Técnicas de minería de datos para determinar la deserción escolar - Apaza-Tarqui, Alejandro; Borda-Navedos, Walter; Cayo, Noemí; Huanca-Suaquita, Jhon (2022)
The objective of this research was to determine the data mining techniques and the associated factors that allow the segmentation of students at risk of dropping out at the Instituto Superior Tecnológico Privado ISTEPSA, in Andahuaylas (Peru). For this purpose, Automatic Learning and Data Mining techniques implemented in WEKA software were applied: The CfsSubsetEval evaluation method and the BestFirst search method were applied to select the most significant factors, to establish the patterns the association algorithm A was used. priori and to segment, the Expected Value Maximization algorithm "Expectation Maximissation" (EM) and Kohonen's self-organizing maps (Self Organizing Maps, SOM) were used. The following results were obtained: 06 significant factors: Motivation of sessions, Laboratories and Classrooms of the Institution, Acceptance of the professional career, Repeated Courses in the school and Academic Semester; For dropout patterns, 100% of students who dropout rate motivation, classrooms, and laboratories as deficient; In addition, 96% consider the professional career they are studying to be deficient and 90% of those who withdraw are from the fourth semester; In the segmentation, 3 groups have been constructed with the EM algorithm and 4 groups for the SOM algorithm, where it is observed that the academic factors are decisive for the dropout of students.
Keywords: machine learning; knowledge extraction (KDD); student dropout; student segmentation