Authors: Sali Radha, Sali Radha, Bacchav Jayesh, Patil Harshal, Boraste Siddesh
Abstract: In today’s educational landscape, institutions increasingly recognize the value of student feedback for enhancing learning experiences. However, traditional methods like manual reviews and basic statistics often fail to capture the complex and complicated patterns within this feedback. Our project proposes a novel approach using Long Short-Term Memory (LSTM) algorithms to analyse student feedback and predict sentiment more effectively. LSTM’s strength in handling sequential data enables us to uncover deeper insights into student experiences and trends. This innovative method aims to transform feedback analysis into a comprehensive, data-driven evaluation tool, ultimately improving educational practices. Additionally, we implement a Generative Pre-trained Transformer (GPT) model to provide dynamic, tailored suggestions for student growth. By combining advanced machine learning techniques, our system not only analyses feedback but also offers actionable recommendations, fostering a more supportive and effective learning environment. This holistic approach aims to enhance both student outcomes and institutional practices.
DOI: https://doi.org/10.5281/zenodo.17985712
