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

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 p...

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Main Authors: Raja Kumaran, Shamini, Othman, Mohd. Shahizan, Yusuf, Lizawati Mi, Arda Yunianta, Arda Yunianta
Format: Conference or Workshop Item
Published: 2020
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Online Access:http://eprints.utm.my/id/eprint/92745/
http://dx.doi.org/10.1109/AECT47998.2020.9194221
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.927452021-10-28T10:13:47Z http://eprints.utm.my/id/eprint/92745/ Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection Raja Kumaran, Shamini Othman, Mohd. Shahizan Yusuf, Lizawati Mi Arda Yunianta, Arda Yunianta QA75 Electronic computers. Computer science 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. 2020 Conference or Workshop Item PeerReviewed Raja Kumaran, Shamini and Othman, Mohd. Shahizan and Yusuf, Lizawati Mi and Arda Yunianta, Arda Yunianta (2020) Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection. In: 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019, 10 February 2020, AlMadinah, AlManawarrah. http://dx.doi.org/10.1109/AECT47998.2020.9194221
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Raja Kumaran, Shamini
Othman, Mohd. Shahizan
Yusuf, Lizawati Mi
Arda Yunianta, Arda Yunianta
Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
description 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.
format Conference or Workshop Item
author Raja Kumaran, Shamini
Othman, Mohd. Shahizan
Yusuf, Lizawati Mi
Arda Yunianta, Arda Yunianta
author_facet Raja Kumaran, Shamini
Othman, Mohd. Shahizan
Yusuf, Lizawati Mi
Arda Yunianta, Arda Yunianta
author_sort Raja Kumaran, Shamini
title Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
title_short Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
title_full Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
title_fullStr Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
title_full_unstemmed Educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
title_sort educational business intelligence framework visualizing significant features using metaheuristic algorithm and feature selection
publishDate 2020
url http://eprints.utm.my/id/eprint/92745/
http://dx.doi.org/10.1109/AECT47998.2020.9194221
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