Recurrent Neural Network Model to Predict and Monitor Student Performance

Academic metrics are very important for monitoring and tracking student achievement, helping to improve instructional quality and expand learning outcomes. There are difficulties in producing indicators and enhancing the precision of student performance prediction models using the assessment data that is currently available. Nonetheless, it assists teachers in adapting their instructional strategies and interventions to the particular needs of each student. As a result, addressing these challenges is crucial for better efficacy and improving students' academic success. We intend to create five recommended machine learning models as case studies to predict academic success for student performance monitoring and tracking. The model seeks to capture the complicated visualizations and dependencies inherent in the sequential structure of grades throughout a course. This research investigates the development of a Recurrent Neural Network (RNN) model to predict student performance using a data set of results from several examinations. We explore generic machine learning approaches, followed by an in depth look at RNNs, their application to our job, and code implementation. Finally, we evaluate the performance of our model, draw conclusions, and discuss further improvements. Our findings are unique and encouraging. This update improves clarity and flow while still conveying the idea of performance monitoring.