Fractional-Iterative BiLSTM Classifier : A Novel Approach to Predicting Student Attrition in Digital Academia

Abstract

- Virtual learning circumstances have been observed as consistent growth over the years. The widespread use of online learning leads to an emerging amount of enrollments, also from pupils who have quit the education scheme previously. However, it also earned an increased amount of withdrawal rate when compared to conventional classrooms. Quick identification of pupils is a difficult issue that can be alleviated with the help of previous models for data evaluation and machine learning. In this research, a fractional-Iterative BiLSTM is used for predicting the student's dropout from online courses with a high accuracy rate. The feature extraction is provided by utilizing the encoder layer that efficiently extracts the features based on Statistical features. The Fractional-Iterative BiLSTM classifier is employed in the decoder layer, which is effectively performed in the classification function to predict the student dropout. The accomplishment of the research is evaluated by calculating the enhancement, and the developed model achieved the increment of 96.71% accuracy, 95.31% sensitivity, and 97.01% specificity, which shows the method's efficiency, and the MSE is reduced by 0.11%.