Hybrid Machine Learning Approach using Real Time Data Processing for Educational Data Mining

Educational data produced by various systems, such as e-learning, student admission process, online student exam, and automated significance management solutions, may be successfully analyzed using educational data mining (EDM) methods to provide beneficial for the academic student’s progress. A highly desired use of EDM is the prediction of standardized testing from previous academic data. In this intelligence, it is critical to establish an automated approach for predicting student success. The majority of existing research on academic prediction problem use traditional feature extraction and illustration approaches, in which retrieved characteristics are feed to machine learning classifier. Supervised learning has recently allowed academics to extract high-level characteristics from raw data automatically. The proposed enhanced feature representation approaches allow for better performance on real time heterogenous data. In this paper, we demonstrate the Long Short-Term Memory (LSTM) with Recurrent Neural Network (RNN) called as RNN-LSTM. By examining current research difficulties based on evolving feature classification and prediction, we applied the most sophisticated LSTM paired with an attentiveness process approach in this work. Academicians, institutions, and governmental organizations benefit from this study since it allows them to anticipate achievement early. In extensive experimental analysis proposed work compared with numerous state-of-the-art, the better recurrent neural network functionalities of LSTM matching with the hybrid mechanism give higher performance. Predictive performance of 90.50% has achieved using the proposed algorithm.