Studying academic success: A data analytics approach to predict performance of higher education students

The dropout of students in higher education is a concern for universities, as it directly impacts the community and the educational level of future generations. For this reason, a data analytics-based model is proposed to support students in making decisions during the course selection process, aiming to guide them towards completing their degree while maximizing their performance. We have a dataset for three different majors in the Universidad de los Andes, (Systems and Computer Engineering, Industrial Engineering, and Economics), containing historical information about students, the courses they chose each semester in their specific curriculum, and their grades. Based on this data, the model analyzes the completed courses and the ones remaining for each student to fulfill their curriculum requirements. In this way, it creates a student profile that is used to calculate the probability of achieving certain grades in their next semester. Assuming this result, the process is iterated to develop a curriculum plan for the upcoming semesters. This outcome will provide students with a course guide for each semester, increasing their likelihood of achieving better performance in their studies.

• Autores: Martínez Osorio, Daniel Felipe
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2024
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/73243
• Acceso en línea: https://hdl.handle.net/1992/73243
• Palabra clave: Performance; Data analytics; Ingeniería
• Rights: openAccess
• License: Attribution 4.0 International