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dc.creatorCamargo García, Aníbal José
dc.date.accessioned2020-09-08T23:29:10Z
dc.date.available2020-09-08T23:29:10Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7077
dc.descriptionMaestría en Ingenieríaspa
dc.description.abstractThe main objective of this research project is to create a model for the prediction of undergraduate student desertion at the Universidad de la Costa - CUC, based on the analysis of different socioeconomic and academic factors. The study required the execution of a series of phases: characterization, experimentation, development and evaluation. During the characterization phase, a dataset was constructed, based on the compilation of demographic, cultural, social, family, educational, socioeconomic status and psychological profile data of each student, for the periods between 2013-1 and 2018-2. Such information was collected from the registration forms that students fill out when they enter the institution, a total of 1,606 unique student records were collected. During the experimental phase, different machine learning techniques were evaluated for the categories: Bayesian networks, support vector machines, and decision trees. The algorithm with which the best hit rate was obtained was Random forest (from the decision tree category), with an accuracy of 84.8%. In the development phase, the model was integrated into an application that allows us to predict whether a student or a group of students will drop out or not. Finally, in the evaluation phase, the application was subjected to different types of tests to evaluate both the functionality of the graphic interface with the final user and the success rate in terms of desertion prediction, the results have coincided with the precision obtained in the experimental phase.spa
dc.description.abstractEl objetivo principal de este proyecto de investigación es crear un modelo para la predicción de la deserción de estudiantes de pregrado en la Universidad de la Costa - CUC, a partir del análisis de diferentes factores socioeconómicos y académicos. El estudio requirió de la ejecución de una serie de fases: caracterización, experimentación, desarrollo y evaluación. Durante la fase de caracterización se construyó un conjunto de datos (dataset), a partir de la compilación de los datos demográficos, culturales, sociales, familiares, educativos, estatus socioeconómico y perfil psicológico de cada estudiante, de los periodos comprendidos entre 2013-1 y 2018-2. Tal información fue recopilada a partir de los formatos de inscripción que diligencian los estudiantes cuando ingresan a la institución, un total de 1.606 registros únicos de estudiantes fueron recopilados. Durante la fase de experimentación se evaluaron distintas técnicas de aprendizaje automático (Machine Learning) de las categorías: redes bayesianas, máquinas de soporte vectorial y árboles de decisiones. El algoritmo con el cual se obtuvo la mejor tasa de aciertos fue Random forest (de la categoría árboles de decisión), con una exactitud del 84.8%. En la fase de desarrollo se integró el modelo a una aplicación que permite predecir si un estudiante o un grupo de ellos desertará o no. Por último, en la fase de evaluación se sometió la aplicación a diferentes tipos de pruebas para valorar tanto la funcionalidad de la interface gráfica con el usuario final como la tasa de aciertos en cuanto a la predicción de la deserción, los resultados han coincidido con la precisión obtenida en la fase experimental.spa
dc.language.isospaspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectHigher educationspa
dc.subjectDropoutspa
dc.subjectData miningspa
dc.subjectDecision treespa
dc.subjectClassificationspa
dc.subjectPredictionspa
dc.subjectEducación superiorspa
dc.subjectDeserciónspa
dc.subjectMinería de datosspa
dc.subjectÁrboles de decisiónspa
dc.subjectClasificaciónspa
dc.subjectPredicciónspa
dc.titleModelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datosspa
dc.typemasterThesisspa
dcterms.referencesAboubakar, M., Kellil, M., Bouabdallah, A., & Roux, P. (2019). Toward Intelligent Reconfiguration of RPL Networks using Supervised Learning. IFIP Wireless Days, 2019- April, 1–4. https://doi.org/10.1109/WD.2019.8734236spa
dcterms.referencesAggarwal, C. (2015). Data Mining: The Textbook. Springer International. https://doi.org/10.1007/978-3-319-14142-8 ISBNspa
