Bayesian Modelling of Weibull Distribution for Predicting Student Dropout Rates A Case of Higher Education Institutions in Nepalese University
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Abstract
Higher education contributes to knowledge creation, and the enhancement of essential skills. Despite its crucial role, dropout rates in higher education affect all universities globally, impeding the effective transmission of knowledge and skills to learners. Hence, this study aims to predict the dropout rates in higher education institutions by using Bayesian modelling of Weibull distribution.
This study utilized a quantitative research design. To ensure representativeness across different university and colleges, 27 colleges were randomly selected. The Bayesian model of the Weibull distribution emerges as an acceptable model for predicting dropout rates. The model is validated using graphical methods such as P-P and Q-Q plots, Kolmogorov-Smirnov, Anderson-Darling, and Cramer-Von Mises tests. In Bayesian modelling, the MCMC simulation method is implemented using the Stan package. Model accuracy is evaluated through trace plots, ergodic mean plots, autocorrelation plots, BGR plots, n_effect, and Rhat. Ultimately, Bayesian modelling of the Weibull distribution emerged as an alternative model for prediction.
The study predicts a dropout rate of approximately 26%, indicating that one in every four students annually drops out of higher education in Nepal. This finding poses a significant challenge to both Nepalese universities and the higher education system of Nepal.
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