Student Perception

Research on student perception focuses on understanding how students experience and interact with various aspects of their education, leveraging both qualitative and quantitative methods. Current studies utilize machine learning techniques, including deep learning models like BERT and LSTM, and sentiment analysis to analyze large datasets of student feedback, such as course reviews and online comments, to identify trends and patterns in student opinions. This work is significant for improving pedagogical practices, informing the design of educational technologies (like AI-powered chatbots), and providing valuable insights into the effectiveness of teaching methods and learning resources.