Treffer: Student Learning Based Data Science Assisted Recommendation System to Enhance Educational Institution Performance.

The student feedback data possesses to be the fundamental influencers of decision-making process in diverse applications. The performance prediction based on student's feedback about the educational institution helps for better solution recommendation. The automated solution recommendation based on student's feedback extensively support the educational institution to make better decisions for improvisation. In most of the existing research works, the performance can be analysed, but suitable solution recommendation is not provided. Also, the existing recommendation works fail to generate accurate outcomes, consumes more time with higher error rates. Hence on diminishing the existing issues, this research work presents a Data science-based solution Recommendation model based on hybrid deep learning approaches. Pre-processing, feature extraction, feature clustering, performance prediction, and recommendation are the steps in the suggested model. In this research, the student feedback data is collected from Kaggle source and some of the attributes are added manually. Pre-processing techniques for the text data include stop-word-removal, tokenization, case-folding, and stemming. The features are extracted using Enhanced Lexicon bidirectional encoder representations from transformers (ELexBert) model. The significant attributes are selected using Adaptive Coati optimization (ACoaT) algorithm. The selected features are clustered based on feature similarity using Upgraded density based k-means clustering (Uden_KMC) model. A new type of hybrid deep learning model known as Channel Block Densnet with Dilated Convolution BiLSTM (ChaBDBiL) is employed for recommending better decisions. The recommendation performances using feedback data are evaluated using PYTHON where the overall accuracy of 98.19%, specificity of 98.25%, F1 score of 91.28%, sensitivity of 97.41% and Kappa score of 91.28% are obtained. [ABSTRACT FROM AUTHOR]

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