Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos

The 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...

Full description

Autores:: Camargo García, Aníbal José

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

id	RCUC2_07778aacc95d763518148a481476953b
oai_identifier_str	oai:repositorio.cuc.edu.co:11323/7077
network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
title	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
spellingShingle	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos Higher education Dropout Data mining Decision tree Classification Prediction Educación superior Deserción Minería de datos Árboles de decisión Clasificación Predicción
title_short	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
title_full	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
title_fullStr	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
title_full_unstemmed	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
title_sort	Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos
dc.creator.fl_str_mv	Camargo García, Aníbal José
dc.contributor.advisor.spa.fl_str_mv	De la hoz Franco, Emiro Mendoza Palechor, Fabio
dc.contributor.author.spa.fl_str_mv	Camargo García, Aníbal José
dc.subject.spa.fl_str_mv	Higher education Dropout Data mining Decision tree Classification Prediction Educación superior Deserción Minería de datos Árboles de decisión Clasificación Predicción
topic	Higher education Dropout Data mining Decision tree Classification Prediction Educación superior Deserción Minería de datos Árboles de decisión Clasificación Predicción
description	The 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.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-09-08T23:29:10Z
dc.date.available.none.fl_str_mv	2020-09-08T23:29:10Z
dc.date.issued.none.fl_str_mv	2020
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
status_str	acceptedVersion
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/7077
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
url	https://hdl.handle.net/11323/7077 https://repositorio.cuc.edu.co/
identifier_str_mv	Corporación Universidad de la Costa REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Aboubakar, 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.2019.8734236 Aggarwal, C. (2015). Data Mining: The Textbook. Springer International. https://doi.org/10.1007/978-3-319-14142-8 ISBN Ahuja, 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.2017.8313763 Alkhasawneh, 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.2011.5773209 Asif, 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.2017.05.007 Askinadze, 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. Azevedo, 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. Aziz, 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.v75.5037 Barbosa 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/10.1145/2554850.2555135 Barker, 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.2004.239819 Barnes, 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. Bayer, 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. Beaulac, C., & Rosenthal, J. S. (2019). Predicting University Students ’ Academic Success and Choice of Major using Random Forests. Beltran, B. (2016). MINERÍA DE DATOS (Vol. 30, Issue 1). https://doi.org/10.1016/0032- 0633(82)90071-X Betancourt, G. A. (2005). LAS MÁQUINAS DE SOPORTE VECTORIAL (SVMs). Scientia Et Technica, XI(27), 67–72. https://doi.org/10.22517/23447214.6895 Birjali, M., Beni-hssane, A., & Erritali, M. (2018). Learning with Big Data Technology: The Future of Education. 565. https://doi.org/10.1007/978-3-319-60834-1 Boser, 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/10.1145/130385.130401 Brown, M. S. (2014). Data Mining for Dummies. https://doi.org/10.1007/978-1-4614-7669-6 Burgueño, 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. Cambruzzi, 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. Carmona 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.pdf Castaño, E., Gallón, S., Gómez, K., & Vásquez, J. (2004). Deserción estudiantil universitaria una aplicación de modelos de duración. Lecturas de Economia, 60(60), 39–65. Chai, 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. Cheewaprakobkit, 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. Christian, 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). Delen, 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.2010.06.003 Delen, 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.b Devasia, 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.2016.7684167 Dharmawan, 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.2018.8528619 Edwards, W., & Fasolo, B. (2001). Decision Technology. 581–606. Fernandes, 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.2018.02.012 Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifier. 131–163. Gandhi, R. (2018). Support Vector Machine - Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learningalgorithms-934a444fca47 García, 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. Gulati, H. (2015). Predictive analytics using data mining technique. 