Version 1
: Received: 22 June 2024 / Approved: 22 June 2024 / Online: 24 June 2024 (08:15:38 CEST)
How to cite:
Alzahrani, M. R. Bayesian Analysis of Exponentiated-Weibull Weibull Distribution for Modelling Student Dropout Times. Preprints2024, 2024061660. https://doi.org/10.20944/preprints202406.1660.v1
Alzahrani, M. R. Bayesian Analysis of Exponentiated-Weibull Weibull Distribution for Modelling Student Dropout Times. Preprints 2024, 2024061660. https://doi.org/10.20944/preprints202406.1660.v1
Alzahrani, M. R. Bayesian Analysis of Exponentiated-Weibull Weibull Distribution for Modelling Student Dropout Times. Preprints2024, 2024061660. https://doi.org/10.20944/preprints202406.1660.v1
APA Style
Alzahrani, M. R. (2024). Bayesian Analysis of Exponentiated-Weibull Weibull Distribution for Modelling Student Dropout Times. Preprints. https://doi.org/10.20944/preprints202406.1660.v1
Chicago/Turabian Style
Alzahrani, M. R. 2024 "Bayesian Analysis of Exponentiated-Weibull Weibull Distribution for Modelling Student Dropout Times" Preprints. https://doi.org/10.20944/preprints202406.1660.v1
Abstract
This paper presents the five-parameter Exponentiated Weibull Weibull (EWW) distribution with Weibull base failure rate parameterization, developed to model the dropout times of students in an online class module. Parameters of the model were estimated using both Maximum Likelihood and Bayesian estimation procedures to determine the most effective estimation method. Simulation results indicate that, across various hazard types and censoring levels, Bayesian estimates are more accurate and precise than Maximum Likelihood estimates. Real-life data analysis of online students module presentation supports these findings, showing significantly lower standard deviations for Bayesian estimates compared to Maximum Likelihood estimates, highlighting the efficiency of Bayesian estimation for the EWW distribution. Additionally, a comparative goodness-of-fit analysis demonstrated that the EWW distribution better fits the student dropout times dataset than Weibull Weibull (WW), Weibull Exponential (WE), and Exponential Weibull (EW) distributions. The median dropout time estimated using the EWW distribution is 24 days, suggesting that instructors should closely monitor student attendance around this period.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
