A Proposed AI Method for Tracking College Students’ Academic Progress

Educators face significant challenges in monitoring the academic progress of their students in a particular course. Identifying students who are struggling academically can be a daunting task, but teachers can take proactive steps to provide them with additional support once these students come to their attention. Educational institutions today collect vast amounts of data on students from various sources, but they are constantly seeking innovative ways to leverage this information to enhance their reputation and the quality of their instruction. This research delves into the potential of utilizing machine learning algorithms to effectively track students’ academic progress and notify teachers about those who may be at risk of failing a class. The translated prediction model presents an easily accessible visual representation that enables teachers to promptly implement appropriate interventions. Our proposed approach employs various machine learning techniques to create a predictive model that can identify students who may be at risk early on, thus mitigating factors that could hinder their learning. This could serve as a foundation for future predictive models to aid university educators in optimizing the overall learning process.