Predictors of student engagement and perceived learning in emergency online education amidst COVID-19: A community of inquiry perspective

Lin Li*, Renwen Zhang, Anne Marie Piper

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

During the COVID-19 pandemic, colleges and universities resorted to emergency online learning, which has significant implications for the future of online education. However, our knowledge of the factors influencing student engagement and learning is somewhat dispersed, partly due to a lack of a theoretical framework that guides research. This study draws on the Community of Inquiry (CoI) framework to examine the factors that predict student engagement and learning during emergency online learning. Through an online survey of 351 undergraduate students in a large public university in the US, we found that students who were more conscientious, open, perceived a higher sense of community at the university, perceived a higher level of nonverbal immediacy from the instructor, turned on the camera more often, had better time management skills, digital skills, and better health, reported higher engagement. Qualitative analysis revealed distractions, lack of copresence, and poor internet connections as challenges students face in synchronous online learning. By utilizing both quantitative and qualitative data collected at the peak of the pandemic, our findings offer lessons for understanding how individual factors related to student engagement and perceived learning in the online environment and how to make the online learning experience more meaningful and engaging for all individuals.

Original languageEnglish (US)
Article number100326
JournalComputers in Human Behavior Reports
Volume12
DOIs
StatePublished - Dec 2023

Keywords

  • Community of inquiry
  • Digital inequality
  • Online learning
  • Remote education
  • Survey method

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Applied Psychology
  • Human-Computer Interaction
  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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