A Novel Concept of Analysing Performance of Deaf Students using Neural Networks

Deaf students need modified curriculum reflecting their special needs as exceptional learners. The barriers related to their disability should be identified and recommendations to overcome these barriers should be made. This paper introduces a novel concept of feature engineering in analyzing the performance assessment of deaf students. In this paper an attempt is made to identify the features specific to deaf students and develop a dataset. The classification models are used to classify the students according to their performance. On comparison, the classification accuracy of ANN is found to be better and the performance of this model is evaluated using various parameters - Accuracy, Precision, Recall, F1-Score and AUROC.