Data mining applied in school dropout prediction
In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the pe...
- Autores:
-
amelec, viloria
García Guliany, Jesús
Niebles Núñez, William
H, H
Niebles Nuñez, Leonardo David
- 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:11323/6184
- Acceso en línea:
- https://hdl.handle.net/11323/6184
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data mining
School dropout
Educational Data Mining (EDM)
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Data mining applied in school dropout prediction |
| title |
Data mining applied in school dropout prediction |
| spellingShingle |
Data mining applied in school dropout prediction Data mining School dropout Educational Data Mining (EDM) |
| title_short |
Data mining applied in school dropout prediction |
| title_full |
Data mining applied in school dropout prediction |
| title_fullStr |
Data mining applied in school dropout prediction |
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Data mining applied in school dropout prediction |
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Data mining applied in school dropout prediction |
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amelec, viloria García Guliany, Jesús Niebles Núñez, William H, H Niebles Nuñez, Leonardo David |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria García Guliany, Jesús Niebles Núñez, William H, H Niebles Nuñez, Leonardo David |
| dc.subject.spa.fl_str_mv |
Data mining School dropout Educational Data Mining (EDM) |
| topic |
Data mining School dropout Educational Data Mining (EDM) |
| description |
In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5]. |
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2020-04-14T20:32:04Z |
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2020 |
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eng |
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[1] L. A. Alvares Aldaco, “Comportamiento de la Deserción y Reprobación en el Colegio de Bachilleres del Estado de Baja California: Caso Plantel Ensenada”, X Congreso Nacional de Investigación Educativa. México, 2009. [2] F. Araque, C. Roldán, A. Salguero, “Factors Influencing University Drop Out Rates”, Computers & Education, vol. 53, pp. 563–574, 2009. [3] M. N. Quadril and N. V. Kalyankar, “Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques”, Global Journal of Computer Science and Technology, vol. 10, pp. 2-5, 2010. [4] C. Romero and S. Ventura, “Educational data mining: A Survey From 1995 to 2005”, Expert System with Applications, vol. 33, pp. 135-146, 2007. [5] M. M. Hernández, “Causas del Fracaso Escolar”, XIII Congreso de la Sociedad Española de Medicina del Adolescente, pp.1-5. 2002. [6] E. Espíndola, A. León, “La Deserción Escolar en América Latina un Tema Prioritario Para la Agenda Regional”, Revista Iberoamericana de Educación, no. 30, pp. 1-17, 2002. [7] I. H. Witten and F. Eibe, “Data Mining, practical Machine Learning Tools and Techniques”, Second Edition, Morgan Kaufman Publishers, 2005. [8] M. A. Hall and G. Holmes, “Benchmarking Attribute Selection Techniques for Data Mining”, Technical Report 00/10, University of Waikato, Department of Computer Science, Hamilton, New Zealand, Julio 2002. Available: http://www.cs.waikato.ac.nz/~ml/publications/2000/00MHGHBenchmarking.pdf. [9] N. V. Chawla, K. W. Bowyer, L. O. Hall, W.P. Kegelmeyer, “Synthetic Minority Over-sampling Technique”, Journal of Artificial Intelligence Research, 2002, 16:321-357. [10] J. Cendrowska, “PRISM: An algorithm for inducing modular rules”, International Journal of ManMachine Studies, vol. 27, no. 4, pp. 349-370, 1987. [11] J. R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufman Publishers, 1993. [12] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, “Classification and Regression Trees”, Chapman & Hall, New York, 1984. [13] Y. Freund and L. Mason, “The Alternating Decision Tree Algorithm”, Proceedings of the 16th International Conference on Machine Learning, pp. 124-133, 1999Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into realworld applications. Birmingham: Packt Publ; 2013. [14] 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. [15] 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. [16] 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. [17] 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, Cham [18] 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. [19] Hox, J., & Maas, C. (2005). Multilevel analysis. Encyclopedia of Social Measurement, 2, 785– 793. doi: 10.1016/B0-12-369398-5/00560-0 [20] Mellado A., Suárez, N., Altimir, C., Martínez, C., Pérez J. C., Krause, M., & Horvath, A. (2017) Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes. Psychotherapy Research, 27(5), 595-607. doi: 10.1080/10503307.2016.1147657 [21] Ogles, B. M. (2013). Measuring change in psychotherapy research. En M. J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change (pp.134– 166). New Jersey: Wiley. [22] Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd Ed.). Thousand Oaks, California: Sage. [23] Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R. T., & du Toit, M. (2011). HLM7: [24] Hierarchical Linear and Nonlinear Modeling. Chicago, IL: Scientific Software International. [25] Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Boca Raton: Chapman & Hall/CRC [26] AlShammari, I., Aldhafiri, M., & Al-Shammari, Z. (2013).A Meta-Analysis of Educational Data Mining on Improvements in Learning Outcomes. College Student Journal, 47(2), 326-333. [27] Baker, R. S. 1. (2011). Data mining for education. In International encyclopedia of education. 3rd ed. Oxford, UK: Elsevier. |
