A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions
International Conference on Enterprise Information Systems, ICEIS - Proceedings
; 1:156-163, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20237560
ABSTRACT
Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
Artificial Intelligence; Covid-19; Decision Trees; Machine Leaning; Online Learning; Data mining; E-learning; Education computing; Learning systems; Machine learning; Students; Higher education institutions; Higher education students; Machine learning techniques; Predictive models; Student satisfaction; Teaching practices; Traditional educations
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
/
Revisiones
Idioma:
Inglés
Revista:
International Conference on Enterprise Information Systems, ICEIS - Proceedings
Año:
2023
Tipo del documento:
Artículo
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