Using Big Data to determine potential dropouts in higher education
In 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...
- Autores:
-
amelec, viloria
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Nuñez, William
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:11323/5950
- Acceso en línea:
- https://hdl.handle.net/11323/5950
https://repositorio.cuc.edu.co/
- Palabra clave:
- Big Data
Dropouts
Higher education
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Using Big Data to determine potential dropouts in higher education |
| title |
Using Big Data to determine potential dropouts in higher education |
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Using Big Data to determine potential dropouts in higher education Big Data Dropouts Higher education |
| title_short |
Using Big Data to determine potential dropouts in higher education |
| title_full |
Using Big Data to determine potential dropouts in higher education |
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Using Big Data to determine potential dropouts in higher education |
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Using Big Data to determine potential dropouts in higher education |
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Using Big Data to determine potential dropouts in higher education |
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amelec, viloria Senior Naveda, Alexa Hernández Palma, Hugo Niebles Nuñez, William Niebles Nuñez, Leonardo David |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Senior Naveda, Alexa Hernández Palma, Hugo Niebles Nuñez, William Niebles Nuñez, Leonardo David |
| dc.subject.spa.fl_str_mv |
Big Data Dropouts Higher education |
| topic |
Big Data Dropouts Higher education |
| description |
In 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. |
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2020 |
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[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), 61-75 [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, Cham [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) [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) [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) [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) [7] James, G.; Witten, D.; Hastie, T.; Tibshirani, R.: An Introduction to Statistical Learning. Springer 7th Ed, pp. 25 (2014) [8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010) [9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015) [10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016). [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. [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. [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. [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, Cham [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. [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). [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). [18] Demšar, J., & Zupan, B. Orange: Data mining fruitful and fun-a historical perspective. Informatica, 37(1), 55-60. (2013). [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). |
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amelec, viloriaSenior Naveda, AlexaHernández Palma, HugoNiebles Nuñez, WilliamNiebles Nuñez, Leonardo David2020-01-30T13:42:58Z2020-01-30T13:42:58Z20201742-65881742-6596https://hdl.handle.net/11323/5950Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In 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.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Senior Naveda, AlexaHernández Palma, HugoNiebles Nuñez, WilliamNiebles Nuñez, Leonardo David-will be generated-orcid-0000-0003-2970-2498-600engJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012077/pdf[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), 61-75[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, Cham[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)[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)[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)[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)[7] James, G.; Witten, D.; Hastie, T.; Tibshirani, R.: An Introduction to Statistical Learning. Springer 7th Ed, pp. 25 (2014)[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).[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.[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.[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.[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, Cham[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.[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).[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).[18] Demšar, J., & Zupan, B. Orange: Data mining fruitful and fun-a historical perspective. Informatica, 37(1), 55-60. (2013).[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. 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