OVERVIEW OF MACHINE LEARNING METHODS FOR ACADEMIC SCHEDULING

O. Zanevych, Vitaliy Kukharskyy

Abstract


Academic scheduling is assigning some educational activities to available resources, which may include time slots, classrooms, or instructors. Scheduling in the educational field is considerably complicated because of its complexity and dynamic constraints bound to change quickly. Machine learning thus could be a promising solution to data-driven techniques for this complex problem. This article surveys several ML techniques that have been utilized for academic scheduling, such as supervised learning, unsupervised learning, reinforcement learning, and hybrid approaches. We consider the implementation of these methods in increasing the effectiveness and elasticity of the scheduling systems, addressing the particular constraints encountered, and effecting an overall improvement of satisfaction for the students, teachers, and administrators. This will compare, through strengths and weaknesses, different ML techniques to provide insights for the most effective strategies to develop an academic scheduling solution.

Keywords: Academic Scheduling, Machine Learning, Resource Optimization, Educational Institutions, Dynamic Scheduling, Data-Driven Techniques.


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References


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DOI: http://dx.doi.org/10.30970/eli.27.8

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