Machine Learning for Smart School Selection and Admissions
Admissions; Artificial Intelligence; Data-driven Decision Making; Education Technology; Educational Data Analysis; Machine Learning; Parental Decision-Making; Recommendation System; School Selection; Smart School
Choosing the best school for their kid is an important choice that parents must make, and it is sometimes stressful and unsure. Machine learning is a potential way to improve and streamline the admissions and school selection process in the current digital era. This study investigates the use of machine learning methods in the context of selective admissions and smart school selection. We propose a user-friendly, web-based tool in the early phases of our study that helps parents and guardians locate the ideal school for their kid by using machine learning algorithms. To provide individualized school recommendations, the platform gathers and analyses a range of data, such as extracurricular activity participation, academic achievement, regional preferences, and school reputation. This makes choosing a school easier and supports parents in making wise choices. This paper's second section explores the technical details of the machine learning techniques used, going into the nuances of feature selection, data preparation, and model assessment. We also draw attention to the difficulties and moral issues - such as maintaining impartiality and avoiding bias - that come with using machine learning to school selection. 2023 IEEE.
Praveenraj D.D.W.; Victor M.; Subramani K.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
Institute of Electrical and Electronics Engineers Inc.
Praveenraj D.D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Victor M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Subramani K., School of Business and Management, Christ (Deemed to Be University), Bangalore, India