Determinants of Active Online Learning in the Smart Learning Environment: An Empirical Study with PLS-SEM

Shaofeng Wang,Gaojun Shi,Ruyi Lin,Mingjie Lu,Junfeng Yang

doi:10.3390/su13179923

Shaofeng Wang, Gaojun Shi + Show 3 more

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https://doi.org/10.3390/su13179923

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Abstract

A smart learning environment, featuring personalization, real-time feedback, and intelligent interaction, provides the primary conditions for actively participating in online education. Identifying the factors that influence active online learning in a smart learning environment is critical for proposing targeted improvement strategies and enhancing their active online learning effectiveness. This study constructs the research framework of active online learning with theories of learning satisfaction, the Technology Acceptance Model (TAM), and a smart learning environment. We hypothesize that the following factors will influence active online learning: Typical characteristics of a smart learning environment, perceived usefulness and ease of use, social isolation, learning expectations, and complaints. A total of 528 valid questionnaires were collected through online platforms. The partial least squares structural equation modeling (PLS-SEM) analysis using SmartPLS 3 found that: (1) The personalization, intelligent interaction, and real-time feedback of the smart learning environment all have a positive impact on active online learning; (2) the perceived ease of use and perceived usefulness in the technology acceptance model (TAM) positively affect active online learning; (3) innovatively discovered some new variables that affect active online learning: Learning expectations positively impact active online learning, while learning complaints and social isolation negatively affect active online learning. Based on the results, this study proposes the online smart teaching model and discusses how to promote active online learning in a smart environment.

Highlights

Educational backgrounds will lead to different active online learning behaviors
Researchers are recommended to use partial least squares structural equation modeling (PLS-structural equation model (SEM)): When the study is exploratory in terms of theory development, multi-theoretical mixed models, testing of theoretical frameworks from a predictive perspective, testing of complex relationships with multiple variables, when the number of variables is large, when there is a lack of normal distribution, and when the sample size is small [36,90,94,95]
To measure the invariance of the model, according to the measurement invariance of composite models (MICOM) developed explicitly for PLS-SEM by Henseler et al [98], a three-step test is used to determine that the latent variables of different groups have the same connotation

Summary

Introduction

Online studying has become an important field of educational research due to the COVID-19 pandemic. The current research on e-learning considers the following issues: Lack of an effective online learning atmosphere [1], insufficient interactivity in virtual classrooms [2], non-diversified teaching method [3], lack of online course resources [4]. The problems accused as listed above are more likely to affect online teaching and learning effectiveness and quality seriously. Studies are mainly focus on constraints of online learning, such as satisfaction [5,6,7,8], attitude [9], adoption [10,11], acceptance [12,13,14,15,16], sustained use [17,18,19,20], motivation [21,22], use behavior [23,24,25,26,27,28,29], and knowledge sharing [30]

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