Bayesian-Structural Equation Modeling on Learning Motivation of Undergraduate Students During Covid-19 Outbreak


  • Reny Rian Marliana Department of Informatics Engineering, Universitas Sebelas April, Indonesia
  • Maya Suhayati Department of Informatics Engineering, Universitas Sebelas April, Indonesia
  • Sri Bekti Handayani N. Department of Informatics Systems, Universitas Sebelas April, Indonesia



Bayesian-SEM, e-learning readiness, learning motivation, MCMC, self-directed learning readiness


The aim of this study is to explore the relationship model between e-learning readiness, self-directed learning readiness, and learning motivation of the students at STMIK Sumedang during the COVID-19 outbreak. Bayesian-Structural Equation Modeling and Markov Chain Monte Carlo Algorithm are used in the estimation of the parameters. The posterior distribution is formed using informative prior i.e., inverse-Gamma distribution on variance parameters, inverse-Wishart distribution on residual covariance, and normal distribution on other parameters of the model. The calculation is performed using the blavaan package on R-Software version 4.1.0 with 19000 iteration and 9000 samples of burn-in period. Data were taken from 214 samples of the students at STMIK Sumedang. The outcome from the calculation showed there is a significant effect from self-directed learning readiness to motivation learning of students and there is no significant effect from e-learning readiness to learning motivation. The direct effect on learning motivation is 7.25 from self-directed learning readiness and 0.045 from e-learning readiness.


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How to Cite

Marliana, R. R., Suhayati, M. ., & Handayani N., S. B. . (2022). Bayesian-Structural Equation Modeling on Learning Motivation of Undergraduate Students During Covid-19 Outbreak. Indonesian Journal of Statistics and Its Applications, 6(1), 63–76.