Abstract:
With the rapid augmentation of the Internet and communication technology, interest in online learning continues to rapidly broaden the horizons of academic institutions. One of the most important components of the e-learning system is the Online Learning Management System (OLMS). The Feedback module of OLMS includes the ability to gather possible teacher feedback which is considered as an important component of education and a valuable contributor to improving the nature of instruction. Student feedback is usually rich and varied. They also provide a complete insight of how their professors motivate and educate their students. The student feedback allows the instructor to experience and comprehend the significance of teaching. The feedback module integrated with Artificial Intelligence (AI), especially Machine learning (ML) enables deep analysis of large amounts of data, discover patterns and the performance of the teachers. In this progression it is observed that sentiment analysis may effectively capture student sentiments, potentially reducing both obscurity and feedback gaps. This work investigates that the incorporation of like Machine Learning algorithms and NLP for sentiment analysis, with the OLMS data may assist in improving teaching efficiency.
