This paper explores the design, implementation, and impact of a Student Feedback System (SFS) aimed at enhancing the quality of education by facilitating systematic collection and analysis of student feedback. The SFS is an automated, web-based platform that allows students to provide anonymous, constructive feedback on various aspects of their learning experience, including course content, teaching effectiveness, classroom environment, and learning resources. The system is designed to be user-friendly, ensuring high participation rates, and is accessible via both web and mobile interfaces, allowing for convenient feedback submission. The feedback collected is processed through advanced data analytics, employing techniques such as sentiment analysis and statistical analysis. Sentiment analysis, powered by natural language processing (NLP), categorizes and scores qualitative responses, providing a deeper understanding of student sentiment and concerns. This data is then presented in visual reports, enabling educators and administrators to make informed, data-driven decisions regarding curriculum improvements, teaching methods, and resource allocation.
Volume 17 | Issue 1
Pages: 52-56