@article{
 title = {Modeling spatial and temporal variability in educational development index of Bangladesh using socio-economic, and demographic data, 2001–2021: A Bayesian approach},
 type = {article},
 year = {2025},
 pages = {e0317030},
 volume = {20},
 month = {3},
 publisher = {Public Library of Science},
 day = {28},
 id = {81c9ecc2-134b-3c92-a046-b10946eaa08a},
 created = {2025-04-15T13:25:57.937Z},
 file_attached = {false},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2025-04-15T13:25:57.937Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Background The Educational Development Index (EDI) is a critical tool for assessing and tracking the progress of education systems from local to national, and even global scales and needs to be chosen for every layer of the subnational boundaries to secure the basic human rights of the people. In reality, there are significant variations within the consecutive time breaks and the geographical boundaries that need to be examined. The authors aim to examine how the EDI relates to various spatiotemporal variables. Methods and Materials This research is based on secondary data on literacy rates (EDI) from 64 districts of Bangladesh and 6 relevant variables over the period 2001 to 2021. The optimal model for the data was identified from Bayesian spatial-temporal modeling (Linear, Analysis of Variance (ANOVA), Autoregressive (AR1), and AR2) and the Markov Chain Monte Carlo (MCMC) method used to generate data about the prior and posterior realizations. To select the best model different model selection and validation criteria such as the Deviance Information Criterion (DIC), Watanabe-Akaike information criterion (WAIC), and Root Mean Square Error (RMSE) were employed in this study. Results The ‘AR1’ model is a ‘temporal model’ performed better than others. Significant spatial (ρS=0.994) and temporal (ρT=0.347) variations were identified for the suited model. Of the factors considered for model fitting, the health index, income index, expected years of schooling, population density, and dependency ratio are found to be important components of educational development in Bangladesh. Conclusion The variation in the spatial domain can be used to identify the districts to improve the educational index controlling responsible factors by the policymakers.},
 bibtype = {article},
 author = {Sultana, Afroza and Ali, Akher and Ar Salan, Md. Sifat and Kabir, Mohammad Alamgir and Hossain, Md. Moyazzem},
 doi = {10.1371/journal.pone.0317030},
 journal = {PLOS ONE},
 number = {3}
}

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In reality, there are significant variations within the consecutive time breaks and the geographical boundaries that need to be examined. The authors aim to examine how the EDI relates to various spatiotemporal variables. Methods and Materials This research is based on secondary data on literacy rates (EDI) from 64 districts of Bangladesh and 6 relevant variables over the period 2001 to 2021. The optimal model for the data was identified from Bayesian spatial-temporal modeling (Linear, Analysis of Variance (ANOVA), Autoregressive (AR1), and AR2) and the Markov Chain Monte Carlo (MCMC) method used to generate data about the prior and posterior realizations. To select the best model different model selection and validation criteria such as the Deviance Information Criterion (DIC), Watanabe-Akaike information criterion (WAIC), and Root Mean Square Error (RMSE) were employed in this study. Results The ‘AR1’ model is a ‘temporal model’ performed better than others. Significant spatial (ρS=0.994) and temporal (ρT=0.347) variations were identified for the suited model. Of the factors considered for model fitting, the health index, income index, expected years of schooling, population density, and dependency ratio are found to be important components of educational development in Bangladesh. Conclusion The variation in the spatial domain can be used to identify the districts to improve the educational index controlling responsible factors by the policymakers.","bibtype":"article","author":"Sultana, Afroza and Ali, Akher and Ar Salan, Md. Sifat and Kabir, Mohammad Alamgir and Hossain, Md. Moyazzem","doi":"10.1371/journal.pone.0317030","journal":"PLOS ONE","number":"3","bibtex":"@article{\n title = {Modeling spatial and temporal variability in educational development index of Bangladesh using socio-economic, and demographic data, 2001–2021: A Bayesian approach},\n type = {article},\n year = {2025},\n pages = {e0317030},\n volume = {20},\n month = {3},\n publisher = {Public Library of Science},\n day = {28},\n id = {81c9ecc2-134b-3c92-a046-b10946eaa08a},\n created = {2025-04-15T13:25:57.937Z},\n file_attached = {false},\n profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},\n last_modified = {2025-04-15T13:25:57.937Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Background The Educational Development Index (EDI) is a critical tool for assessing and tracking the progress of education systems from local to national, and even global scales and needs to be chosen for every layer of the subnational boundaries to secure the basic human rights of the people. In reality, there are significant variations within the consecutive time breaks and the geographical boundaries that need to be examined. The authors aim to examine how the EDI relates to various spatiotemporal variables. Methods and Materials This research is based on secondary data on literacy rates (EDI) from 64 districts of Bangladesh and 6 relevant variables over the period 2001 to 2021. The optimal model for the data was identified from Bayesian spatial-temporal modeling (Linear, Analysis of Variance (ANOVA), Autoregressive (AR1), and AR2) and the Markov Chain Monte Carlo (MCMC) method used to generate data about the prior and posterior realizations. To select the best model different model selection and validation criteria such as the Deviance Information Criterion (DIC), Watanabe-Akaike information criterion (WAIC), and Root Mean Square Error (RMSE) were employed in this study. Results The ‘AR1’ model is a ‘temporal model’ performed better than others. Significant spatial (ρS=0.994) and temporal (ρT=0.347) variations were identified for the suited model. Of the factors considered for model fitting, the health index, income index, expected years of schooling, population density, and dependency ratio are found to be important components of educational development in Bangladesh. Conclusion The variation in the spatial domain can be used to identify the districts to improve the educational index controlling responsible factors by the policymakers.},\n bibtype = {article},\n author = {Sultana, Afroza and Ali, Akher and Ar Salan, Md. Sifat and Kabir, Mohammad Alamgir and Hossain, Md. 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