Data analytics for students' feedback in college education using bi-directional models and fasttext embeddings

This study aims to analyse the students' feedback data for enhancing the educational system. Teachers' feedback serves as a critical tool for assessing educational outcomes and improving teaching strategies. Natural language processing (NLP), an active research area of artificial intelligence (AI), offers novel solutions for analysing and understanding large volumes of feedback data, aiding in the refinement of educational colleges. This paper aims to carry out a comprehensive analysis of students' feedback by classifying content into five classes using advanced AI techniques including machine learning, ensemble methods, and deep learning (DL) combining with both textual features and word embedding features to improve predictive performance. Among all the applied features, the hybrid approach of the latest technique of FastText with DL model of Bi-GRU reveals the highest results with accuracy of 95%. This research confirms that NLP features provide deep insights into content and help us predict the various aspects of students' feedback for improvements in the educational sector.