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

Shaofeng Wang, Gaojun Shi, Mingjie Lu, Ruyi Lin and Junfeng Yang
Additional contact information
Shaofeng Wang: Smart Learning Institute, Beijing Normal University, Beijing 100875, China
Gaojun Shi: School of Education, Hangzhou Normal University, Hangzhou 311121, China
Mingjie Lu: Research Center for Intelligent Social Governance, Zhejiang Lab, Hangzhou 310005, China
Ruyi Lin: School of Education, Hangzhou Normal University, Hangzhou 311121, China
Junfeng Yang: School of Education, Hangzhou Normal University, Hangzhou 311121, China

Sustainability, 2021, vol. 13, issue 17, 1-19

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.

Keywords: active online learning; smart learning environment; technology acceptance model; social isolation; PLS-SEM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/17/9923/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/17/9923/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:17:p:9923-:d:628594

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().