School database Processing from the perspective of artificial neural networks

CIENCIA ergo-sum

View Publication Info
 
 
Field Value
 
Title School database Processing from the perspective of artificial neural networks
Procesamiento de bases de datos escolares por medio de redes neuronales artificiales
 
Creator Miranda García, Brenda
González Bárcenas, Víctor Manuel
Reyes Nava, Adriana
Alejo Eleuterio, Roberto
Rendón Lara, Eréndira
 
Description The study of school databases is an area that has been not significantly studied and has been questioned from the point of view of data mining or artificial intelligence. Currently, there are some works that show their processing through machine learning algorithms or the so called “smart” algorithms; however, this do not stop to analyze the relevance of processing qualitative data as if they were quantitative. In this work, this problem is studied with the use of three neuronal network models. Results showed the ability of these models to classify, with a high degree of quality, the correct trends in students using mainly qualitative data.
El estudio de bases de datos escolares es un área que ha sido poco estudiada y cuestionada desde el punto de vista de la minería de datos o la inteligencia artificial, actualmente, existen algunos trabajos que muestran su procesamiento mediante algoritmos de aprendizaje automático o “inteligentes”, sin embargo, no se detienen a analizar la pertinencia de procesar datos cualitativos como si fueran cuantitativos. En este trabajo, se estudia este problema con el uso de tres modelos de red neuronal. Los resultados evidencian la capacidad de estos modelos para clasificar con un alto grado de acierto tendencias en los estudiantes, utilizando principalmente datos cualitativos.
 
Publisher Universidad Autónoma del Estado de México
 
Date 2020-10-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artículo revisado por pares
 
Format application/pdf
text/html
application/zip
application/zip
 
Identifier https://cienciaergosum.uaemex.mx/article/view/13136
10.30878/ces.v27n3a11
 
Source CIENCIA ergo-sum; Vol. 27 Núm. 3 (2020): CIENCIA ergo-sum (noviembre 2020-febrero 2021)
CIENCIA ergo-sum; Vol. 27 Núm. 3 (2020): CIENCIA ergo-sum (noviembre 2020-febrero 2021)
2395-8782
1405-0269
 
Language spa
 
Relation https://cienciaergosum.uaemex.mx/article/view/13136/11474
https://cienciaergosum.uaemex.mx/article/view/13136/11494
https://cienciaergosum.uaemex.mx/article/view/13136/11495
https://cienciaergosum.uaemex.mx/article/view/13136/11496
/*ref*/Alejo, R., Monroy, J., Ambriz, J. C., & Pacheco-Sánchez, J. H. (2017). An improved dynamic sampling back-propagation algorithm based on mean square error to face the multiclass imbalance problem. Neural Computing and Applications, 28(10), 2843-2857.
/*ref*/Agarwal, B., Nayak, R., Mittal, N., & Patnaik, S. (2020). Deep learning-based approaches for sentiment analysis. Springer Singapore.
/*ref*/Benítez, J. M., Castro, J. L., & Requena, I. (1997). Are artificial neural networks black boxes? IEEE transactions on neural networks, 8(5), 1156-1164.
/*ref*/Duda, R., Hart, P., & Stork, D. (2001). Pattern Classification and Scene Analysis. New York.
/*ref*/Eckert, K. B. y Suénaga, R. (2015). Análisis de deserción permanencia de estudiantes universitarios utilizando técnica de clasificación en minería de datos. Formación universitaria, 8(5), 3-12.
/*ref*/Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861-874.
/*ref*/Fernández, A., del Río, S., Chawla, N. V., & Herrera, F. (2017). An insight into imbalanced big data classification: Outcomes and challenges. Complex & Intelligent Systems, 3(2), 105-120.
/*ref*/Fernandez, A., García, S., & Herrera, F. (2018). SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research, 61(1), 863-905.
/*ref*/Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques. Morgan Kaufmann Publishers.
/*ref*/Haykin, S. (2009). Neural networks and learning machines. Hamilton: McMaster University.
/*ref*/Heredia, D., Amaya, Y., & Barrientos, E. (2015). Student dropout predictive model using data mining techniques. IEEE Latin America Transactions, 9(13), 3127-3134.
/*ref*/Jiménez, A. J. y Tamiran, R. S. (2015). Caracterización de la deserción estudiantil en educación superior con minería de datos. Revista Tecnológica ESPOL, 28(5), 447-463.
/*ref*/Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R., & Addison, L. K. (2015). A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1909-1918.
/*ref*/Leevy, L. J., Khoshgoftaar, M. T., Bauder, A. R., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1), 1-42.
/*ref*/López, G. E., Alejo, R., & Velázquez, J. (2015). Loyalty index of students, a view from the Tesjo stage with data mining. ECORFAN Journal, 2(2), 133-139.
/*ref*/López, V., Fernández, A., García, S., Palade, V., y Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113-141.
/*ref*/Mendiola, J. L. A., Valdovinos, R. M., Antonio, J. A. Alejo, R. y Marcial, R. (2015). “Análisis de deserción escolar con minería de datos,” Research in Computer Science, 93, 71-82.
/*ref*/Miranda, A. M. y Guzmán, J. (2017). Análisis de la deserción de estudiantes universitarios usando técnicas de minería de datos. Formación Universitaria, 61-68.
/*ref*/Naser, A. S., Zaqout, I., Ghosh, A. M., Atallah, R. R., & Alajrami, E. (2015). Predicting student performance using artificial neural network: In the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 221-228.
/*ref*/Oviedo, B., Zambrano-Vega, C., & Gómez, J. (2019). Clasificador bayesiano simple aplicado al aprendizaje. RISTI E18, 74-85.
/*ref*/Patri, R. C., Batista, G. E., & Monard, M. C. (2009). Data mining with imbalanced class distributions: Concepts and methods. Proceedings of the 4th Indian International Conference on Artificial Intelligence (359-376). IICAI: Tumkur.
/*ref*/Refaeilzadeh, P., Tang L., & Liu H. (2009) Cross-Validation. In L. Liu & M. T. Özsu (Eds.) Encyclopedia of Database Systems. Boston: Springer.
/*ref*/Reyes, N. A., Flores, F. A., Alejo, R. y Rendón, L. E. (2017). Minería de datos aplicada para la identificación de factores de riesgo en alumnos. Research in Computing Science, 177-189.
/*ref*/Solís, M., Moreira-Mora, T. E., González Laisa, R., Fernández-Martín, T., & Hernández-Jiménez, M. T. (2018).
/*ref*/Perspectives to predict dropout in university students with machine learning. In Proceedings of the 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), 1-6.
/*ref*/Tan, M., & Shao, P. (2014). Predicting dropout from online education based on neural networks. The Open Cybernetics and Systemics Journal, 263-627.
/*ref*/Tan, M., & Shao, P. (2015). Prediction of student dropout in E-Learning program through the use of machine learning method. International Journal of Emerging Technologies in Learning (iJET), 11-17.
/*ref*/Vásquez, A., Peláez, E. y Ochoa, X. (2015). Predictor basado en prototipos difusos y clasificación no supervisada. Revista Latinoamericana de Ingeniería de Software, 135-140.
/*ref*/Yukselturk, E., Ozekez, S., & Türel, K. Y. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open, Distance and e-Learning, 118-133.
 
Rights Derechos de autor 2020 CIENCIA ergo-sum
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library