dcterms.referencesAhuja, R., & Kankane, Y. (2017). Predicting the probability of student’s degree completion by using different data mining techniques. 2017 Fourth International Conference on Image Information Processing (ICIIP), 1–4. https://doi.org/10.1109/ICIIP.2017.8313763spa
dcterms.referencesAlkhasawneh, R., & Hobson, R. (2011). Modeling student retention in science and engineering disciplines using neural networks. 2011 IEEE Global Engineering Education Conference, EDUCON 2011, 660–663. https://doi.org/10.1109/EDUCON.2011.5773209spa
dcterms.referencesAsif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007spa
dcterms.referencesAskinadze, A., & Conrad, S. (2017). Application of the Dynamic Time Warping Distance for the Student Drop-out Prediction on Time Series Data. Proceedings of the 10th International Conference on Educational Data Mining, 342–343.spa
dcterms.referencesAzevedo, A., & Santos, M. F. (2008). KDD, semma and CRISP-DM: A parallel overview. MCCSIS’08 - IADIS Multi Conference on Computer Science and Information Systems; Proceedings of Informatics 2008 and Data Mining 2008, June, 182–185.spa
dcterms.referencesAziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students’ academic performance analysis using naïve bayes classifier. Jurnal Teknologi, 75(3), 13–19. https://doi.org/10.11113/jt.v75.5037spa
dcterms.referencesBarbosa Manhães, L. M., Serra da Cruz, S. M., & Zimbrão, G. (2014). WAVE: an Architecture for Predicting Dropout in Undergraduate Courses using EDM. Proceeding SAC ’14 Proceedings of the 29th Annual ACM Symposium on Applied Computing, 243–247. https://doi.org/10.1145/2554850.2555135spa
dcterms.referencesBarker, K., Trafalis, T., & Reed Rhoads, T. (2004). LEARNING FROM STUDENT DATA. Proceedings of the 2004 Systems and Information Engineering Design Symposium Matthew. https://doi.org/10.1109/SIEDS.2004.239819spa
dcterms.referencesBarnes, T., Desmarais, M., Romero, C., & Ventura, S. (2009). EDM’09 - Educational Data Mining 2009: 2nd International Conference on Educational Data Mining. In EDM’09 - Educational Data Mining 2009: 2nd International Conference on Educational Data Mining.spa
dcterms.referencesBayer, J., Bydzovská, H., Géryk, J., Obsivac, T., & Popelínský, L. (2012). Predicting drop-out from social behaviour of students. Proceedings of the 5th International Conference on Educational Data Mining, Dm, 103–109.spa
dcterms.referencesBeaulac, C., & Rosenthal, J. S. (2019). Predicting University Students ’ Academic Success and Choice of Major using Random Forests.spa
dcterms.referencesBeltran, B. (2016). MINERÍA DE DATOS (Vol. 30, Issue 1). https://doi.org/10.1016/0032- 0633(82)90071-Xspa
dcterms.referencesBetancourt, G. A. (2005). LAS MÁQUINAS DE SOPORTE VECTORIAL (SVMs). Scientia Et Technica, XI(27), 67–72. https://doi.org/10.22517/23447214.6895spa
dcterms.referencesBirjali, M., Beni-hssane, A., & Erritali, M. (2018). Learning with Big Data Technology: The Future of Education. 565. https://doi.org/10.1007/978-3-319-60834-1spa
dcterms.referencesBoser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, 144–152. https://doi.org/10.1145/130385.130401spa
dcterms.referencesBrown, M. S. (2014). Data Mining for Dummies. https://doi.org/10.1007/978-1-4614-7669-6spa
dcterms.referencesBurgueño, M. J., García-Bastos, J. L., & González-Buitrago, J. M. (1995). ROC curves in the evaluation of diagnostic tests. Medicina Clínica, 104(17), 661–670.spa
dcterms.referencesCambruzzi, W., Rigo, S. J., & Barbosa, J. L. V. (2015). Dropout prediction and reduction in distance education courses with the learning analytics multitrail approach. Journal of Universal Computer Science, 21(1), 23–47.spa
dcterms.referencesCarmona Suárez, E. J. (2014). Máquinas de Vectores Soporte (SVM). Dpto. de Inteligencia Artificial, ETS de Ingeniería Inforática, Universidad Nacional de Educación a Distancia (UNED), 1–25. http://www.ia.uned.es/~ejcarmona/publicaciones/[2013-Carmona] SVM.pdfspa
dcterms.referencesCastaño, E., Gallón, S., Gómez, K., & Vásquez, J. (2004). Deserción estudiantil universitaria una aplicación de modelos de duración. Lecturas de Economia, 60(60), 39–65.spa
dcterms.referencesChai, K. E. K., & Gibson, D. (2015). Predicting the risk of attrition for undergraduate students with time based modelling. Proceedings of the 12th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2015, Celda, 109–116.spa
