Intelligent Analysis of Student Feedback in Post-course Assessment Using a Multiclass Classification Model

Student feedback is a powerful tool for educational institutions and educators to assess, improve, and adapt their programs and services to meet the evolving needs of students. Through Post-Course assessment, the success of the training or course is evaluated and course instructors gather insights into the effectiveness of their teaching practices, make improvements in course content and delivery methods. Traditional methods rely on manual evaluation of student feedback, which can be time-consuming and subjective. By leveraging ANNs approach, this study aims to automate the analysis process and classify the student satisfaction level into three levels. The study begins by gathering student opinions on various aspects of a certain course through surveys given at the conclusion of each course or semester. Based on accuracy, the model's performance is contrasted with the results of other well-known methods and classified student input in an accurate manner.