Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning

Abstract

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.