Machine learning for strategic decision making during COVID-19 at higher education institutes

Ahmed, Amjed Sid Ahmed Mohamed Sid; Malik, Mazhar Hussain

Abstract

Machine learning is becoming driving force for strategic decision making in higher educational institutions and it calls for cooperation between stakeholders and the use of efficient computation methods. Contrariwise, making decisions might consume much time, if there is no use of data and computational methods during the process of decision making. The utilization of machine learning is essential when coming up with an ultimate analysis of data and decision making. Besides, the technology which is under artificial intelligence could facilitates incredible output for educational institutes when it came to decision making. This paper analyses the output generated using machine learning algorithms that help in prediction of no detriment policy applicability rate in the case of e-learning during COVID-19. The study investigates the performance of machine learning algorithms for strategic decision making in the higher educational institutes, Global College of Engineering and Technology in particular, whether no detriment policy will be applicable for a particular student based on students performance before COVID-19. The study shown that Random Forest machine learning algorithm performance is higher as compare to Support Vector Machine, Decision Tree and Navie Bayes.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
• Start Date: Nov 8, 2020
• End Date: Nov 9, 2020
• Acceptance Date: Nov 1, 2020
• Publication Date: Jan 15, 2021
• Deposit Date: Nov 10, 2022
• Publicly Available Date: Jan 16, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 663-668
• Book Title: 2020 International Conference on Decision Aid Sciences and Application (DASA)
• ISBN: 978-1-7281-9677-0
• DOI: https://doi.org/10.1109/DASA51403.2020.9317042
• Keywords: COVID-19, Machine Learning Algorithms, No Detriment Policy, Strategic Decisions, Machine learning algorithms, Decision making, Support vector machines, Decision trees, Computational modeling, Machine learning, Data models
• Public URL: https://uwe-repository.worktribe.com/output/10133569
• Publisher URL: https://ieeexplore.ieee.org/abstract/document/9317042
• Related Public URLs: https://ieeexplore.ieee.org/xpl/conhome/9316858/proceeding