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dc.creatoramelec, viloria
dc.creatorSenior Naveda, Alexa
dc.creatorHernández Palma, Hugo
dc.creatorNiebles Nuñez, William
dc.creatorNiebles Nuñez, Leonardo David
dc.date.accessioned2020-01-30T13:42:58Z
dc.date.available2020-01-30T13:42:58Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/11323/5950
dc.description.abstractIn higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.spa
dc.language.isoengspa
dc.publisherJournal of Physics: Conference Seriesspa
dc.relation.ispartof10.1088/1742-6596/1432/1/012077/pdfspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectBig Dataspa
dc.subjectDropoutsspa
dc.subjectHigher educationspa
dc.titleUsing Big Data to determine potential dropouts in higher educationspa
dc.typeArticlespa
dcterms.references[1] Pineda Lezama, O., & Gómez Dorta, R. (2017). Techniques of multivariate statistical analysis: An application for the Honduran banking sector. Innovare: Journal of Science and Technology, 5 (2), 61-75spa
dcterms.references[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Chamspa
dcterms.references[3] Badr, G.; Algobail, A.; Almutairi, H.; Almutery, M.: Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department. Procedia Computer Science, Vol. 82, pp. 80-89 (2016)spa
dcterms.references[4] Hutt, S.; Gardener, M.; Kamentz, D.; Duckworth, A.; D'Mello, S.: Prospectively Predicting 4- year College Graduation from Student Applications. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 280-289 (2018)spa
dcterms.references[5] Ahuja, R.; Kankane, Y.: Predicting the probability of student's degree completion by using different data mining techniques. Fourth International Conference on Image Information Processing (ICIIP), pp. 1-4 (2017)spa
dcterms.references[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)spa
dcterms.references[7] James, G.; Witten, D.; Hastie, T.; Tibshirani, R.: An Introduction to Statistical Learning. Springer 7th Ed, pp. 25 (2014)spa
dcterms.references[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)spa
dcterms.references[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)spa
dcterms.references[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).spa
dcterms.references[11] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.spa
dcterms.references[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.spa
dcterms.references[13] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Computer Science , 151 , 1225- 1230.spa
dcterms.references[14] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Chamspa
dcterms.references[15] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.spa
dcterms.references[16] Demsar, J., Curk, T., Erjavec, A., Gorup C, Hocevar, T., Milutinovic, M., Mozina, M., Polajnar, M., Toplak, M., Staric, A., Stajdohar, M., Umek, L., Zagar, L., Zbontar, J., Zitnik, M., Zupan, B.: Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research 14(Aug):2349−2353 (2013).spa
dcterms.references[17] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).spa
dcterms.references[18] Demšar, J., & Zupan, B. Orange: Data mining fruitful and fun-a historical perspective. Informatica, 37(1), 55-60. (2013).spa
dcterms.references[19] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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