Higher Education during the Pandemic

The Predictive Factors of Learning Effectiveness in Covid-19 Online Learning

Bibliographic Data

ID22047366
AuthorsJenny Tsang (Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong), Jenny Tsun Yee Tsang (0000-0003-4558-9384, Education University of Hong Kong), Mike K P So (0000-0003-0781-8166, Hong Kong University of Science and Technology), Andy Chun Yin Chong (0000-0001-8679-9316, Hong Kong Metropolitan University), Andy Chong (School of Nursing and Health Studies, The Open University of Hong Kong, Ho Man Tin, Hong Kong), Benson S Y Lam (0000-0002-0836-4162, Hang Seng University of Hong Kong), Benson Lam (The Hang Seng University of Hong Kong), Amanda M Y Chu (0000-0002-9543-747X, Education University of Hong Kong, corresponding author)
Year2021
Volume11
Issue8
Pages446
Publication date2021-08-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci11080446
OpenAlexW3194044060
LanguageEN
Citations received27
References cited35

The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of perceived learning outcomes whereas instructor–student dialogue was a determinant of student initiative. University support had no significant relationship with either perceived learning outcomes or student initiative. In terms of learning effectiveness, both perceived learning outcomes and student initiative determined student satisfaction. The results identified that student–student dialogue, course design, and instructor–student dialogue were the key predictive factors of CoOL learning effectiveness, which may determine the ultimate success of CoOL

Blended Learning · Educational technology · Higher education · Mathematics education · Medical education · Multimedia · Online learning · Pandemic · Political science · Student engagement · Computer Science · COVID-19 and Mental Health · Innovative Teaching Methods · Medicine · Online and Blended Learning · Psychology · Artificial Intelligence

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