STUDENT ACADEMIC PERFORMANCE PREDICTION USING PROBIT-GENERATIVE RESTRICTED DEEP BOLTZMANN MACHINE

Big data mining also known as Knowledge Discovery in Databases (KDD) is a process that uses statistical and computational techniques to identify significant hidden patterns and insights in massive datasets. The prediction of students' academic achievement is a key component of modern educational systems that aim to raise educational standards. Even though predicting students' performance has been the focus of numerous studies, there are still problems with early dropout prediction, learning pattern recognition, and student success prediction, which cause delays and reduced efficiency. To overcome these issues, Probit Generative Restricted Deep Boltzmann Machine (PGRDBM) method utilizes Deep Boltzmann Machine to include four different layers such as one input layer, two hidden layers, and one output layer. At first, the data collection followed by efficient performance of two significant stages such as feature selection and data classification to enable more accurate and time, space-efficient student’s academic performance prediction in educational institutions. At the input layer, the process of analysing educational performance begins with the collected student data. The Concept Drift-based Feature Selection (CD-FS) model is designed at the hidden layer to choose most pertinent features that are stable and relevant even in drift for precise prediction. The Probit Regression-based Data Classification (PR-DC) model is applied for robust student academic performance prediction by student data and classifying student performance into different classes. Then, hidden layer 2 sends the prediction results to the output layer. It is feasible to predict academic performance effectively. The performance analysis is effectively proposed in student academic performance prediction and two existing methods are conducted on different specific metrics such as accuracy, time, error rate, and space complexity