Bayesian-Structural Equation Modeling on Learning Motivation of Undergraduate Students During Covid-19 Outbreak - Reny Rian Marliana; Maya Suhayati; Sri Bekti Handayani N. (Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI), 2022)
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.