Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education [Scholarly Article - Data, August 2022]

Title:

Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

 

Authors:

Joanna Alvarado-Uribe, Paola Mejía-Almada, Ana Luisa Masetto Herrera, Roland Molontay, Isabel Hilliger, Vinayak Hegde, José Enrique Montemayor Gallegos, Renato Armando Ramírez Díaz & Hector G. Ceballos

 

Published:

Data, 7(9), 25 August 2022

https://www.mdpi.com/2306-5729/7/9/119

Abstract:

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.

 

Scholarly Article (2020) - A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students

Title:
A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students

Author:
Clauirton Albuquerque Siebra (Federal University of Paraiba, Paraiba, Brazil), Ramon N. Santos (Federal University of Paraiba, Paraiba, Brazil) & Natasha C.Q. Lino (Federal University of Paraiba, Paraiba, Brazil)

Published:
International Journal of Distance Education Technologies (IJDET), 18(2), 2020

Available: https://www.igi-global.com/article/a-self-adjusting-approach-for-temporal-dropout-prediction-of-e-learning-students/248003

From the abstract:
"This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor."