Data-Driven Approaches to Student Retention: Analysis of Dropout Trends in the Measurements Laboratory

Abstract

This paper presents a data-driven investigation of student dropout in the Measurements laboratory, a core course in electrical engineering education. By analysing decades of performance and motivation data, with a focus on recent years, we identified key social, Curricular, and infrastructural factors influencing dropout. We highlight student attitudes and instructional conditions that strongly affect retention. The research aims to reveal intervention strategies that foster active student participation and competence development while maintaining academic standards and promoting long-term retention.

This paper presents a data-driven investigation of student dropout in the Measurements laboratory, a core course in electrical engineering education. By analysing decades of performance and motivation data, with a focus on recent years, we identified key social, Curricular, and infrastructural factors influencing dropout. We highlight student attitudes and instructional conditions that strongly affect retention. The research aims to reveal intervention strategies that foster active student participation and competence development while maintaining academic standards and promoting long-term retention.