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Online education – bane or boon? A quantitative plus text mining approach

Online education – bane or boon? A quantitative plus text mining approach The COVID-19 pandemic and its aftermath sent the entire educational system across the globe topsy-turvy. Virtual classrooms, online lectures and online evaluations became the order of the day, replacing traditional face to face classroom interactions and examinations conducted physically. While it may be possible to reach out to a larger audience in remote places via online platforms, the new medium lacks personal touch of the past, and is known to cause physical and psychological problems for participants. This study collects primary data from a representative sample of students from emerging economies to study the factors that influence intention to pursue online education.Design/methodology/approachANOVA, Kruskal–Wallis test, exploratory factor analysis (EFA) and multiple linear regression (MLR) have been used to test our hypothesis. We have also used text mining to corroborate statistical test results and ascertain the sentiment of students towards online learning.FindingsThis study not only confirms findings in extant literature that “benefits” is an important factor. It also identifies new factors such as “health”, “evaluation”, “class duration” and “student qualification”, that influence student intention to pursue online education. Sentiment analysis shows that students have positive sentiment coupled with trust towards online education. Text mining shows that “mode of class”, “time or duration of class” and “quality of learning” are important features that students consider.Originality/valueThis is one of the few studies to use quantitative plus text mining method of research to understand intention to pursue online education. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Educational Management Emerald Publishing

Online education – bane or boon? A quantitative plus text mining approach

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References (60)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0951-354X
DOI
10.1108/ijem-08-2023-0420
Publisher site
See Article on Publisher Site

Abstract

The COVID-19 pandemic and its aftermath sent the entire educational system across the globe topsy-turvy. Virtual classrooms, online lectures and online evaluations became the order of the day, replacing traditional face to face classroom interactions and examinations conducted physically. While it may be possible to reach out to a larger audience in remote places via online platforms, the new medium lacks personal touch of the past, and is known to cause physical and psychological problems for participants. This study collects primary data from a representative sample of students from emerging economies to study the factors that influence intention to pursue online education.Design/methodology/approachANOVA, Kruskal–Wallis test, exploratory factor analysis (EFA) and multiple linear regression (MLR) have been used to test our hypothesis. We have also used text mining to corroborate statistical test results and ascertain the sentiment of students towards online learning.FindingsThis study not only confirms findings in extant literature that “benefits” is an important factor. It also identifies new factors such as “health”, “evaluation”, “class duration” and “student qualification”, that influence student intention to pursue online education. Sentiment analysis shows that students have positive sentiment coupled with trust towards online education. Text mining shows that “mode of class”, “time or duration of class” and “quality of learning” are important features that students consider.Originality/valueThis is one of the few studies to use quantitative plus text mining method of research to understand intention to pursue online education.

Journal

International Journal of Educational Management – Emerald Publishing

Published: Dec 4, 2024

Keywords: Online education; Benefits; Health hazards; Evaluation; Class duration; Exploratory factor analysis; Statistical tests; Multiple linear regression; Sentiment analysis

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