Search Results (3)

To mitigate future educational challenges, the early childhood period is critical for cognitive development, so understanding the factors influencing child learning abilities is essential. This study investigates the impact of parenting techniques, sociodemographic characteristics, and health conditions on the learning abilities of children under five years old. Our primary goal is to explore the key factors that influence children’s learning abilities. For our study, we utilized the 2019 Multiple Indicator Cluster Surveys (MICS) dataset in Bangladesh. Using statistical analysis, we identified the key factors that affect children’s learning capability. To ensure proper analysis, we used extensive data preprocessing, feature manipulation, and model evaluation. Furthermore, we explored robust machine learning (ML) models to analyze and predict the learning challenges faced by children. These include logistic regression (LRC), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and bagging classification models. Out of these, GB and XGB, with 10-fold cross-validation, achieved an impressive accuracy of 95%, F1-score of 95%, and receiver operating characteristic area under the curve (ROC AUC) of 95%. Additionally, to interpret the model outputs and explore influencing factors, we used explainable AI (XAI) techniques like SHAP and LIME. Both statistical analysis and XAI interpretation revealed key factors that influence children’s learning difficulties. These include harsh disciplinary practices, low socioeconomic status, limited maternal education, and health-related issues. These findings offer valuable insights to guide policy measures to improve educational outcomes and promote holistic child development in Bangladesh and similar contexts. Full article

Child labour is a common financial coping strategy in poor households, especially in low-and middle-income countries with many children working under hazardous conditions. Little is known about the linkages between hazardous work conditions and psycho-social and educational outcomes. We analysed the Bangladesh Multiple Indicator Cluster Survey (BMICS) round 6 to assess the association between the exposure variables, including child labour, hazardous child labour (HZCL) and hazardous work, and outcome variables, including psychosocial functioning difficulty and school dropout, in children aged 5 to 17 years. We conducted bivariable and multivariable analyses to examine the association. In the adjusted analyses, children engaged in HZCL had increased odds of psychosocial functioning difficulty (aOR: 1.41; 95% CI: 1.16–1.72) and school dropout (aOR: 5.65; 95% CI: 4.83–6.61) among 5–14-year-olds compared to children who did not engage in child labour and hazardous work. Other independent factors associated with psychosocial functioning difficulty and school dropout included being male, living in a deprived neighbourhood, being exposed to violent punishment, the caregiver’s attitude towards physical punishment, the mother’s functional difficulty and lower maternal education. The linkages between hazardous work and psychosocial functioning difficulty appear more prominent among children not in school. Further, the evidence on the relationship between hazardous work and school dropout is stronger among children with psychosocial functioning difficulty. Policies and programmes that target the most hazardous forms of work are likely to have the greatest benefits for children’s mental health, social well-being and educational attainment. Full article

This paper aims to show the impact of access to electricity on school enrollment in Bangladesh. It offers an empirical investigation of the relationship between access to electricity and school enrollment statuses, such as grade progression, repetition, and non-attendance. The data were taken from Bangladesh’s Multiple Indicator Cluster Survey (MICS) database 2012–2013 provided by the Bangladesh Bureau of Statistics (BBS) and UNICEF; the data include two years of grading information for children of ages ranging from 5–15. We applied the propensity score matching (PSM) and the Markov schooling transition model using matched sample data. The results show that access to electricity has a significant positive effect on grade progression and a significant negative effect on non-attendance in the short run as well as in the long run. The simulation result shows that the non-attendance rate is lower and the school enrollment rate for children grades 9-11 is higher in the electrified areas compared to unelectrified areas. This result suggests that access to electricity is an important strategic indicator for increasing school enrollment in both primary and secondary schools. Full article