Development of software to predict academic performance using data mining techniques and tools

Abstract

Student desertion due to academic performance is understood as the interruption that a student makes voluntarily or by warning of his academic continuity by not achieving an acceptable accumulated average that keeps him active within his curriculum. The objective of this research was to develop a software that allows from the extraction of the data to the prediction of the academic performance of the students of the systems engineering program of the Universidad Francisco de Paula Santander, Ocana, Colombia, in order to create strategies to reduce student dropout rates within the career. The methodology used was qualitative research with a descriptive approach. The life cycle in the development of the software whose name is Universidad Francisco de Paula Santander, Ocana, Colombia, desertion was followed with the merger between open up and the knowledge extraction process. The type of learning used was supervised, obtaining that the highest percentage of correctly classified instances was achieved with algorithm randomizable filtered classifiers as well as J48 with a percentage of 92.6307% and 79.3185%, respectively. In addition, this software is applicable for any area in science and academia, since they worked with standard variables of which, the attributes with the highest incidence in low academic performance are, student status, academic sanction, the assessment of the proof of status, age, gender, social status and place of origin.