Data mining to identify risk factors associated with university students dropout

. 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...

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Autores:
Viloria Silva, Amelec Jesus
Castro Sarmiento, Alex
María Santodomingo, Nicolás
María Santodomingo, Nicolas Elias
Márquez Blanco, Norka
Cadavid Basto, Wilmer
Hernández P, Hugo
Navarro Beltrán, Jorge
de la Hoz Hernández, Juan
Romero, Ligia
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5225
Acceso en línea:
https://hdl.handle.net/11323/5225
https://repositorio.cuc.edu.co/
Palabra clave:
Knowledge extraction process
Tutoring
Decision making
Data mining
Rights
openAccess
License
Attribution-NonCommercial-ShareAlike 4.0 International
id RCUC2_ee9c3f81e254dd11f57b54128c97ef1d
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5225
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Data mining to identify risk factors associated with university students dropout
title Data mining to identify risk factors associated with university students dropout
spellingShingle Data mining to identify risk factors associated with university students dropout
Knowledge extraction process
Tutoring
Decision making
Data mining
title_short Data mining to identify risk factors associated with university students dropout
title_full Data mining to identify risk factors associated with university students dropout
title_fullStr Data mining to identify risk factors associated with university students dropout
title_full_unstemmed Data mining to identify risk factors associated with university students dropout
title_sort Data mining to identify risk factors associated with university students dropout
dc.creator.fl_str_mv Viloria Silva, Amelec Jesus
Castro Sarmiento, Alex
María Santodomingo, Nicolás
María Santodomingo, Nicolas Elias
Márquez Blanco, Norka
Cadavid Basto, Wilmer
Hernández P, Hugo
Navarro Beltrán, Jorge
de la Hoz Hernández, Juan
Romero, Ligia
dc.contributor.author.spa.fl_str_mv Viloria Silva, Amelec Jesus
Castro Sarmiento, Alex
María Santodomingo, Nicolás
María Santodomingo, Nicolas Elias
Márquez Blanco, Norka
Cadavid Basto, Wilmer
Hernández P, Hugo
Navarro Beltrán, Jorge
de la Hoz Hernández, Juan
Romero, Ligia
dc.subject.spa.fl_str_mv Knowledge extraction process
Tutoring
Decision making
Data mining
topic Knowledge extraction process
Tutoring
Decision making
Data mining
description . 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 dropout.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-08-31T03:07:51Z
dc.date.available.none.fl_str_mv 2019-08-31T03:07:51Z
dc.date.issued.none.fl_str_mv 2019-07-26
dc.type.spa.fl_str_mv Pre-Publicación
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1007/978-981-32-9563-6_5
dc.relation.references.spa.fl_str_mv 1. 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). https://doi.org/10.1007/978-3-642-36318-4_3 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). https://doi.org/10.1007/978-3-319-93803-5_26 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). https://doi.org/10.1007/978-3-319-93803-5_63 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)
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spelling Viloria Silva, Amelec JesusCastro Sarmiento, AlexMaría Santodomingo, NicolásMaría Santodomingo, Nicolas EliasMárquez Blanco, NorkaCadavid Basto, WilmerHernández P, HugoNavarro Beltrán, Jorgede la Hoz Hernández, JuanRomero, Ligia2019-08-31T03:07:51Z2019-08-31T03:07:51Z2019-07-261865-0929https://hdl.handle.net/11323/5225Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/. 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 dropout.Universidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria Latinoamericana.Viloria Silva, Amelec JesusCastro Sarmiento, AlexMaría Santodomingo, NicolásMaría Santodomingo, Nicolas EliasMárquez Blanco, NorkaCadavid Basto, WilmerHernández P, HugoNavarro Beltrán, Jorgede la Hoz Hernández, JuanRomero, LigiaengCommunications in Computer and Information Sciencehttps://doi.org/10.1007/978-981-32-9563-6_51. 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). https://doi.org/10.1007/978-3-642-36318-4_3 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). https://doi.org/10.1007/978-3-319-93803-5_26 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). https://doi.org/10.1007/978-3-319-93803-5_63 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. 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