PROFS - Predictive Recognition Of Failing Students

Description

Predicting at-risk students who are struggling and in danger of failing is a critical aspect of educational institutions' efforts to provide timely support and intervention. By employing data-driven strategies and analytics, teachers and Development Advisors can identify students facing potential academic, social, or personal challenges. These challenges range from poor academic performance to attendance issues and emotional well-being. Early identification of at-risk students allows institutions to implement targeted interventions, such as mentorship programs, tutoring, or counseling services, to help these students overcome their difficulties and stay on track to success. Moreover, predictive analytics not only benefit individual students but also contribute to improving overall retention rates and ensuring that educational resources are allocated more effectively. This helps educational institutions create a more supportive and inclusive environment that allows all students to thrive. This project aims to develop machine learning-based analytic models to automate the prediction process of at-risk students.

Period	1 Jul 2024 → 31 Aug 2024
Degree of Recognition	Local