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015, 713–716. Güner, 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.11.05.2014.05.12 Guzmán 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.pdf Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques. https://doi.org/10.1109/ICMIRA.2013.45 Hasbun, 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.2016.66 Heredia, 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.2015.7350068 Hernandez 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.2016.7795831 Hoffait, 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.2017.05.003 Huber, 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.2019.02.106 Jin, 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. Kabakchieva, 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. Kalles, 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/10.1080/08839510600844946 Kalles, 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_2 Kingsford, C., & Salzberg, S. L. (2008). What are decision trees. Nat Biotechnol, 23(1), 1–7. https://doi.org/10.1038/nbt0908-1011 Kotsiantis, 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.2005.223 Kotsiantis, 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/10.1080/08839510490442058 Krishna 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.2014.6921731 Kumar Baradwaj, B., & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. JACSA) International Journal of Advanced Computer Science and Applications, 02. Lee, 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/app9153093 Lesinski, 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.2016.09.348 López de Ullibarri, G. I., & Píta Fernández, S. (1998). Curvas ROC. Cad Aten Primaria, 5(4), 229–235. Lykourentzou, 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.2009.05.010 Manhães, 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/10.5220/0004958601240129 Márquez-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.12135 Márquez-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.2013.2244695 Mayilvaganan, 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.2014.7062736 Ministerio de Educación de Colombia. (2006). La Revolución Educativa 2002 – 2006. Media, 1– 6. Ministerio de Educación de Colombia. (2019). Qué es el SPADIES. https://www.mineducacion.gov.co/sistemasinfo/spadies/Informacion- Institucional/254648:Que-es-el-SPADIES Miranda, 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/S0718-50062017000300007 Mishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm. https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithmf10ba6e38234 Mishra, 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.2014.105 Mitchell, T. M. (1997). Machine Learning. In McGraw-Hill Science/Engineering/Math. Moseley, 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.2007.07.012 Mustafa, 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.2012.6317441 Oskouei, 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.v3i2.81 Osmanbegovi, E. (2012). Data Mining Approach for Predicting Student Performance. Economic Review : Journal of Economics and Business, X(1), 3–12. Peralta, 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.2016.7836055 Pereira, 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/10.5220/0004543001360142 Pérez, 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.2018.8705262 Perez, 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.2018.8484847 Perez, M. (2014). Minería de datos a treves de ejemplos. 22. http://www.rclibros.es/pdf/capitulo_mineria.pdf Picard, 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.0000047603.37042.33 Pradeep, 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.2015.7292372 Quadri, 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/128 Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/bf00116251 Recuero, P. (2018). Machine Learning a tu alcance: La matriz de confusión. https://empresas.blogthinkbig.com/ml-a-tu-alcance-matriz-confusion/ Salazar, 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.2004.1393665 Sangodiah, 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. Santana, 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. Şara, 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. Saravanan, 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. Sarker, 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.2014.6996973 Segura-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.2017.197 Shahiri, 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.2015.12.157 Sharabiani, 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.2014.6826192 Siri, A. (2015). Predicting Students’ Dropout at University Using Artificial Neural Networks. Italian Journal of Sociology of Education, 7(2), 225–247. Solis, 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.2018.8464191 Tair, 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. Thomas, 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., & Alvarado Pérez, 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/10.16925/9789587600490 Tsai, 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.956 Universidad 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.html Veitch, W. R. (2004). Identifying Characteristics of High School Dropouts: Data Mining with A Decision Tree Model. Online Submission, 1–11. Wirth, 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/10.1.1.198.5133 Yehuala, 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. Zaki, M., & Meira, W. J. (2013). Data Mining and Analysis: Fundamental Concepts and Algorithms. https://doi.org/10.1145/3054925 Zeng, 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. Zhang, Y., & Oussena, S. (2010). USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION – A CASE STUDY. Middlesex University Research Repository.