dcterms.referencesCheewaprakobkit, P. (2013). Study of factors analysis affecting academic achievement of undergraduate students in international program. Lecture Notes in Engineering and Computer Science, 2202, 332–336.spa
dcterms.referencesChristian, T. M., & Ayub, M. (2014). Exploration of classification using NBTree for predicting students’ performance. Proceedings of 2014 International Conference on Data and Software Engineering, ICODSE 2014, 1–6. https://doi.org/10.1109/ICODSE.2014.7062654 Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Mach. Learn. , 20 (3), 44(13), 273– 297. Predicting Students Drop Out A Case Study, (2009).spa
dcterms.referencesDelen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506. https://doi.org/10.1016/j.dss.2010.06.003spa
dcterms.referencesDelen, D. (2011). Predicting student attrition with data mining methods. Journal of College Student Retention: Research, Theory and Practice, 13(1), 17–35. https://doi.org/10.2190/CS.13.1.bspa
dcterms.referencesDevasia, T., Vinushree, T. P., & Hegde, V. (2016). Prediction of students performance using Educational Data Mining. Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, 91–95. https://doi.org/10.1109/SAPIENCE.2016.7684167spa
dcterms.referencesDharmawan, T., Ginardi, H., & Munif, A. (2018). Dropout Detection Using Non-Academic Data. Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018, 1, 1–4. https://doi.org/10.1109/ICSTC.2018.8528619spa
dcterms.referencesEdwards, W., & Fasolo, B. (2001). Decision Technology. 581–606.spa
dcterms.referencesFernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. Van. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94(August 2017), 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012spa
dcterms.referencesFriedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifier. 131–163.spa
dcterms.referencesGandhi, R. (2018). Support Vector Machine - Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learningalgorithms-934a444fca47spa
dcterms.referencesGarcía, J. G., Puga, J. L., Cano Guillén, C. J., Gea, A. B., & de la Fuente Sánchez, L. (2006). Aplicación de las redes bayesianas al modelado de las actitudes emprendedoras. IV Congreso de Metodología de Encuestas, August 2015, 235–242.spa
dcterms.referencesGulati, H. (2015). Predictive analytics using data mining technique. 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015, 713–716.spa
dcterms.referencesGüner, N., Yaldir, A., Gündüz, G., Çomak, E., Tokat, S., & Iplikçi, S. (2014). Predicting academically at-risk engineering students: A soft computing application. Acta Polytechnica Hungarica, 11(5), 199–216. https://doi.org/10.12700/aph.11.05.2014.05.12spa
dcterms.referencesGuzmán Ruiz, C., Muriel Durán, D., & Franco Gallego, J. (2009). Deserción estudiantil en la educación superior colombiana. Metodología de seguimiento, diagnóstico y elementos para su prevención. http://www.mineducacion.gov.co/sistemasdeinformacion/1735/articles- 254702_libro_desercion.pdfspa
dcterms.referencesHan, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques. https://doi.org/10.1109/ICMIRA.2013.45spa
dcterms.referencesHasbun, T., Araya, A., & Villalon, J. (2016). Extracurricular activities as dropout prediction factors in higher education using decision trees. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016, 242–244. https://doi.org/10.1109/ICALT.2016.66spa
dcterms.referencesHeredia, D., Amaya, Y., & Barrientos, E. (2015). Student Dropout Predictive Model Using Data Mining Techniques. IEEE Latin America Transactions, 13(9), 3127–3134. https://doi.org/10.1109/TLA.2015.7350068spa
dcterms.referencesHernandez Gonzalez, A. G., Melendez Armenta, R. A., Morales Rosales, L. A., Garcia Barrientos, A., Tecpanecatl Xihuitl, J. L., & Algredo, I. (2016). Comparative Study of Algorithms to Predict the Desertion in the Students at the ITSM-Mexico. IEEE Latin America Transactions, 14(11), 4573–4578. https://doi.org/10.1109/TLA.2016.7795831spa
dcterms.referencesHoffait, A. S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11. https://doi.org/10.1016/j.dss.2017.05.003spa
dcterms.referencesHuber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model. Procedia CIRP, 79, 403–408. https://doi.org/10.1016/j.procir.2019.02.106spa
dcterms.referencesJin, Q., Imbrie, P. K., Lin, J. J. J., & Chen, X. (2011). A multi-outcome hybrid model for predicting student success in engineering. ASEE Annual Conference and Exposition, Conference Proceedings.spa
