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AuthorBen Said, Ahmed
AuthorAbdel-Salam, Abdel-Salam G.
AuthorAbu-Shanab, Emad
AuthorAlhazaa, Khalifa
Available date2023-10-08T08:41:46Z
Publication Date2022
Publication NameStudies in Computational Intelligence
ResourceScopus
ISSN1860949X
URIhttp://dx.doi.org/10.1007/978-3-031-05258-3_25
URIhttp://hdl.handle.net/10576/48314
AbstractDuring the outbreak of the Covid-19 pandemic, universities were forced to adopt technology and collaboration tools to reinforce online teaching and sustain their operations. This radical change pushes universities, researchers, educators, practitioners and decision makers to explore the perceptions of students and provide high quality online teaching operations. This study offers an understanding of the factors influencing students' satisfaction with online teaching. Using data from an institutional survey, a machine learning approach is developed along with feature importance analysis using Permutation Importance and SHAP. The two techniques yielded similar results, where quality, interaction, and comprehension were the most significant predictors of satisfaction while student class, gender and nationality were insignificant. Such results support previous research conducted on similar data but with different statistical techniques. Other factors might be significant in the online environment such as student support, academic experience, and assessment.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectFeature importance
Machine learning
Online learning
Student satisfaction
TitleFactors Affecting Student Satisfaction Towards Online Teaching: A Machine Learning Approach
TypeConference Paper
Pagination309-318
Volume Number1010
dc.accessType Abstract Only


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