dc.rights.spa.fl_str_mv	Attribution-NonCommercial-ShareAlike 4.0 International
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.publisher.spa.fl_str_mv	Corporación Universidad de la Costa
dc.publisher.program.spa.fl_str_mv	Maestría en Ingeniería
institution	Corporación Universidad de la Costa
bitstream.url.fl_str_mv	https://repositorio.cuc.edu.co/bitstreams/837274c4-814e-440b-9668-ad287c3b15c5/download https://repositorio.cuc.edu.co/bitstreams/efa03807-b78b-4671-9df5-ba26d7a0e4e4/download https://repositorio.cuc.edu.co/bitstreams/df3c7a58-9b94-40c0-b9e0-750669def4e3/download https://repositorio.cuc.edu.co/bitstreams/6a1ba468-2cce-486c-9ec5-76081059f2c2/download https://repositorio.cuc.edu.co/bitstreams/d8c1cf3a-3005-4c49-b1dd-76ee57dd4a81/download https://repositorio.cuc.edu.co/bitstreams/b303bf15-1e1b-4c23-b514-5a09cc7715b8/download
bitstream.checksum.fl_str_mv	3fa8da0438279c3839c5d31a1d2864ed 934f4ca17e109e0a05eaeaba504d7ce4 e30e9215131d99561d40d6b0abbe9bad 631709ed4e9bcd5e097b8300de6b4a43 631709ed4e9bcd5e097b8300de6b4a43 883c081178af3b7ca2843441b5a3c256
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv	repdigital@cuc.edu.co
_version_	1828166904099897344
spelling	De la hoz Franco, EmiroMendoza Palechor, FabioCamargo García, Aníbal José2020-09-08T23:29:10Z2020-09-08T23:29:10Z2020https://hdl.handle.net/11323/7077Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.El 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.Camargo García, Aníbal JoséspaCorporación Universidad de la CostaMaestría en IngenieríaAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Higher educationDropoutData miningDecision treeClassificationPredictionEducación superiorDeserciónMinería de datosÁrboles de decisiónClasificaciónPredicciónModelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datosTrabajo de grado - MaestríaTextinfo:eu-repo/semantics/masterThesishttp://purl.org/redcol/resource_type/TMinfo:eu-repo/semantics/acceptedVersionAboubakar, 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.2019.8734236Aggarwal, C. (2015). Data Mining: The Textbook. Springer International. https://doi.org/10.1007/978-3-319-14142-8 ISBNAhuja, 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.2017.8313763Alkhasawneh, 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.2011.5773209Asif, 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.2017.05.007Askinadze, 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.Azevedo, 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.Aziz, 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.v75.5037Barbosa 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/10.1145/2554850.2555135Barker, 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.2004.239819Barnes, 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.Bayer, 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.Beaulac, C., & Rosenthal, J. S. (2019). Predicting University Students ’ Academic Success and Choice of Major using Random Forests.Beltran, B. (2016). MINERÍA DE DATOS (Vol. 30, Issue 1). https://doi.org/10.1016/0032- 0633(82)90071-XBetancourt, G. A. (2005). LAS MÁQUINAS DE SOPORTE VECTORIAL (SVMs). Scientia Et Technica, XI(27), 67–72. https://doi.org/10.22517/23447214.6895Birjali, M., Beni-hssane, A., & Erritali, M. (2018). Learning with Big Data Technology: The Future of Education. 565. https://doi.org/10.1007/978-3-319-60834-1Boser, 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/10.1145/130385.130401Brown, M. S. (2014). Data Mining for Dummies. https://doi.org/10.1007/978-1-4614-7669-6Burgueño, 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.Cambruzzi, 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.Carmona 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.pdfCastaño, E., Gallón, S., Gómez, K., & Vásquez, J. (2004). Deserción estudiantil universitaria una aplicación de modelos de duración. Lecturas de Economia, 60(60), 39–65.Chai, 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.Cheewaprakobkit, 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.Christian, 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).Delen, 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.2010.06.003Delen, 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.bDevasia, 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.2016.7684167Dharmawan, 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.2018.8528619Edwards, W., & Fasolo, B. (2001). Decision Technology. 581–606.Fernandes, 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.2018.02.012Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifier. 131–163.Gandhi, R. (2018). Support Vector Machine - Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learningalgorithms-934a444fca47García, 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.Gulati, H. (2015). Predictive analytics using data mining technique. 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015, 713–716.Güner, 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.11.05.2014.05.12Guzmán 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.pdfHan, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques. https://doi.org/10.1109/ICMIRA.2013.45Hasbun, 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.2016.66Heredia, 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.2015.7350068Hernandez 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.2016.7795831Hoffait, 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.2017.05.003Huber, 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.2019.02.106Jin, 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.Kabakchieva, 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.Kalles, 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/10.1080/08839510600844946Kalles, 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_2Kingsford, C., & Salzberg, S. L. (2008). What are decision trees. Nat Biotechnol, 23(1), 1–7. https://doi.org/10.1038/nbt0908-1011Kotsiantis, 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.2005.223Kotsiantis, 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/10.1080/08839510490442058Krishna 