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Title

Whether perceived TPACK could improve deep learning? Through the lens of the mediating role of self-regulatory learning and the moderating role of technology self-efficacy in the online environment.

Authors

Zhao, Jingxian; Liu, Enyun; Sofeia, Noorzareith

Abstract

Digitization of the higher education system and artificial intelligence embedding are essential to promote students' deep learning. Teachers also widely use digital technology in the online environment. However, whether students can achieve deep learning is still a problem online. Therefore, this study aims to investigate whether perceived TPACK could influence deep learning, and through the lens of the mediating effect of self-regulatory learning and the moderating effect of technology self-efficacy, investing influences factors in exploring how to influence deep learning and achieve deeper learning. This study used a Wenjuanxing e-questionnaire platform collecting 400 students online learning questionnaires, three universities, and freshmen (n = 170, 42.5%), sophomores (n = 52, 13.0%), juniors (n = 160, 40.0%), and seniors (n = 18, 4.5%), and then, applying SPSS 26.0 and AMOS 24.0 to test the reliability and validity of the instruments with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and to test moderating and mediating effect with correlation analysis, structural equation model (SEM) and regression analysis. Perceived TPACK has a significant effect on deep learning; self-regulatory learning has a mediating effect between perceived TPACK and deep learning; technology self-efficacy has a negatively moderating effect between perceived TPACK and self-regulatory learning. However, technology self-efficacy has no moderating effect between perceived TPACK and deep learning. Technology self-efficacy significantly impacts self-regulatory and deep learning and can be used as a substitute for perception teachers' TPACK. This study's results make contributions to the growing literature and theory about these four variables, as well as beneficial to the practical online teaching and learning environment, which provides some influence perspective on how to improve online teaching and learning.

Subjects

DEEP learning; EXPLORATORY factor analysis; ARTIFICIAL intelligence; STRUCTURAL equation modeling; CONFIRMATORY factor analysis; ONLINE education

Publication

Current Psychology, 2024, Vol 43, Issue 37, p29848

ISSN

1046-1310

Publication type

Academic Journal

DOI

10.1007/s12144-024-06455-x

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