Abstract
Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer relevant data to the teacher. This paper reports the use of data mining techniques and Differential Evolution Clustering for discovering learning routes frequently applied in the Moodle Platform. Data were obtained form 4.115 university students monitored in an online course using Moodle 3.1. Firstly, students were grouped according to the data from a final qualifications report in a course. Secondly, the data of the Moodle logs about each cluster/group of students was used separately with the aim of obtaining more specific and precise models of the students behavior in the processes.
Keywords
- K-means
- Clustering
- Differential evolution
- Data mining
- Moodle
The Editors have retracted this conference paper [1] because it contains material that substantially overlaps with content translated from another article by different authors [2]. The authors Amelec Viloria, Tito Crissien Borrero, Jesús Vargas Villa, Maritza Torres, Nataly Orellano Llinas, and Karina Batista Zea agree to this retraction, the authors Jesús García Guiliany and Carlos Vargas Mercado have not responded to any correspondence from the editor/publisher about this retraction. Tito Crissien stated that he was not aware of this submission.
[1] Viloria, Amelec, et al. “Differential evolution clustering and data mining for determining learning routes in moodle.” International Conference on Data Mining and Big Data. Springer, Singapore, 2019. https://doi.org/10.1007/978-981-32-9563-6_18
[2] Vega, Alejandro Bogarín, Cristóbal Romero Morales, and Rebeca Cerezo Menéndez. “Aplicando minería de datos para descubrir rutas de aprendizaje frecuentes en Moodle.” Edmetic 5.1 (2016): 73–92. https://doi.org/10.21071/edmetic.v5i1.4017
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08 April 2021
The Editors have retracted this conference paper [1] because it contains material that substantially overlaps with content translated from another article by different authors [2]. The authors Jesús Silva, Alex Castro Sarmiento, Hugo Hernández P., and Ligia Romero agree to this retraction, the authors Nicolás María Santodomingo, Norka Márquez Blanco, Wilmer Cadavid Basto, Jorge Navarro Beltrán, and Juan de la Hoz Hernández have not responded to any correspondence from the editor/publisher about this retraction.
References
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
Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012)
Ben Salem, S., Naouali, S., Chtourou, Z.: A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463–483 (2018). https://doi.org/10.1016/j.compeleceng.2018.04.023
Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148–156 (2018). https://doi.org/10.1016/j.spl.2018.01.015
Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: a new approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034
Rahman, M.A., Islam, M.Z., Bossomaier, T.: ModEx and seed-detective: two novel techniques for high quality clustering by using good initial seeds in K-Means. J. King Saud Univ. - Comput. Inf. Sci. 27, 113–128 (2015). https://doi.org/10.1016/j.jksuci.2014.04.002
Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with K-means. Knowl.-Based Syst. 71, 345–365 (2014). https://doi.org/10.1016/j.knosys.2014.08.011
Ramadas, M., Abraham, A., Kumar, S.: FSDE-forced strategy differential evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci (2016). https://doi.org/10.1016/j.jksuci.2016.12.005
Yaqian, Z., Chai, Q.H., Boon, G.W.: Curvature-based method for determining the number of clusters. Inf. Sci. (2017). https://doi.org/10.1016/j.ins.2017.05.024
Tîrnăucă, C., Gómez-Pérez, D., Balcázar, J.L., Montaña, J.L.: Global optimality in k-means clustering. Inf. Sci. (Ny) 439–440, 79–94 (2018). https://doi.org/10.1016/j.ins.2018.02.001
Xiang, W., Zhu, N., Ma, S., Meng, X., An, M.: A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing (2015). https://doi.org/10.1016/j.neucom.2015.01.058
Garcia, A.J., Flores, W.G.: Automatic clustering using nature-inspired metaheuristics: a survey. Appl. Soft Comput (2016). https://doi.org/10.1016/j.asoc.2015.12.001
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans 38, 218–237 (2008). https://doi.org/10.1109/TSMCA.2007.909595
Costa, C., Alvelos, H., Teixeira, L.: The use of MOODLE e-learning platform: a study in a Portuguese University. Procedia Technology 5, 334–343 (2012)
El-Bahsh, R., Daoud, M.: Evaluating the use of MOODLE to achieve effective and interactive learning: a case study at the German Jordanian University. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, pp. 1–5 (2016)
Coll, S.D., Treagust, D.: Blended learning environment: an approach to enhance student’s learning experiences outside school (LEOS). MIER J. Educ. Stud. Trends Pract. 7, 2 (2018)
Kuo, R., Suryani, E., Yasid, A.: Automatic clustering combining differential evolution algorithm and k-means algorithm. In: Lin, Y.K., Tsao, Y.C., Lin, S.W. (eds.) Proceedings of the Institute of Industrial Engineers Asian Conference 2013, pp. 1207–1215. Springer, Singapore (2013). https://doi.org/10.1007/978-981-4451-98-7_143
Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017). https://doi.org/10.1016/j.swevo.2016.05.003
Kaya, I.: A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny) 179, 1552–1566 (2009). https://doi.org/10.1016/j.ins.2008.09.024
Dobbie, G., Sing, Y., Riddle, P., Ur, S.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014). https://doi.org/10.1016/j.swevo.2014.02.001
Departamento Administrativo Nacional de Estadística.: Página principal. Recuperado de:DANE (2018). http://www.dane.gov.co/
Torres-Samuel, M., Vásquez, C.L., Viloria, A., Varela, N., Hernández-Fernandez, L., Portillo-Medina, R.: Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188–199. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_18
Vásquez, 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)
Torres-Samuel, M., et al.: Efficiency analysis of the visibility of Latin American Universities and their impact on the ranking web. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 235–243. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_18
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Viloria, A. et al. (2019). RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_18
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DOI: https://doi.org/10.1007/978-981-32-9563-6_18
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