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.2014.6921731Kumar Baradwaj, B., & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. JACSA) International Journal of Advanced Computer Science and Applications, 02.Lee, 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/app9153093Lesinski, 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.2016.09.348López de Ullibarri, G. I., & Píta Fernández, S. (1998). Curvas ROC. Cad Aten Primaria, 5(4), 229–235.Lykourentzou, 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.2009.05.010Manhães, 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/10.5220/0004958601240129Márquez-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.12135Márquez-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.2013.2244695Mayilvaganan, 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.2014.7062736Ministerio de Educación de Colombia. (2006). La Revolución Educativa 2002 – 2006. Media, 1– 6.Ministerio de Educación de Colombia. (2019). Qué es el SPADIES. https://www.mineducacion.gov.co/sistemasinfo/spadies/Informacion- Institucional/254648:Que-es-el-SPADIESMiranda, 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/S0718-50062017000300007Mishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm. https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithmf10ba6e38234Mishra, 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.2014.105Mitchell, T. M. (1997). Machine Learning. In McGraw-Hill Science/Engineering/Math.Moseley, 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.2007.07.012Mustafa, 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.2012.6317441Oskouei, 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.v3i2.81Osmanbegovi, E. (2012). Data Mining Approach for Predicting Student Performance. Economic Review : Journal of Economics and Business, X(1), 3–12.Peralta, 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.2016.7836055Pereira, 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/10.5220/0004543001360142Pérez, 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.2018.8705262Perez, 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.2018.8484847Perez, M. (2014). Minería de datos a treves de ejemplos. 22. http://www.rclibros.es/pdf/capitulo_mineria.pdfPicard, 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.0000047603.37042.33Pradeep, 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.2015.7292372Quadri, 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/128Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/bf00116251Recuero, P. (2018). Machine Learning a tu alcance: La matriz de confusión. https://empresas.blogthinkbig.com/ml-a-tu-alcance-matriz-confusion/Salazar, 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.2004.1393665Sangodiah, 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.Santana, 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.Şara, 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.Saravanan, 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.Sarker, 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.2014.6996973Segura-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.2017.197Shahiri, 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.2015.12.157Sharabiani, 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.2014.6826192Siri, A. (2015). Predicting Students’ Dropout at University Using Artificial Neural Networks. Italian Journal of Sociology of Education, 7(2), 225–247.Solis, 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.2018.8464191Tair, 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.Thomas, 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., & AlvaradoPérez, 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/10.16925/9789587600490Tsai, 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.956Universidad 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.htmlVeitch, W. R. (2004). Identifying Characteristics of High School Dropouts: Data Mining with A Decision Tree Model. Online Submission, 1–11.Wirth, 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/10.1.1.198.5133Yehuala, 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.Zaki, M., & Meira, W. J. (2013). Data Mining and Analysis: Fundamental Concepts and Algorithms. https://doi.org/10.1145/3054925Zeng, 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.Zhang, Y., & Oussena, S. (2010). USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION – A CASE STUDY. Middlesex University Research Repository.PublicationORIGINALMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdfMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdfapplication/pdf1809812https://repositorio.cuc.edu.co/bitstreams/837274c4-814e-440b-9668-ad287c3b15c5/download3fa8da0438279c3839c5d31a1d2864edMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81031https://repositorio.cuc.edu.co/bitstreams/efa03807-b78b-4671-9df5-ba26d7a0e4e4/download934f4ca17e109e0a05eaeaba504d7ce4MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/df3c7a58-9b94-40c0-b9e0-750669def4e3/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.jpgMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.jpgimage/jpeg25282https://repositorio.cuc.edu.co/bitstreams/6a1ba468-2cce-486c-9ec5-76081059f2c2/download631709ed4e9bcd5e097b8300de6b4a43MD54THUMBNAILMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.jpgMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.jpgimage/jpeg25282https://repositorio.cuc.edu.co/bitstreams/d8c1cf3a-3005-4c49-b1dd-76ee57dd4a81/download631709ed4e9bcd5e097b8300de6b4a43MD54TEXTMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.txtMODELO PARA LA PREDICCIÓN DE LA DESERCIÓN DE ESTUDIANTES DE PREGADO, BASADO EN TÉCNICAS DE MINERÍA DE DATOS.pdf.txttext/plain195277https://repositorio.cuc.edu.co/bitstreams/b303bf15-1e1b-4c23-b514-5a09cc7715b8/download883c081178af3b7ca2843441b5a3c256MD5511323/7077oai:repositorio.cuc.edu.co:11323/70772024-09-17 14:24:12.736http://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos

Publicaciones similares