Machine Learning-Based Real-Time Feedback Assessment System for Student Performance Prediction in Tertiary Institution

The need for effective and digitized formative feedback mechanisms in classroom management of core courses in tertiary institutions in the developing world such as Nigeria, Kenya, and Ghana is paramount. A fair trivial environment is needed for students to learn and interact with their tutor effectively. This paper presents a framework for feedback assessment in real-time for student performance prediction using a machine learning approach in the university to maximize students’ satisfaction through an internalized and effective learning environment by monitoring students’ level of engagement during lecture sessions. The analysis from the existing system shows that the large amount of data generated from students’ responses makes it possible to predict student performance per course. This was done using machine learning (K-Nearest Neighbor) to predict the likelihood of student performance and engagement overtime on the dataset generated from attendance, personal and assessment history. The system was developed using Django (Python Framework). The empirical result from the classifier shows that KNN presented an accuracy of 78%. The implication of the study would further assist the developing country’s university system, increase the performance rate of student engagement and lecturer’s teaching styles, as well as aid in the educational decision-making process.