Authors :

Kavitha and Ashok Kumar

Address :

Dept of Computer Science and Engineering, Vinayaga Missions Kirupanda variyar Engineering College, Vinayaga Missions Research Foundation (Deemed to be University), Salem

Dept of Computer Science and Engineering , Skyline University, Nigeria

Abstract :

Improving school administration procedures is a crucial goal in the constantly changing field of education. Genetic algorithms (GA), influenced by natural selection, repeatedly examine possible solutions, assess their suitability, and progress towards the best patterns. In educational settings, this repeated improvement process enables the identification of concealed patterns in various datasets, such as student performance statistics, resource distribution trends, and administrative procedures. By using genetic algorithms, policymakers and educators have access to an advanced set of tools for making decisions based on data and improving processes. This paper introduces an Educational Optimization Genetic Algorithm (EOGA), a new approach for pattern mining to enhance school management operations. The suggested method deals with the changing nature of educational leadership by providing flexibility to adapt to different situations and developing priorities. Genetic algorithms help continuously improve and optimize school administration procedures, allowing institutions to adjust to new challenges and possibilities. By using genetic algorithm-based pattern mining, educators and others can discover detailed insights that standard analytical methods could miss. These understandings help administrators make educated decisions about resource distribution, curriculum improvement, and student assistance, leading to better educational results and organizational efficiency. By adopting a data-focused method, schools can go beyond conventional thinking and make ongoing enhancements in several aspects of school administration. Incorporating genetic algorithms into educational practices significantly promotes creativity, effectiveness, and high quality in education.

Keywords :

Educational Optimization Genetic Algorithm; Pattern mining; School management; Educational optimization; Decision-making; Student performance.

DOI :

https://doi.org/10.70023/piqm24124