International Journal For Multidisciplinary Research

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A Data Analytical Survey on School Selection in the Kerala Scenario for Proposing an ML-based Approach for Improvising the Public Sector

Author(s) Devika E., Dr. Manusankar C., Ambili M.S., Dr. Prathibha P.H.
Country India
Abstract Education aids individuals in improving society. It helps a person to acquire more knowledge and to know more about the technology. Machine Learning in education is a form of personalized learning that could be used to give each student an individualized educational experience. It is a subset of Artificial Intelligence (AI) that helps computers or teaching machines learn from all previous data and make intelligent decisions. Artificial Intelligence in the classroom allows teaching to be differentiated and personalized. Here, the students are guided in their learning, can follow the pace they want, and make their own decisions about what to learn. There are different types of education that a student may receive in today’s society. The choices are narrowed down into two main categories, public and private schools. In this paper, we are trying to figure out whether it’s better to send their children to private schools rather than public schools. Here, we are conducting a survey based on this topic. According to this survey, we reach a conclusion that will be portrayed at the end session of this paper. The survey's results will be a starting point for creating ML-based solutions to increase the efficiency of the education sector.
Keywords Education, School Choice, School Quality, Machine Learning, Artificial Intelligence, Employment
Field Sociology > Education
Published In Volume 4, Issue 4, July-August 2022
Published On 2022-08-15
Cite This A Data Analytical Survey on School Selection in the Kerala Scenario for Proposing an ML-based Approach for Improvising the Public Sector - Devika E., Dr. Manusankar C., Ambili M.S., Dr. Prathibha P.H. - IJFMR Volume 4, Issue 4, July-August 2022. DOI 10.36948/ijfmr.2022.v04i04.041
DOI https://doi.org/10.36948/ijfmr.2022.v04i04.041
Short DOI https://doi.org/10/gqpmmp
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