Providing an Intelligent Model for Predicting Student Academic Decline with Emphasis on Family Characteristics: A Case Study of Alborz Province
Keywords:
student academic decline, family characteristics, machine learning, academic decline predictionAbstract
The education system, as one of the fundamental pillars of human development, plays a crucial role in fostering individual and social advancement. Student academic decline is one of the serious challenges in this field, with long-term individual and social consequences. Although numerous studies have been conducted globally, local research in this area remains limited; therefore, conducting a case study in Alborz Province can help fill this research gap. This study aimed to analyze and model student academic decline based on family indicators through a case study in Alborz Province. The research data were collected from primary and secondary schools in the province and included variables such as parents’ educational level, household income, parents’ occupations, and family structure. To design the predictive model for academic decline, four machine learning algorithms were applied: logistic regression, support vector machine (SVM) with an RBF kernel, random forest, and XGBoost. The results showed that among the tested models, the random forest algorithm performed best and achieved 98% accuracy in identifying complex relationships among the variables. For analyzing the relationships between family variables and academic decline, Spearman’s rank correlation coefficient was first used to examine statistical associations. Then, SHAP (SHapley Additive exPlanations) analysis was employed to interpret the decisions of the machine learning models and clarify the role of each variable. Based on the findings, mother’s education, household income, and father’s education were identified as the most significant factors contributing to academic decline. The results of this study can be highly effective in designing preventive educational policies and timely identification of at-risk students.
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