“EXPLORING THE IMPACT OF SOCIAL MEDIA ON STUDENT ACADEMIC PERFORMANCE: AI-DRIVEN REGRESSION AND PREDICTIVE DASHBOARDS”

Abstract

Social media has become an integral component of student life, shaping communication, collaboration, and learning patterns. While it offers opportunities for academic engagement, excessive usage may lead to distractions and poor academic outcomes. This study investigates the impact of Social Media Usage (SMU) on Student Academic Performance (SAP), examining how demographic factors such as gender, department, and program type influence social media behaviour. Additionally, it explores the use of statistical and machine learning approaches to predict academic outcomes and develop AI-driven dashboards for identifying at-risk students.

A structured questionnaire was administered to 150 students across selected colleges, yielding 135 responses. After validation for completeness and accuracy, 80 responses were considered suitable for analysis. A Simple Random Sampling (SRS) technique was employed to ensure unbiased representation. Collected data included demographic information, social media usage patterns, and academic performance indicators. Correlation analysis was applied to identify associations, ANOVA was used to examine demographic influences, and linear regression was employed to assess predictive effects. Machine learning techniques were integrated to enhance predictive accuracy and uncover latent patterns in the data.

The findings indicate that SMU has a measurable impact on SAP, with both positive and negative influences depending on usage patterns. Gender, department, and program type were found to significantly moderate the relationship between social media engagement and academic outcomes. Furthermore, the AI-driven dashboards successfully identified students at risk of underperformance, providing a visual and actionable tool for educators to implement targeted interventions.

This study offers important insights for students and educators seeking to balance social media engagement with academic achievement. By combining traditional statistical methods with machine learning and AI-driven visualization, the research demonstrates a practical approach to understanding and managing the complex relationship between social media behavior and academic performance. These results can inform strategies for promoting effective social media use and supporting at-risk students in higher education settings.