Alcover, R., Benlloch, J., Blesa, P., Calduch, M.A., Celma, M., Ferri, C., et al. (2007) Análisis del rendimiento académico en los estudios de informática de la Universidad Politécnica de Valencia aplicando técnicas de minería de datos. Pace Pacing and Clinical Electrophysiology, 164, 170.

has been cited by the following article:

• TITLE: Evaluation of Prediction Algorithms in the Student Dropout Problem

  AUTHORS: Luis Earving Lee, Salvador Ibarra Martínez, José Antonio Castán Rocha, Jesús David Terán Villanueva, Julio Laria Menchaca, Mayra Guadalupe Treviño Berrones, Emilio Castán Rocha

  KEYWORDS: Student Dropout, Prediction Algorithms, J48, K-Near Neighbors, Support Vector Machine

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.3, March 5, 2020

  ABSTRACT: University dropout is a growing problem which, in recent years, is using computer techniques to assist in the detection process. The paper presents the evaluation of some prediction algorithms to detect a student with a high possibility of scholar desertion. The approach uses real data from past scholar periods to create a dataset with different information of the students (i.e., personal, economic, and academic records). The algorithms selected in the experimental phase were: J48 decision tree, K-near neighbors, and support vector machine. We use two similarity metrics to split the dataset with cases with at least 80% of similarity to evaluate each case. We use the data from 2010 to 2016 with real students’ information to predict if there exists the possibility of a real academic dropout in one test for a period. The results show that the J48 algorithm reaches a better performance in both experiments. Besides, the tree generated for each student is taken as a path of attention, reaching around 88% of effectiveness. Finally, the conclusions argue the contributions of the paper and propose a future line of research.