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المؤلفBen Said, Ahmed
المؤلفAbdel-Salam, Abdel-Salam G.
المؤلفAbu-Shanab, Emad
المؤلفAlhazaa, Khalifa
تاريخ الإتاحة2023-10-08T08:41:46Z
تاريخ النشر2022
اسم المنشورStudies in Computational Intelligence
المصدرScopus
الرقم المعياري الدولي للكتاب1860949X
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/978-3-031-05258-3_25
معرّف المصادر الموحدhttp://hdl.handle.net/10576/48314
الملخصDuring 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.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعFeature importance
Machine learning
Online learning
Student satisfaction
العنوانFactors Affecting Student Satisfaction Towards Online Teaching: A Machine Learning Approach
النوعConference
الصفحات309-318
رقم المجلد1010
dc.accessType Abstract Only


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