dcterms.referencesKabakchieva, D., Stefanova, K., & Kisimov, V. (2011). Analyzing university data for determining student profiles and predicting performance. EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining.spa
dcterms.referencesKalles, D., & Pierrakeas, C. (2006a). Analyzing student performance in distance learning with genetic algorithms and decision trees. Applied Artificial Intelligence, 20(8), 655–674. https://doi.org/10.1080/08839510600844946spa
dcterms.referencesKalles, D., & Pierrakeas, C. (2006b). Using genetic algorithms and decision trees for a posteriori analysis and evaluation of tutoring practices based on student failure models. IFIP International Federation for Information Processing, 204(August), 9–18. https://doi.org/10.1007/0-387-34224-9_2spa
dcterms.referencesKingsford, C., & Salzberg, S. L. (2008). What are decision trees. Nat Biotechnol, 23(1), 1–7. https://doi.org/10.1038/nbt0908-1011spa
dcterms.referencesKotsiantis, S. B., & Pintelas, P. E. (2005). Predicting students’ marks in Hellenic Open University. Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005, 2005, 664–668. https://doi.org/10.1109/ICALT.2005.223spa
dcterms.referencesKotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting students’ performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18(5), 411–426. https://doi.org/10.1080/08839510490442058spa
dcterms.referencesKrishna Kishore, K. V., Venkatramaphanikumar, S., & Alekhya, S. (2014). Prediction of student academic progression: A case study on Vignan University. 2014 International Conference on Computer Communication and Informatics: Ushering in Technologies of Tomorrow, Today, ICCCI 2014, 2, 1–6. https://doi.org/10.1109/ICCCI.2014.6921731spa
dcterms.referencesKumar Baradwaj, B., & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. JACSA) International Journal of Advanced Computer Science and Applications, 02.spa
dcterms.referencesLee, S., & Chung, J. Y. (2019). The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction. Applied Sciences, 9(15), 3093. https://doi.org/10.3390/app9153093spa
dcterms.referencesLesinski, G., Corns, S., & Dagli, C. (2016). Application of an Artificial Neural Network to Predict Graduation Success at the United States Military Academy. Procedia Computer Science, 95, 375–382. https://doi.org/10.1016/j.procs.2016.09.348spa
dcterms.referencesLópez de Ullibarri, G. I., & Píta Fernández, S. (1998). Curvas ROC. Cad Aten Primaria, 5(4), 229–235.spa
dcterms.referencesLykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers and Education, 53(3), 950–965. https://doi.org/10.1016/j.compedu.2009.05.010spa
dcterms.referencesManhães, L. M. B., Da Cruz, S. M. S., & Zimbrão, G. (2014). The impact of high dropout rates in a large public brazilian university a quantitative approach using educational data mining. CSEDU 2014 - Proceedings of the 6th International Conference on Computer Supported Education, 3, 124–129. https://doi.org/10.5220/0004958601240129spa
dcterms.referencesMárquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124. https://doi.org/10.1111/exsy.12135spa
dcterms.referencesMárquez-Vera, C., Romero Morales, C., & Ventura Soto, S. (2013). Predicting school failure and dropout by using data mining techniques. Revista Iberoamericana de Tecnologias Del Aprendizaje, 8(1), 7–14. https://doi.org/10.1109/RITA.2013.2244695spa
dcterms.referencesMayilvaganan, M., & Kalpanadevi, D. (2015). Comparison of classification techniques for predicting the performance of students academic environment. 2014 International Conference on Communication and Network Technologies, ICCNT 2014, 2015-March, 113–118. https://doi.org/10.1109/CNT.2014.7062736spa
dcterms.referencesMinisterio de Educación de Colombia. (2006). La Revolución Educativa 2002 – 2006. Media, 1– 6.spa
dcterms.referencesMinisterio de Educación de Colombia. (2019). Qué es el SPADIES. https://www.mineducacion.gov.co/sistemasinfo/spadies/Informacion- Institucional/254648:Que-es-el-SPADIESspa
dcterms.referencesMiranda, M. A., & Guzmán, J. (2017). Análisis de la deserción de estudiantes universitarios usando técnicas de minería de datos. Formacion Universitaria, 10(3), 61–68. https://doi.org/10.4067/S0718-50062017000300007spa
dcterms.referencesMishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm. https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithmf10ba6e38234spa
dcterms.referencesMishra, T., Kumar, D., & Gupta, S. (2014). Mining students’ data for prediction performance. International Conference on Advanced Computing and Communication Technologies, ACCT, 255–262. https://doi.org/10.1109/ACCT.2014.105spa
