Prediction of academic dropout in university students using data mining: Engineering case

Student dropout is considered an important indicator for measuring social mobility and reflecting the social contribution that universities offer. In economic terms, there is evidence that students attribute their decision to defect from their academic programs because of their economic situation. D...

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Autores:
Silva, Jesús
Arrieta Matos, Luisa Fernanda
Medina Mosquera, Claudia
Vargas Mercado, Carlos
Barrios González, Rosio
Orellano Llinás, Nataly
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7781
Acceso en línea:
https://hdl.handle.net/11323/7781
https://doi.org/10.1007/978-981-15-3125-5_49
https://repositorio.cuc.edu.co/
Palabra clave:
Student dropout
Classification based on decision trees
Optimization
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/7781
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Prediction of academic dropout in university students using data mining: Engineering case
title Prediction of academic dropout in university students using data mining: Engineering case
spellingShingle Prediction of academic dropout in university students using data mining: Engineering case
Student dropout
Classification based on decision trees
Optimization
title_short Prediction of academic dropout in university students using data mining: Engineering case
title_full Prediction of academic dropout in university students using data mining: Engineering case
title_fullStr Prediction of academic dropout in university students using data mining: Engineering case
title_full_unstemmed Prediction of academic dropout in university students using data mining: Engineering case
title_sort Prediction of academic dropout in university students using data mining: Engineering case
dc.creator.fl_str_mv Silva, Jesús
Arrieta Matos, Luisa Fernanda
Medina Mosquera, Claudia
Vargas Mercado, Carlos
Barrios González, Rosio
Orellano Llinás, Nataly
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Arrieta Matos, Luisa Fernanda
Medina Mosquera, Claudia
Vargas Mercado, Carlos
Barrios González, Rosio
Orellano Llinás, Nataly
Pineda, Omar
dc.subject.spa.fl_str_mv Student dropout
Classification based on decision trees
Optimization
topic Student dropout
Classification based on decision trees
Optimization
description Student dropout is considered an important indicator for measuring social mobility and reflecting the social contribution that universities offer. In economic terms, there is evidence that students attribute their decision to defect from their academic programs because of their economic situation. Dropout causes significant waging gaps among people who complete their tertiary studies compared to those who do not, leading to a lack of skilled human capital that pays greater productivity to economic development of a country. Given the above, the objective of this study is to present a tree-based classification of decisions (CBAD) with optimized parameters to predict the dropout of students at Colombian universities. The study analyses 10,486 cases of students from three private universities with similar characteristics. The result of the application of this technique with optimized parameters achieved a precision ratio of 88.14%.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-28T12:56:03Z
dc.date.available.none.fl_str_mv 2021-01-28T12:56:03Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-3125-5_49
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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url https://hdl.handle.net/11323/7781
https://doi.org/10.1007/978-981-15-3125-5_49
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1):159–175
2. Duan L, Xu L, Liu Y, Lee J (2009) Cluster-based outlier detection. Ann Oper Res 168(1):151–168
3. Haykin S (1999) Neural networks a comprehensive foundation, 2nd edn. Macmillan College Publishing, Inc., New York. ISBN: 9780023527616
4. Haykin S (2009) Neural networks and learning machines. Prentice Hall International, London, NJ
5. Isasi P, Galván I (2004) Redes de neuronas artificiales. Un enfoque Práctico. Pearson, London. ISBN: 8420540250
6. Kulkarni S, Haidar I (2009) Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int J Comput Sci Inf Secur 2(1):81–89
7. Mazón JN, Trujillo J, Serrano M, Piattini M (2005) 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
8. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Berlin
9. Pineda Lezama O, Gómez Dorta R (2017) Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J Sci Technol 5(2):61–75
10. Viloria A, Lis-Gutierrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (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, Berlin
11. Ben Salem S, Naouali S, Chtourou Z (2018) A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput Electronic Eng 68:463–483.
12. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted gaussian algorithm means. Stat Probab Lett 137:148–156.
