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Testing students' e-learning via Facebook through Bayesian structural equation modeling.

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Area of Science:

  • Educational Technology
  • Information Systems
  • Psychology of Learning

Background:

  • Student intention to use new learning technologies is influenced by various factors.
  • Existing research on technology acceptance often uses regression or structural equation modeling.
  • Bayesian analysis offers a more accurate approach to data analysis in this field.

Purpose of the Study:

  • To re-examine the unified theory of acceptance and technology use (UTAUT) in the context of e-learning via Facebook.
  • To compare the effectiveness of Bayesian analysis with maximum likelihood estimation for analyzing technology acceptance data.
  • To identify key factors influencing students' intention to use Facebook for e-learning.

Main Methods:

  • Data collected from 170 business statistics students at the University of Malaya, Malaysia.
  • Technology acceptance model tested using both maximum likelihood and Bayesian approaches.
  • Comparative analysis of results from both statistical methods.

Main Results:

  • Performance expectancy and hedonic motivation were identified as the strongest predictors of intention to use e-learning via Facebook.
  • The Bayesian estimation model demonstrated a superior data fit compared to the maximum likelihood estimator model.
  • Discrepancies between the two analytical approaches were observed and discussed.

Conclusions:

  • Bayesian analysis provides a more accurate and reliable method for analyzing technology acceptance models in e-learning contexts.
  • Understanding performance expectancy and hedonic motivation is crucial for promoting the adoption of e-learning technologies like Facebook.
  • The study highlights the importance of employing advanced analytical techniques for robust research findings in educational technology.