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ERIC Number: EJ1363504
Record Type: Journal
Publication Date: 2023
Pages: 22
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1467-6370
EISSN: EISSN-1758-6739
Statistical Modelling of the Impact of Online Courses in Higher Education on Sustainable Development
Arango-Uribe, Marta Luz; Barrera-Causil, Carlos Javier; Pallares, Vladimir; Rojas, Jessica Maria; Mercado Díaz, Luís Roberto; Marrone, Rebecca; Marmolejo-Ramos, Fernando
International Journal of Sustainability in Higher Education, v24 n2 p404-425 2023
Purpose: The concept of sustainable development (SD) is a popular response to society's need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is "education quality" (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental. Design/methodology/approach: A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD. Findings: One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity. Research limitations/implications: The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size. Originality/value: This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.
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Publication Type: Journal Articles; Reports - Research; Tests/Questionnaires
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Spain
Grant or Contract Numbers: N/A