Statistical and Exploratory Analysis of Student Academic Performance Using Socio-Demographic Factors

Educational data analysis plays a significant role in evaluating student achievement and improving academic decision-making processes. This study proposes a structured Student Performance Analysis Framework that integrates mathematical modeling, percentage computation, grading classification, and descriptive statistical evaluation. The dataset consists of student scores in Mathematics, Reading, and Writing, from which overall academic percentage is computed using an averagebased formulation. A systematic grading function is applied to categorize performance into standardized grade levels. Statisti- cal analysis indicates an overall mean percentage of 67.77%, reflecting moderate academic performance across the dataset. Among the subjects, Reading records the highest average score (69.16), followed by Writing (68.05) and Mathematics (66.08). Standard deviation analysis reveals moderate variability, partic- ularly in Mathematics and Writing. Visualization of subject-wise averages supports comparative interpretation of performance trends. The proposed framework provides a transparent and scalable analytical model that can assist educational institutions in monitoring student progress and identifying areas requiring targeted academic intervention.