Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout
Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational t...
Main Authors: | Juan Pablo Salazar-Fernandez, Marcos Sepúlveda, Jorge Munoz-Gama, Miguel Nussbaum |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-02-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/4/1436 |
Similar Items
-
Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
by: Juan Pablo Salazar-Fernandez, et al.
Published: (2021-05-01) -
How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review
by: Catarina Félix de Oliveira, et al.
Published: (2021-11-01) -
Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques
by: Janka Kabathova, et al.
Published: (2021-04-01) -
Predictive analytics study to determine undergraduate students at risk of dropout
by: Andres Gonzalez-Nucamendi, et al.
Published: (2023-10-01) -
ADHE: A Tool to Characterize Higher Education Dropout Phenomenon
by: Oscar Daniel Rivera-Baena, et al.
Published: (2023-05-01)