Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning
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 condit...
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| Format: | Article |
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2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/20 |
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| author | Mithila Akter Mim M. R. Khatun Muhammad Minoar Hossain Wahidur Rahman Arslan Munir |
| author_facet | Mithila Akter Mim M. R. Khatun Muhammad Minoar Hossain Wahidur Rahman Arslan Munir |
| author_sort | Mithila Akter Mim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-986b04fc21b645bd9a840505f2116971 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-986b04fc21b645bd9a840505f21169712025-01-24T13:17:29ZengMDPI AGAlgorithms1999-48932025-01-011812010.3390/a18010020Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine LearningMithila Akter Mim0M. R. Khatun1Muhammad Minoar Hossain2Wahidur Rahman3Arslan Munir4Department of Computer Science and Engineering, Bangladesh University, Dhaka 1000, BangladeshDepartment of Computer Science and Engineering, Bangladesh University, Dhaka 1000, BangladeshDepartment of Computer Science and Engineering, Bangladesh University, Dhaka 1000, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, BangladeshDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USATo 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.https://www.mdpi.com/1999-4893/18/1/20child developmentBangladesh multiple indicator cluster surveys (MICS)machine learningexplainable AISHAPLIME |
| spellingShingle | Mithila Akter Mim M. R. Khatun Muhammad Minoar Hossain Wahidur Rahman Arslan Munir Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning Algorithms child development Bangladesh multiple indicator cluster surveys (MICS) machine learning explainable AI SHAP LIME |
| title | Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning |
| title_full | Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning |
| title_fullStr | Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning |
| title_full_unstemmed | Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning |
| title_short | Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning |
| title_sort | exploring early learning challenges in children utilizing statistical and explainable machine learning |
| topic | child development Bangladesh multiple indicator cluster surveys (MICS) machine learning explainable AI SHAP LIME |
| url | https://www.mdpi.com/1999-4893/18/1/20 |
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