Deep Learning Model to Improve Teaching-Learning Process using Sentiment Analysis
Ganpat Singh Chauhan, Neeraj Kumar Garg, Ravi Nahta, Yogesh Kumar Meena · 2023
The integration of teacher capabilities and teaching quality has become a crucial concern in today’s Outcome-Based Education (OBE) in higher educational institutes. In addition, there is a requirement to utilize the vast amounts of textual qualitative data obtained through stakeholder’s input, primarily the responses to open-ended inquiries, which pose a significant problem to academics and institutions alike. The present research assesses instructors' interpersonal abilities using sentiment analysis techniques. We evaluated certain skills using aspect-based sentiment severity of the over 4,000 inputs left by Stakeholders for higher learning. The latest deep learning models and visualization frameworks were used to carry out the sentiment assessment system. According to the research presented, certain abilities are brought to light using aspect-driven sentiment analysis by the course’s structure, teacher’s teaching, and student’s learning. Additionally, the results show that stakeholders have an equally monolithic and converging perspective, supporting the legitimacy of their opinions and their significant involvement towards the tracking of the teaching environment generally concerning the ultimate aim of improving learning of the students.