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amelec, viloria2f22a05451ff1bbfc2d4dd00035c952fGarcía Guliany, Jesús38d903c035ad000ae5478bfcd97f6705Niebles Núñez, William16e7911c93826189c8c01f9f8591e9d4H, Ha50768e271729098aea43bcf7439850cNiebles Nuñez, Leonardo David9b60381984d50491485a21d84cc827742020-04-14T20:32:04Z2020-04-14T20:32:04Z20201742-65881742-6596https://hdl.handle.net/11323/6184doi:10.1088/1742-6596/1432/1/012092Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5].engJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data miningSchool dropoutEducational Data Mining (EDM)Data mining applied in school dropout predictionArtículo 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/acceptedVersion[1] L. A. Alvares Aldaco, “Comportamiento de la Deserción y Reprobación en el Colegio de Bachilleres del Estado de Baja California: Caso Plantel Ensenada”, X Congreso Nacional de Investigación Educativa. México, 2009.[2] F. Araque, C. Roldán, A. Salguero, “Factors Influencing University Drop Out Rates”, Computers & Education, vol. 53, pp. 563–574, 2009.[3] M. N. Quadril and N. V. Kalyankar, “Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques”, Global Journal of Computer Science and Technology, vol. 10, pp. 2-5, 2010.[4] C. Romero and S. Ventura, “Educational data mining: A Survey From 1995 to 2005”, Expert System with Applications, vol. 33, pp. 135-146, 2007.[5] M. M. Hernández, “Causas del Fracaso Escolar”, XIII Congreso de la Sociedad Española de Medicina del Adolescente, pp.1-5. 2002.[6] E. Espíndola, A. León, “La Deserción Escolar en América Latina un Tema Prioritario Para la Agenda Regional”, Revista Iberoamericana de Educación, no. 30, pp. 1-17, 2002.[7] I. H. Witten and F. Eibe, “Data Mining, practical Machine Learning Tools and Techniques”, Second Edition, Morgan Kaufman Publishers, 2005.[8] M. A. Hall and G. Holmes, “Benchmarking Attribute Selection Techniques for Data Mining”, Technical Report 00/10, University of Waikato, Department of Computer Science, Hamilton, New Zealand, Julio 2002. Available: http://www.cs.waikato.ac.nz/~ml/publications/2000/00MHGHBenchmarking.pdf.[9] N. V. Chawla, K. W. Bowyer, L. O. Hall, W.P. Kegelmeyer, “Synthetic Minority Over-sampling Technique”, Journal of Artificial Intelligence Research, 2002, 16:321-357.[10] J. Cendrowska, “PRISM: An algorithm for inducing modular rules”, International Journal of ManMachine Studies, vol. 27, no. 4, pp. 349-370, 1987.[11] J. R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufman Publishers, 1993.[12] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, “Classification and Regression Trees”, Chapman & Hall, New York, 1984.[13] Y. Freund and L. Mason, “The Alternating Decision Tree Algorithm”, Proceedings of the 16th International Conference on Machine Learning, pp. 124-133, 1999Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into realworld applications. Birmingham: Packt Publ; 2013.[14] 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.[15] 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.[16] 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.[17] 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, Cham[18] 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.[19] Hox, J., & Maas, C. (2005). Multilevel analysis. Encyclopedia of Social Measurement, 2, 785– 793. doi: 10.1016/B0-12-369398-5/00560-0[20] Mellado A., Suárez, N., Altimir, C., Martínez, C., Pérez J. C., Krause, M., & Horvath, A. (2017) Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes. Psychotherapy Research, 27(5), 595-607. doi: 10.1080/10503307.2016.1147657[21] Ogles, B. M. (2013). Measuring change in psychotherapy research. En M. J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change (pp.134– 166). New Jersey: Wiley.[22] Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd Ed.). Thousand Oaks, California: Sage.[23] Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R. T., & du Toit, M. (2011). HLM7:[24] Hierarchical Linear and Nonlinear Modeling. Chicago, IL: Scientific Software International.[25] Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Boca Raton: Chapman & Hall/CRC[26] AlShammari, I., Aldhafiri, M., & Al-Shammari, Z. (2013).A Meta-Analysis of Educational Data Mining on Improvements in Learning Outcomes. College Student Journal, 47(2), 326-333.[27] Baker, R. S. 1. (2011). Data mining for education. In International encyclopedia of education. 3rd ed. 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