. 222–225Orong, M Y. ;Sison, A M. ;Medina, R P.: A new crossover mechanism for genetic algorithm with rank-based selection method. En:Proceedings of 2018 5th InternationalConference on Business and Industrial Research: Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018, 2018, p.83–88Orooji, M ;Chen, J: Predicting louisiana public high school dropout through imbalanced learning techniques. En:Proceedings - 18th IEEE International Conference onMachine Learning and Applications, ICMLA 2019, 2019, p. 456–461Palacios-Pacheco, X ;Villegas-Ch, W ;Luján-Mora, S: Application of data mining for the detection of variables that cause university desertion. En:Communications in Computer and Information Science895 (2019), p. 510–520. – ISBN9783030055318Paura, L ;Arhipova, I: Student dropout rate in engineering education study pro-gram. En:Engineering for Rural DevelopmentVol. 2016-Janua, 2016, p. 641–646Peguero, A A. ;Hong, J S.: Are violence and disorder at school placing adolescents within immigrant families at higher risk of dropping out? En:Journal of SchoolViolence18 (2019), Nr. 2, p. 241–258Peralta, B ;Poblete, T ;Caro, L: Automatic feature selection for desertion and graduation prediction: A chilean case. En:Proceedings - International Conference of the Chilean Computer Science Society, SCCC, 2017Pérez, A ;Grand ́on, E E. ;Caniupán, M ;Vargas, G: Comparative Analysis ofPrediction Techniques to Determine Student Dropout: Logistic Regression vs DecisionTrees. En:Proceedings - International Conference of the Chilean Computer ScienceSociety, SCCCVol. 2018-Novem, 2018Perez, B ;Castellanos, C ;Correal, D: Applying Data Mining Techniques toPredict Student Dropout: A Case Study. En:2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, 2018Pradeep, A ;Das, S ;Kizhekkethottam, J J.: Students dropout factor prediction using EDM techniques. En:Proceedings of the IEEE International Conference onSoft-Computing and Network Security, ICSNS 2015, 2015Rana, S ;Gupta, S K. ;Venkatesh, S: Differentially private random forest with high utility. En:Proceedings - IEEE International Conference on Data Mining, ICDMVol. 2016-Janua, 2016, p. 955–960Rea, Louis M. ;Parker, Richard A.:Designing and conducting survey research: A comprehensive guide. John Wiley & Sons, 2014Reason, Robert D.: Student variables that predict retention: Recent research and new developments. En:NASPA journal46 (2009), Nr. 3, p. 482–501Restrepo, E G Y. ;Ferreira, F ;Boticario, J G. ;Marcelino-Jesus, E ;Sarraipa, J ;Jardim-Goncalves, R: Enhanced affective factors management forHEI students dropout prevention. En:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)9753 (2016), p. 675–684Rodriguez Maya, N E. ;Jimenez Alfaro, A J. ;Reyes Hernandez, L A. ;Suarez Carranza, B A. ;Ruiz Garduno, J K.: Data mining: a scholar dropout predictive model. En:2017 IEEE Mexican Humanitarian Technology Conference(MHTC), 2017, p. 89–93Rodríguez-Mu ̃niz, L J. ;Bernardo, A B. ;Esteban, M ;Díaz, I: Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques? En:PLoS ONE14 (2019), Nr. 6Rokach, Lior ;Maimon, Oded:Data mining with decision trees: theory and applications. Vol. 81. World scientific, 2014Rovira, S ;Puertas, E ;Igual, L: Data-driven system to predict academic gradesand dropout. En:PLoS ONE12 (2017), Nr. 2Sajjadi, S ;Shapiro, B ;Mckinlay, C ;Sarkisyan, A ;Shubin, C ;Osoba, E:Finding bottlenecks: Predicting student attrition with unsupervised classifier. En:2017Intelligent Systems Conference, IntelliSys 2017Vol. 2018-Janua, 2018, p. 1166–1172Salazar-Fernandez, J P. ;Sepúlveda, M ;Munoz-Gama, J: Influence of student diversity on educational trajectories in engineering high-failure rate courses that lead to late dropout. En:IEEE Global Engineering Education Conference, EDUCONVol.April-2019, 2019, p. 607–616Sangodiah, A ;Beleya, P ;Muniandy, M ;Heng, L E. ;Ramendran Spr, C:Minimizing student attrition in higher learning institutions in Malaysia using support vector machine. En:Journal of Theoretical and Applied Information Technology71(2015), Nr. 