Show simple item record

dc.creatorViloria Silva, Amelec Jesus
dc.creatorCastro Sarmiento, Alex
dc.creatorMaría Santodomingo, Nicolás
dc.creatorMaría Santodomingo, Nicolas Elias
dc.creatorMárquez Blanco, Norka
dc.creatorCadavid Basto, Wilmer
dc.creatorHernández P, Hugo
dc.creatorNavarro Beltrán, Jorge
dc.creatorde la Hoz Hernández, Juan
dc.creatorRomero, Ligia
dc.description.abstract. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university
dc.description.sponsorshipUniversidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria
dc.publisherCommunications in Computer and Information Sciencespa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.subjectKnowledge extraction processspa
dc.subjectDecision makingspa
dc.subjectData miningspa
dc.titleData mining to identify risk factors associated with university students dropoutspa
dcterms.references1. Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística 9(1), 93–106 (2016) 2. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web visibility profiles of top100 latin american universities. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 254–262. Springer, Cham (2018). https://doi. org/10.1007/978-3-319-93803-5_24 3. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003) 4. Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Ann. Oper. Res. 168(1), 151–168 (2009) 5. Haykin, S.: Neural Networks a Comprehensive Foundation, 2nd edn. Macmillan College Publishing, Inc. USA (1999). ISBN 9780023527616 6. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, New Jersey (2009) 7. Abhay, K.A., Badal, N.A.: Novel approach for intelligent distribution of data warehouses. Egypt. Inf. J. 17(1), 147–159 (2015) 8. Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimiento 6(1), 1–17 (2009) 9. Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). 10. Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson (2004). ISBN 8420540250 11. Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2009) 12. Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Designing data warehouses: from business requirement analysis to multidimensional modeling. In: Proceedings of the 1st International Workshop on Requirements Engineering for Business Need and IT Alignment, Paris, France (2005) 13. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996) 14. Kuan, C.M.: Artificial neural networks. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2008) 15. Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015) 16. Parthasarathy, S., Zaki, M.J., Ogihara, M.: Parallel data mining for association rules on shared-memory systems. Knowl. Inf. Syst. Int. J. 3(1), 1–29 (2001) 17. Sekmen, F., Kurkcu, M.: An early warning system for Turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Financ. Rev. 4(1), 529–543 (2014) 18. Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014) 19. Singhal, D., Swarup, K.S.: Electricity price forecasting using artificial neural networks. IJEPE 33(1), 550–555 (2011) 20. Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12 (11), 2963–2965 (2017) 21. Vásquez, C., et al.: Cluster of the latin american universities top100 according to webometrics 2017. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 276–283. Springer, Cham (2018). 22. Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: 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.) DMBD 2018. LNCS, vol. 10943, pp. 670–679. Springer, Cham (2018). 23. Prodromidis, A., Chan, P.K., Stolfo, S.J.: Meta learning in distributed data mining systems: Issues and approaches. In: Kargupta, H., Chan, P. (eds.) Book on Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press (2000) 24. Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for data mining association rules in large databases. In: Proceedings of 21st Very Large Data Base Conference, vol. 5, no. 1, pp. 432– 444 (1995) 25. Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W.: Java agents for metalearning over distributed databases. In: Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining, vol. 5, no. 2, pp. 74–81 (1997)spa

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International