Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection

Abstract

Educational business intelligence concerns the decision-making in the education sector and this article intends to analyse the student's attributes' contribution toward graduating within the duration. In this research, the framework identifies the best set of attributes and evaluates the performance of the model with the help of 22 input features. This article discussed the development of the business intelligence (BI) framework for the higher education that is able to explore, analyse and visualize the relevant data into information. This is to assist the top management in improving the methodologies in teaching and learning. In this case study, the framework used metaheuristic algorithm, Ant Colony Optimization (ACO) technique mainly to identify the best set of attributes, and the performance was validated using Support Vector Machine (SVM). The framework consists of four layers which are data source, data integration, analytics, and access layers. In this study, 46, 658 input data were processed for the identification of postgraduate students who completed their studies within a specified period. The performance evaluation of the data achieved accuracy, sensitivity and precision of 87.44% for PhD dataset and t-test has been conducted to prove that the selected features are significant. Based on the findings, the results from the proposed educational business intelligence framework produced BI dashboard as an output from the framework is capable to act as a decision-making tool for education management and educational technology system.