dcterms.referencesMitchell, T. M. (1997). Machine Learning. In McGraw-Hill Science/Engineering/Math.spa
dcterms.referencesMoseley, L. G., & Mead, D. M. (2008). Predicting who will drop out of nursing courses: A machine learning exercise. Nurse Education Today, 28(4), 469–475. https://doi.org/10.1016/j.nedt.2007.07.012spa
dcterms.referencesMustafa, M. N., Chowdhury, L., & Kamal, M. S. (2012). Students dropout prediction for intelligent system from tertiary level in developing country. 2012 International Conference on Informatics, Electronics and Vision, ICIEV 2012, 113–118. https://doi.org/10.1109/ICIEV.2012.6317441spa
dcterms.referencesOskouei, R. J., & Askari, M. (2014). Predicting Academic Performance with Applying Data Mining Techniques (Generalizing the results of two Different Case Studies). Computer Engineering and Applications Journal, 3(2), 79–88. https://doi.org/10.18495/comengapp.v3i2.81spa
dcterms.referencesOsmanbegovi, E. (2012). Data Mining Approach for Predicting Student Performance. Economic Review : Journal of Economics and Business, X(1), 3–12.spa
dcterms.referencesPeralta, B., Poblete, T., & Caro, L. (2017). Automatic feature selection for desertion and graduation prediction: A chilean case. Proceedings - International Conference of the Chilean Computer Science Society, SCCC. https://doi.org/10.1109/SCCC.2016.7836055spa
dcterms.referencesPereira, R. T., Romero, A. C., & Toledo, J. J. (2013). Extraction student dropout patterns with data mining techniques in undergraduate programs. IC3K 2013; KDIR 2013 - 5th International Conference on Knowledge Discovery and Information Retrieval and KMIS 2013 - 5th International Conference on Knowledge Management and Information Sharing, Proc., 136–142. https://doi.org/10.5220/0004543001360142spa
dcterms.referencesPérez, A., Grandón, E. E., Caniupán, M., & Vargas, G. (2019). Comparative Analysis of Prediction Techniques to Determine Student Dropout: Logistic Regression vs Decision Trees. Proceedings - International Conference of the Chilean Computer Science Society, SCCC, 2018-Novem. https://doi.org/10.1109/SCCC.2018.8705262spa
dcterms.referencesPerez, B., Castellanos, C., & Correal, D. (2018). Applying Data Mining Techniques to Predict Student Dropout: A Case Study. 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, 1–6. https://doi.org/10.1109/ColCACI.2018.8484847spa
dcterms.referencesPerez, M. (2014). Minería de datos a treves de ejemplos. 22. http://www.rclibros.es/pdf/capitulo_mineria.pdfspa
dcterms.referencesPicard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., & Strohecker, C. (2004). Affective learning - a manifesto. BT Technology Journal, 22(4), 253–269. https://doi.org/10.1023/B:BTTJ.0000047603.37042.33spa
dcterms.referencesPradeep, A., Das, S., & Kizhekkethottam, J. J. (2015). Students dropout factor prediction using EDM techniques. Proceedings of the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015, 1–7. https://doi.org/10.1109/ICSNS.2015.7292372spa
dcterms.referencesQuadri, M., & Kalyankar, D. (2010). Drop out feature of student data for academic performance using decision tree techniques. Global Journal of Computer, 10(2), 2–5. http://computerresearch.org/stpr/index.php/gjcst/article/viewArticle/128spa
dcterms.referencesQuinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/bf00116251spa
dcterms.referencesRecuero, P. (2018). Machine Learning a tu alcance: La matriz de confusión. https://empresas.blogthinkbig.com/ml-a-tu-alcance-matriz-confusion/spa
dcterms.referencesSalazar, A., Gosálbez, J., Bosch, I., Miralles, R., & Vergara, L. (2004). A case study of knowledge discovery on academic achievement, student desertion and student retention. ITRE 2004 - 2nd International Conference on Information Technology: Research and Education - Proceedings, January, 150–154. https://doi.org/10.1109/itre.2004.1393665spa
dcterms.referencesSangodiah, A., Beleya, P., Muniandy, M., Heng, L. E., & Ramendran Spr, C. (2015). Minimizing student attrition in higher learning institutions in Malaysia using support vector machine. Journal of Theoretical and Applied Information Technology, 71(3), 377–385.spa
dcterms.referencesSantana, M. A., Costa, E. B., Neto, B. F. S., Silva, I. C. L., & Rego, J. B. A. (2015). A predictive model for identifying students with dropout profiles in online courses. CEUR Workshop Proceedings, 1446.spa
