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) - Highlighting E‑learning Adoption Challenges using data Analysis Techniques: University of Kufa as a Case Study

Title:
Highlighting E‑learning Adoption Challenges using data Analysis Techniques: University of Kufa as a Case Study

Authors:
Ammar J.M. Karkar, Hayder K. Fatlawi & Ahmed A. Al-Jobouri

Published:
E-Journal of e-Learning (EJEL), volume 18, issue 2, February 2020
Available: file:///C:/Users/User/Downloads/ejel-volume18-issue2-article700.pdf

From the abstract:
"Electronic learning (e-learning) plays a significant role in improving the efficiency of the education process. However, in many cases in developing countries, technology transfer without consideration of technology acceptance factors has limited the impact of e-learning and the expected outcome of the education process. Therefore, this shift in learning method has been met with low enthusiasm from academic staff and students owing to its low perceived usefulness and perceived ease-of-use. The University of Kufa (UoK) in Iraq is considered a good case study because it has implemented the e-learning platform since 2013. The UoK platform is based on open-source Moodle owing to the latter’s advantages, such as low implementation cost, open community for support and continuous update and development. To identify and evaluate the challenges, this study uses a questionnaire survey that targets the level of adoption, implementation, familiarity and technology acceptance of staff and students."

Scholarly Article: An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Courses

Title:
An artificial neural network based early prediction of failure-prone students in blended learning courses

Authors:
Otgontsetseg Sukhbaatar, Tsuyoshi Usagawa, Lodoiravsal Choimaa

Published:
International Journal of Emerging Technologies in Learning (iJET), volume 14, issue 19, pp.77-92 (2019)
doi: https://doi.org/10.3991/ijet.v14i19.10366

From the abstract:
"One of the objectives of the performance measurement of grade-based higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of web-based learning systems in academic institutions revealed the posibility of evaluating student activities via these systems. In this paper, we propose an early prediction sceme to identify students at risk of failing in a blended learning course."