3, p. 377–385Sansone, D: Beyond Early Warning Indicators: High School Dropout and MachineLearning. En:Oxford Bulletin of Economics and Statistics81 (2019), Nr. 2, p. 456–485Sari, E Y. ;Kusrini;Sunyoto, A: Optimization of weight backpropagation with particle swarm optimization for student dropout prediction. En:2019 4th InternationalConference on Information Technology, Information Systems and Electrical Enginee-ing, ICITISEE 2019, 2019, p. 423–428Selvan, M P. ;Navadurga, N ;Prasanna, N L.: An efficient model for predicting student dropout using data mining and machine learning techniques. En:InternationalJournal of Innovative Technology and Exploring Engineering8 (2019), Nr. 9 SpecialIssue 2, p. 750–752Serra, A ;Perchinunno, P ;Bilancia, M: Predicting student dropouts in higher education using supervised classification algorithms. En:Lecture Notes in ComputerScience (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10962 LNCS (2018), p. 18–33Shiratori, N: Modeling Dropout Behavior Patterns Using Bayesian Networks inSmall-Scale Private University. En:Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, 2017, p. 170–173Shiratori, N: Derivation of Student Patterns in a Preliminary Dropout State andIdentification of Measures for Reducing Student Dropouts. En:Proceedings - 20187th International Congress on Advanced Applied Informatics, IIAI-AAI 2018, 2018, p.497–500Silva, J ;Castro Sarmiento, A ;Mar ́ıa Santo domingo, N ;M ́arquez Blanco, N ;Cadavid Basto, W ;Hernández P, H ;Navarro Beltr ́an, J ;de laHoz Hernández, J ;Romero, L: Data mining to identify risk factors associated with university students dropout. En:Communications in Computer and InformationScience1071 (2019), p. 44–52Simon, D ;Fonseca, D ;Necchi, S ;Vanesa-Sánchez, M ;Campanyá, C: Architecture and Building Engineering Educational Data Mining. Learning Analytics for detecting academic dropout [Miner ́ıa de datos educativos en los grados de Arquitectura y Arquitectura Técnica. Uso de anal ́ıtica de aprendizaje para la detección del abandono. En:Iberian Conference on Information Systems and Technologies, CISTIVol. 2019-June, 2019Solis, M ;Moreira, T ;Gonzalez, R ;Fernandez, T ;Hernandez, M: Perspectives to Predict Dropout in University Students with Machine Learning. En:2018IEEE International Work Conference on Bio inspired Intelligence, IWOBI 2018 - Proceedings, 2018Song, Yan-Yan ;Ying, L U.: Decision tree methods: applications for classification and prediction. En:Shanghai archives of psychiatry27 (2015), Nr. 2, p. 130–135Spady, William G.: Dropouts from higher education: An interdisciplinary review and synthesis. En:Interchange1 (1970), Nr. 1, p. 64–85. – ISSN 1573–1790Sultana, Jabeen ;Usha Rani, M. ;Farquad, M. A.: Student’s performance pre-diction using deep learning and data mining methods. En:International Journal ofRecent Technology and Engineering8 (2019), Nr. 1 Special Issue 4, p. 1018–1021. –ISSN 22773878Sultana, S ;Khan, S ;Abbas, M A.: Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. En:International Journal of Electrical Engineering Education54 (2017),Nr. 2, p. 105–118Timaran Pereira, R ;Caicedo Zambrano, J: Application of decision trees for detection of student dropout profiles. En:Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017Vol. 2017-Decem, 2017,p. 528–531Timbal, M A.: Analysis of Student-at-Risk of Dropping out (SARDO) Using deci-sion tree: An Intelligent predictive model for reduction. En:International Journal ofMachine Learning and Computing9 (2019), Nr. 3, p. 273–278Tinto, Vincent: Dropout from higher education: A theoretical synthesis of recent research. En:Review of educational research45 (1975), Nr. 1, p. 89–125Tinto, Vincent:Leaving college: Rethinking the causes and cures of student attrition.Chicago : University of Chicago Press, 5801 S. Ellis Avenue, Chicago, IL 60637, 1987.– 246 p.. – ISBN 0–226–80446–1Tinto, Vincent ;Cullen, John: Dropout in Higher Education: A Review and Theoretical Synthesis of Recent Research. (1973)Tomczak, Maciej ;Tomczak, Ewa: The need to report effect size estimates revisited.An overview of some recommended measures of effect size. En:Trends in sport sciences1 (2014), Nr. 21, p. 19–25Uribe Mallarino, Consuelo ;Ramirez Moreno, Jaime: Clase media y movilidad social en Colombia. En:Revista colombiana de sociología42 (2019), Nr. 2, p. 10Urrutia Montoya, Miguel: La educación como factor de movilidad social. En:Cuadernos de Economía12 (1975), Nr. 37, p. 21–32Valveny Ernest, González Sabaté Jordi, Baldrich Caselles R.Clasificación de imágenes: cómo reconocer el contenido de una imagen. 2010Vásquez, Jonathan ;Miranda, Jaime: Student Desertion: What Is and How CanIt Be Detected on Time? En:Data Science and Digital Business. Springer, 2019, p.263–283Velásquez, Leonardo ;Hitpass, Bernhard: El nivel de Actividad en el ProcesoEducativo como Indicador de Riesgo de Deserción Estudiantil medido en tiempo real con apoyo de tecnología BAM, 2014Vila, D ;Cisneros, S ;Granda, P ;Ortega, C ;Posso-Yépez, M ;García-Santillán, I: Detection of desertion patterns in university students using data mining techniques: A case study. En:Communications in Computer and Information Science895 (2019), p. 420–429. – ISBN 9783030055318Viloria, A ;García Guliany, J ;Niebles N ́uz, W ;Hernández Palma, H ;Niebles Núz, L: Data Mining Applied in School Dropout Prediction. En:Journal ofPhysics: Conference SeriesVol. 1432, 2020Viloria, A ;Lezama, O B P.: Mixture structural equation models for classifying university student dropout in Latin america. En:Procedia Computer ScienceVol. 160,2019, p. 629–634Viloria, A ;Padilla, J G. ;Vargas-Mercado, C ;Hernández-Palma, H ;Llinas, N O. ;David, M A.: Integration of data technology for analyzing university dropout. En: Procedia Computer ScienceVol. 155, 2019, p. 569–574Wan Yaacob, W F. ;Mohd Sobri, N ;Nasir, S A M. ;Wan Yaacob, W F.;Norshahidi, N D. ;Wan Husin, W Z.: Predicting Student Drop-Out in HigherInstitution Using Data Mining Techniques. En:Journal of Physics: Conference SeriesVol. 1496, 2020Wardono;Mariani, S ;Afifa, N N.: Application of decomposition methods for forecasting the percentage of students who drop out of school to predict the amount of scholarship needed in central java indonesia. En:Journal of Theoretical and AppliedInformation Technology98 (2020), Nr. 2, p. 327–337Witten, Ian H. ;Frank, Eibe ;Hall, Mark A.:Data Mining: Practical machine learning tools and techniques. 3. Morgan Kaufmann, 2011. – 665 p.Xu, L. ;Krzyzak, A. ;Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. En:IEEE Transactions on Systems,Man, and Cybernetics22 (1992), Nr. 3, p. 418–435Zea, L D F. ;Reina, Y F P. ;Molano, J I R.: Machine Learning for the Identification of Students at Risk of Academic Desertion. En:Communications in Computer andInformation Science1011 (2019), p. 462–473. – ISBN 9783030207977Zhao, Z. ;Anand, R. ;Wang, M.: Maximum Relevance and Minimum RedundancyFeature Selection Methods for a Marketing Machine Learning Platform. (2019), p.442–452S ̧ara, N.-B. ;Halland, R ;Igel, C ;Alstrup, S: High-school dropout prediction using machine learning: A Danish large-scale study. En:23rd European Symposium onArtificial NeuralInvestigadoresORIGINAL1121871537.2021.pdf1121871537.2021.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf7901459https://repositorio.unal.edu.co/bitstream/unal/80615/5/1121871537.2021.pdf17eceb37d21e0b7e2164ae19644816e1MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80615/3/license.txt8153f7789df02f0a4c9e079953658ab2MD53THUMBNAIL1121871537.2021.pdf.jpg1121871537.2021.pdf.jpgGenerated Thumbnailimage/jpeg4856https://repositorio.unal.edu.co/bitstream/unal/80615/6/1121871537.2021.pdf.jpg1d83a004d5b18d5797c903fe28491141MD56unal/80615oai:repositorio.unal.edu.co:unal/806152024-08-01 23:09:36.181Repositorio Institucional Universidad Nacional de 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Método para la detección de estudiantes en riesgo de deserción, basado en un diseño de métricas y una técnica de minería de datos

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