Forecasting literacy development in Bangladesh: A comparative study of machine learning and hybrid approaches

Abstract

In general in Bangladesh there have been some progress in literacy rates during the last couple of decades. Though literacy has persisted into the twenty first century as a major concern on the international level, it is still difficult to attain universal literacy. In this study, the possibility of using machine learning and mixed models to predict literacy in Bangladesh is considered, with a focus on the most accurate prediction of literacy levels by the end of the 2030 period. From a set of socio-economic and demographic indicators - including GDP, population, unemployment rate and life expectancy, we assessed few given predictive models. These were Linear Regression, SVR, Gradient Boosting, Random Forest, XGBoost, ANN, LSTM, CNN, RNN, GRU, LSTM + ARIMA, ANN + ARIMA, CNN + ARIMA, GRU + ARIMA. The hybrid models performed better than others in all parameters such as MAE, MSE, & R2 Score which again validate the ability of the hybrid models to capture temporal and non-linear dynamics inherent in the data. Further, based on the ARIMA-models analysis, literacy rate that was below 60% a decade ago is expected to reach nearly 90% in 2030. It also discusses how the policy-driven hybrid modeling can be useful and promotes data drive approach in perusing the SDG goals.