13. Abhay KA, Badal NA (2015) Novel approach for intelligent distribution of data warehouses. Egypt Inform J 17(1):147–159
14. Aguado-López E, Rogel-Salazar R, Becerril-García A, Baca-Zapata G (2009) 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
15. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure M-A, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–73
16. Parthasarathy S et al (2001) Parallel data mining for association rules on shared-memory systems. Knowl Inf Syst 3(1):1–29
17. Grossman RL, Bailey SM, Sivakumar H, Turinsky AL (1999) Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proceedings of ACM/IEEE conference on supercomputing, Article No 63
18. Chattratichat J, Darlington J, Guo Y, Hedvall S, Kohler M, Syed J (1999) An architecture for distributed enterprise data mining. In: Proceedings of 7th international conference on high performance computing and networking, Netherlands, 12–14 Apr, pp 573–582
19. Wang L et al (2013) G-hadoop: MapReduce across distributed data centers for data-intensive computing. Futur Gener Comput Syst 29(3):739–750
20. Butenhof DR (1997) Programming with POSIX threads. Addison-Wesley, Boston
21. Bhaduri K, Wolf R, Giannella C, Kargupta H (2008) Distributed decision-tree induction in peer-to-peer systems. Stat Anal Data Min 1(2):85–103
22. Rafailidis D, Kefalas P, Manolopoulos Y (2017) Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst Appl 74:11–18
23. Vásquez C, Torres M, Viloria A (2017) 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
24. Aguado-López E, Rogel-Salazar R, Becerril-García A, Baca-Zapata G (2009) Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimento 6(1):1–17
25. Torres-Samuel M, Vásquez C, Viloria A, Lis-Gutiérrez JP, Borrero TC, Varela N (2018) Web visibility profiles of top 100 Latin American universities. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Berlin
26. Caicedo EJC, Guerrero S, López D (2016) Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística 9(1):93–106 (85–97 English)
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spelling Silva, JesúsArrieta Matos, Luisa FernandaMedina Mosquera, ClaudiaVargas Mercado, CarlosBarrios González, RosioOrellano Llinás, NatalyPineda, Omar2021-01-28T12:56:03Z2021-01-28T12:56:03Z2020https://hdl.handle.net/11323/7781https://doi.org/10.1007/978-981-15-3125-5_49Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Student dropout is considered an important indicator for measuring social mobility and reflecting the social contribution that universities offer. In economic terms, there is evidence that students attribute their decision to defect from their academic programs because of their economic situation. Dropout causes significant waging gaps among people who complete their tertiary studies compared to those who do not, leading to a lack of skilled human capital that pays greater productivity to economic development of a country. Given the above, the objective of this study is to present a tree-based classification of decisions (CBAD) with optimized parameters to predict the dropout of students at Colombian universities. The study analyses 10,486 cases of students from three private universities with similar characteristics. The result of the application of this technique with optimized parameters achieved a precision ratio of 88.14%.Silva, JesúsArrieta Matos, Luisa FernandaMedina Mosquera, ClaudiaVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600Barrios González, RosioOrellano Llinás, NatalyPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_49Student dropoutClassification based on decision treesOptimizationPrediction of academic dropout in university students using data mining: Engineering caseArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1):159–1752. Duan L, Xu L, Liu Y, Lee J (2009) Cluster-based outlier detection. Ann Oper Res 168(1):151–1683. Haykin S (1999) Neural networks a comprehensive foundation, 2nd edn. Macmillan College Publishing, Inc., New York. ISBN: 97800235276164. Haykin S (2009) Neural networks and learning machines. Prentice Hall International, London, NJ5. Isasi P, Galván I (2004) Redes de neuronas artificiales. Un enfoque Práctico. Pearson, London. ISBN: 84205402506. Kulkarni S, Haidar I (2009) Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int J Comput Sci Inf Secur 2(1):81–897. Mazón JN, Trujillo J, Serrano M, Piattini M (2005) 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, France8. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Berlin9. Pineda Lezama O, Gómez Dorta R (2017) Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J Sci Technol 5(2):61–7510. Viloria A, Lis-Gutierrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (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, Berlin11. Ben Salem S, Naouali S, Chtourou Z (2018) A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput Electronic Eng 68:463–483.12. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted gaussian algorithm means. Stat Probab Lett 137:148–156.13. Abhay KA, Badal NA (2015) Novel approach for intelligent distribution of data warehouses. Egypt Inform J 17(1):147–15914. Aguado-López E, Rogel-Salazar R, Becerril-García A, Baca-Zapata G (2009) 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):1–1715. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure M-A, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–7316. Parthasarathy S et al (2001) Parallel data mining for association rules on shared-memory systems. Knowl Inf Syst 3(1):1–2917. Grossman RL, Bailey SM, Sivakumar H, Turinsky AL (1999) Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proceedings of ACM/IEEE conference on supercomputing, Article No 6318. Chattratichat J, Darlington J, Guo Y, Hedvall S, Kohler M, Syed J (1999) An architecture for distributed enterprise data mining. In: Proceedings of 7th international conference on high performance computing and networking, Netherlands, 12–14 Apr, pp 573–58219. Wang L et al (2013) G-hadoop: MapReduce across distributed data centers for data-intensive computing. Futur Gener Comput Syst 29(3):739–75020. Butenhof DR (1997) Programming with POSIX threads. Addison-Wesley, Boston21. Bhaduri K, Wolf R, Giannella C, Kargupta H (2008) Distributed decision-tree induction in peer-to-peer systems. Stat Anal Data Min 1(2):85–10322. Rafailidis D, Kefalas P, Manolopoulos Y (2017) Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst Appl 74:11–1823. Vásquez C, Torres M, Viloria A (2017) 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):2963–296524. Aguado-López E, Rogel-Salazar R, Becerril-García A, Baca-Zapata G (2009) Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimento 6(1):1–1725. Torres-Samuel M, Vásquez C, Viloria A, Lis-Gutiérrez JP, Borrero TC, Varela N (2018) Web visibility profiles of top 100 Latin American universities. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Berlin26. Caicedo EJC, Guerrero S, López D (2016) Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística 9(1):93–106 (85–97 English)PublicationORIGINALPrediction of academic dropout in university students using data mining Engineering case.pdfPrediction of academic dropout in university students using data mining Engineering case.pdfapplication/pdf97651https://repositorio.cuc.edu.co/bitstreams/379857d4-f364-4c00-a0be-99e146671c43/downloadde22b64b3517f7b45c2c0de46b39e082MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/71555c2a-6a5e-4073-8811-d7ef483e624a/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/a5f0c5b5-1c51-4b1b-8c6d-b5439eaff742/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILPrediction of academic dropout in university students using data mining Engineering case.pdf.jpgPrediction of academic dropout in university students using data mining Engineering 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