Applied Sciences, Free Full-Text

Por um escritor misterioso

Last updated 14 abril 2025

This paper describes the application of Data Science and Educational Data Mining techniques to data from 4529 students, seeking to identify behavior patterns and generate early predictive models at the Universidad de la República del Uruguay. The paper describes the use of data from different sources (a Virtual Learning Environment, survey, and academic system) to generate predictive models and discover the most impactful variables linked to student success. The combination of different data sources demonstrated a high predictive power, achieving prediction rates with outstanding discrimination at the fourth week of a course. The analysis showed that students with more interactions inside the Virtual Learning Environment tended to have more success in their disciplines. The results also revealed some relevant attributes that influenced the students’ success, such as the number of subjects the student was enrolled in, the students’ mother’s education, and the students’ neighborhood. From the results emerged some institutional policies, such as the allocation of computational resources for the Virtual Learning Environment infrastructure and its widespread use, the development of tools for following the trajectory of students, and the detection of students at-risk of failure. The construction of an interdisciplinary exchange bridge between sociology, education, and data science is also a significant contribution to the academic community that may help in constructing university educational policies.

Applied Sciences An Open Access Journal from MDPI

Applied sciences Stock Photos, Royalty Free Applied sciences Images

Angewandte Chemie International Edition: Early View

UNCF & 3M Launch New Scholarship Program for St. Paul, MN Students to Attend HBCUs - UNCF

Applied Sciences

Applied Sciences An Open Access Journal from MDPI

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy - ScienceDirect