Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes

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.

A systematic review of research on artificial intelligence in higher education: Practice, gaps, and future directions in the GCC

“…Table 4 summarizes the papers analysed, including information about the AI applied, who used the AI, and what main benefits it provided. In brief, the results revealed that the main advantages of AI applications in higher education were to improve educational outcomes [3,7,12,16,18,22,27,29,30,32], to enhance institutional decision-making [1,8,14,21,26], and to advance institutional systems [2,10,23]. Moreover, the end users benefiting from the AI applications mainly were higher education institutions (n=11), instructors (n=9), and college students (n=6).…”

“…Improve educational decision-making processes, such as the admission process, curriculum, and learning activities [27] Regarding decision-making, paper [1] used different machine learning algorithms to advance data analysis at a higher education institute in Oman. Data from 1020 students were utilized to examine whether academic performance could predict the no-detriment rate.…”

“…Regarding gaps, issues, and research directions about machine learning, paper [1] shows that most of the machine learning research concentrates on students and instructors, disregarding the needs of administrators to handle complex decision-making for coming up with effective policies, strategies, and actions to improve quality of education. Researchers suggest more studies use machine-learning algorithms to help in decision-making at the tertiary level.…”