dcterms.referencesŞara, N. B., Halland, R., Igel, C., & Alstrup, S. (2015). High-school dropout prediction using machine learning: A Danish large-scale study. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings, April, 319–324.spa
dcterms.referencesSaravanan, R., & Sujatha, P. (2018). Algorithms : A Perspective of Supervised Learning Approaches in Data Classification. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Iciccs, 945–949.spa
dcterms.referencesSarker, F., Tiropanis, T., & Davis, H. C. (2014). Linked data, data mining and external open data for better prediction of at-risk students. Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014, 652–657. https://doi.org/10.1109/CoDIT.2014.6996973spa
dcterms.referencesSegura-Morales, M., & Loza-Aguirre, E. (2018). Using Decision Trees for Predicting Academic Performance Based on Socio-Economic Factors. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, 1132– 1136. https://doi.org/10.1109/CSCI.2017.197spa
dcterms.referencesShahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72(February 2016), 414–422. https://doi.org/10.1016/j.procs.2015.12.157spa
dcterms.referencesSharabiani, A., Karim, F., Sharabiani, A., Atanasov, M., & Darabi, H. (2014). An enhanced bayesian network model for prediction of students’ academic performance in engineering programs. IEEE Global Engineering Education Conference, EDUCON, April, 832–837. https://doi.org/10.1109/EDUCON.2014.6826192spa
dcterms.referencesSiri, A. (2015). Predicting Students’ Dropout at University Using Artificial Neural Networks. Italian Journal of Sociology of Education, 7(2), 225–247.spa
dcterms.referencesSolis, M., Moreira, T., Gonzalez, R., Fernandez, T., & Hernandez, M. (2018). Perspectives to Predict Dropout in University Students with Machine Learning. 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. https://doi.org/10.1109/IWOBI.2018.8464191spa
dcterms.referencesTair, M. M. A. (2015). Mining Educational Data to Improve Students ’ Performance : A Case Study Mining Educational Data t o Improve Students ’ Performance : A Case Study. October.spa
dcterms.referencesThomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251–269. Timarán Pereira, S. R., Hernández Arteaga, I., Caicedo Zambrano, S. J., Hidalgo Troya, A., & Alvaradospa
dcterms.referencesPérez, J. C. (2016). Descubrimiento de patrones de desempeño académico con árboles de decisión en las competencias genéricas de la formación profesional. Descubrimiento de Patrones de Desempeño Académico Con Árboles de Decisión En Las Competencias Genéricas de La Formación Profesional, 2016, 63–86. https://doi.org/10.16925/9789587600490spa
dcterms.referencesTsai, C. F., Tsai, C. T., Hung, C. S., & Hwang, P. Sen. (2011). Data mining techniques for identifying students at risk of failing a computer proficiency test required for graduation. Australasian Journal of Educational Technology, 27(3), 481–498. https://doi.org/10.14742/ajet.956spa
dcterms.referencesUniversidad Pedagógica y Tecnológica de Colombia. (2004). Unidad 1 Estadistica Descriptiva. https://virtual.uptc.edu.co/ova/estadistica/docs/libros/h_men_prob_est/lecciones_html/un1/1 _8_3.htmlspa
dcterms.referencesVeitch, W. R. (2004). Identifying Characteristics of High School Dropouts: Data Mining with A Decision Tree Model. Online Submission, 1–11.spa
dcterms.referencesWirth, R. (2000). CRISP-DM : Towards a Standard Process Model for Data Mining. Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, 24959, 29–39. https://doi.org/10.1.1.198.5133spa
dcterms.referencesYehuala, M. A. (2015). Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University. International Journal of Scientific & Technology Research, 4(4), 91–94.spa
dcterms.referencesZaki, M., & Meira, W. J. (2013). Data Mining and Analysis: Fundamental Concepts and Algorithms. https://doi.org/10.1145/3054925spa
dcterms.referencesZeng, W., Chin, S.-C., Zeimet, B., Kuang, R., & Chi, C.-L. (2017). Dropout Prediction in Home Care Training. Proceedings of the 10th International Conference on Educational Data Mining, 442–447.spa
dcterms.referencesZhang, Y., & Oussena, S. (2010). USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION – A CASE STUDY. Middlesex University Research Repository.spa
dc.contributor.tutorDe la hoz Franco, Emiro
dc.contributor.tutorMendoza Palechor, Fabio
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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