Application of Data Mining Techniques for the Analysis of Academic Efficiency
Keywords:
data mining; data mining techniques; academic efficiency; clustering; decision tree.Abstract
The Institutions of Higher Education are immersed in periodic processes of evaluation and accreditation, that demand the fulfillment of minimum standards, being one of them the Academic Efficiency, integrated by the Student Retention indicator, which is determined by the number of students enrolled for the first time to the first year and are still studying two years later. The present study was carried out at the Agrarian University of Ecuador with the goal of predicting factors of higher incidence that generate student dropout in higher education, using Data Mining, and decision tree techniques and Clustering to obtain desertion behavior patterns. We worked with first year students of different careers at the Guayaquil and Milagro venues, during the academic periods 2014- 2015 and 2016, applying the Knowledge Discovery in Database (KKD) where the initial data is integrated, selected and transformed for the application of the chosen technique and are subsequently evaluated and spread to allow the corresponding authorities to make decisions that lead to the generation of action plans, which increase the student retention rate in the institution. With the application of the mentioned techniques it was concluded that the most relevant factor affecting the retention rate in the institution are the low averages obtained during the first semesters as well as the subjects approved, which lead to the loss of the year of the levels under study.
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