ABSTRACT

The increasing prevalence of social media platforms has revolutionized how students communicate, collaborate, and engage with academic content. This study aims to develop a predictive model for student engagement on social media platforms by analyzing demographic and behavioral data. Using data collected from students' social media account and engagement patterns, the research explores the impact of variables such as age, gender, and academic discipline on social media usage. The study applies machine learning algorithms to build predictive models that forecast student engagement on Instagram and Tiktok. The findings indicate that demographic characteristics, platform type, and user behavior significantly influence engagement patterns, with younger students and those in social-oriented disciplines demonstrating higher levels of activity. This research contributes to understanding student engagement on social media and provides educators with valuable insights into managing the educational benefits and challenges associated with social media usage. The study also addresses ethical considerations related to data privacy and user consent, ensuring that data collection complies with best practices in research ethics.