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.
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Zhu, Y., Li, M.H., Li, L., Huang, R.W., Zhang, J.H. (2023). An Investigation of Predictive Relationships Between University Students’ Online Learning Power and Learning Outcomes in a Blended Course. In: Radomir, L., Ciornea, R., Wang, H., Liu, Y., Ringle, C.M., Sarstedt, M. (eds) State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM). Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-34589-0_32
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