Beyond Poverty: Identifying the Overlooked Factors Influencing Education

Poverty is the most critical factor hindering quality education for students all over the world and sustaining a vicious cycle of inequity. Poverty affects over 700 million people worldwide and causes a deep impact on students learning. Education, a widely accepted means of breaking poverty, is pivotal to the United Nations Sustainable Development Goals (SDGs): 1-No Poverty and 4-Quality Education. Keeping these goals in mind, this work explores the correlation between poverty and education, focusing on the socioeconomic linkages, challenges of resource scarcity and many more. The paper discovers 16 socioeconomic factors, in addition to financial factors that affect students’ academics. The work also proposes strategies with special emphasis on improved access, inclusion, and learning outcomes, through past literature, and by leveraging machine learning algorithms: Random Forest and XGBoost.

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Publication Details

Published
2025-02-06
DOI
https://doi.org/10.1109/cictn64563.2025.10932371
Primary Topic
Education Systems and Policy
Type
article
Field-Weighted Citation Impact
0.00
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article

Beyond Poverty: Identifying the Overlooked Factors Influencing Education

Kumar Ayush, K. K. Jain
Education Systems and Policy
article

Beyond Poverty: Identifying the Overlooked Factors Influencing Education

Kumar Ayush, K. K. Jain
article en

Abstract

Poverty is the most critical factor hindering quality education for students all over the world and sustaining a vicious cycle of inequity. Poverty affects over 700 million people worldwide and causes a deep impact on students learning. Education, a widely accepted means of breaking poverty, is pivotal to the United Nations Sustainable Development Goals (SDGs): 1-No Poverty and 4-Quality Education. Keeping these goals in mind, this work explores the correlation between poverty and education, focusing on the socioeconomic linkages, challenges of resource scarcity and many more. The paper discovers 16 socioeconomic factors, in addition to financial factors that affect students’ academics. The work also proposes strategies with special emphasis on improved access, inclusion, and learning outcomes, through past literature, and by leveraging machine learning algorithms: Random Forest and XGBoost.

Bennett University (IN)
No poverty
Openalex Percentile: Top 94%
Education Systems and Policy
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