Comparative Analysis of Data Mining Based Performance Evaluation Using Hybrid Deep Learning Approach

Educational failure is prevalent. The surge in the number of students who quit school has multiple root factors. The inability to succeed academically is a primary factor in why students drop out of school. Since many students struggle to adjust to their new school, this affects performance. Our study aims to identify all elements affecting undergraduate academic achievement. Thus, this initiative aims to help students identify the factors that lead to their successes so they can take steps to change their results. Students, course instructors, and others can improve the environment after identifying and assessing its main components. We used a Recurrent Neural Network and Long Short-Term Memory classification technique to forecast student academic success early. This method is compared to numerous machine learning classifiers and a deep learning classification model. Using study findings from numerous trials, we examined the classification performance of several standard machine learning techniques, such as support vector machine, random forest, J48, artificial neural network, and naive bayes, as well as deep learning models, such as RNN. RNN-LSTM sigmoid, Tan–h, and ReLU function are used to predict student performance and enhance teaching. The results are compared to deep learning and machine learning methods. RNN-LSTM (ReLU) has the highest accuracy rate of 97%, as per experiments. Our technique has great classification accuracy on different datasets or real-time complex huge datasets of students with multivalued variables.