Deep Learning-Based Education Decision Support System for Student E-learning Performance Prediction

Abstract

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students.