Geographic Patterns of Academic Dropout and Socioeconomic Characteristics Using Clustering

Choque-Soto V.M.; Sosa-Jauregui V.D.; Ibarra W. · Lecture Notes in Networks and Systems (2023) · DOI: 10.1007/978-3-031-33258-6_47

Resumen

Academic dropout is a fundamental problem at all educational levels. Several studies have been conducted to identify the characteristics that motivate dropout. Few studies have found patterns related to location. The objective of this article is to show the geographic patterns of academic dropout of undergraduate university students and to analyze them in order to improve decision making in Peruvian universities. The methodology for this purpose is KDD (Knowledge Data Discovery) commonly known as data mining, which includes the Clustering technique and the application of the K means algorithm, one of the most popular and efficient algorithms. The results show outstanding geographical patterns such as the university site and district to which dropout students belong. The results help to identify the university campuses with the highest dropout rates and the districts from which the dropouts come. The latter is analyzed from a socioeconomic perspective.

Instituciones: Universidad Nacional de San Antonio Abad del Cusco