An Investigation of Predictive Relationships Between University Students’ Online Learning Power and Learning Outcomes in a Blended Course

Yue Zhu, Ming Hua Li, Lu Li, Rong Wei Huang and Jia Hua Zhang ()
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Yue Zhu: Zhejiang Normal University
Ming Hua Li: Zhejiang Normal University
Lu Li: Yantai Zhifu Wanhua Primary School
Rong Wei Huang: Linyi Hedong District Tangtou Neighborhood Gegou Central Primary School
Jia Hua Zhang: Zhejiang Normal University

A chapter in State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), 2023, pp 391-408 from Springer

Abstract: Abstract The researchers reported how university students’ online learning power could predict their learning outcomes in a blended course. The participants were 62 Chinese students enrolled in a university blended course combining face-to-face instruction and online learning tasks provided on the course platform. A questionnaire survey assessing students’ online learning power was administered among the participants. The data in relation to the participants’ weekly online learning tasks were retrieved from the course platform and indexed as their online course engagement. The participants’ learning outcomes were indicated by their overall course results and overall marks of online learning tasks, designing tasks, and IWB operation. SPSS and SmartPLS were used for data analysis. The factor analysis on the participants’ responses to the questionnaire scales indicated a five-factor solution for online learning power: (a) goal orientation, (b) resilience, (c) problem solving, and (d) metacognitive awareness. The results from PLS-SEM showed that problem solving and online course engagement directly predicted the participants’ overall marks of online learning tasks, which in turn affected their overall course results. As a driving force, goal orientation affected the participants’ overall marks of online learning tasks and this relationship was mediated through resilience and problem solving.

Keywords: Online learning power; Learning outcomes; Online course engagement; Blended learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-34589-0_32

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DOI: 10.1007/978-3-031-34589-0_32

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