THE POISSON PROCESS PREDICTION OF STUDENTS APPLYING FOR TEMPORARY WITHDRAWAL ON YEARLY BASIS IN FACULTY IN ENGINEERING IN FEDERAL UNIVERSITY OTUOKE, BAYELSA STATE

Student withdrawal is a recurring challenge in higher education institutions, often influenced by academic, financial, and socio-psychological factors. Understanding and predicting this phenomenon is crucial for effective planning and student support services. This study applies the Poisson process model to predict the yearly trend of temporary withdrawals among students in the Faculty of Engineering, Federal University Otuoke, Bayelsa State. Data covering the period from 2023 to 2025 was collected across six departments: Mechanical, Mechatronics, Electrical and Electronics, Civil, Petroleum and Gas, and Chemical Engineering. The analysis revealed fluctuating withdrawal patterns, with the highest incidence recorded in Electrical and Electronics Engineering (44 withdrawals over three years), while Chemical Engineering exhibited relatively stable figures. Using the Poisson rate parameter (λ), the probability distribution process was estimated, and the mean of temporary withdrawal was computed. Findings suggest that the Poisson process provides a reliable framework for modeling student withdrawal patterns and can serve as an early warning tool for university administrators. Policy implications recommend enhanced academic support, financial aid programs, and mental health interventions to address the underlying causes of withdrawal. This study contributes to predictive modeling in higher education and provides a foundation for data-driven decision-making aimed at reducing student attrition rates in engineering faculties.