A Deep Learning and Ensemble-Based Intelligent Academic Feedback Analysis System
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A Deep Learning and Ensemble-Based Intelligent Academic Feedback Analysis System
Dr. K. Satyam1, R MuniKumari2
1Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AndhraPradesh, India.
2 Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AndhraPradesh, India.
Abstract:Gathering and evaluating student input is essential to raising academic achievement and teaching quality in contemporary educational institutions. However, conventional feedback systems are frequently labour-intensive, manual, and incapable of drawing significant conclusions from massive amounts of data. This research proposes an intelligent academic feedback analysis system that combines ensemble machine learning and deep learning methods to address these issues. Students' textual feedback is processed by the suggested system, which also preprocesses the data and uses feature extraction techniques to transform unstructured data into a format that can be analysed. To improve forecast accuracy and robustness, ensemble techniques are used with deep learning models, such as neural networks. Institutions can make data-driven decisions thanks to the system's ability to automatically classify input into several categories and spot sentiment patterns. According to experimental findings, the hybrid model performs more accurately and efficiently than conventional machine learning techniques. In the end, this method improves educational results by lowering human labour and offering a scalable solution for real-time academic feedback evaluation.
Keywords:Academic Feedback Analysis, Deep Learning, Ensemble Learning, Natural Language Processing, Sentiment Analysis,Educational Data Mining, Text Classification, Machine Learning
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