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Personalized Career Guidance for School Students for Smart Education
Subject area: Science,Engineering and Technology · Area of research: Computer Science
DOI: https://doi.org/10.64388/IREV9I10-1716180
Abstract
Career selection is a vital decision which has a strong impact on the academic and professional future of a student. Traditional forms of career counseling methods offer generic advice and do not give appropriate regard to individual aptitude, personality traits and performances at school. This paper suggests a Personalized Career Guidance System which combines psychometric analysis and clustering methods for obtaining customized career guidance to school students. The system uses available academic information, cognitive capabilities and psychological characteristics as input parameters for assigning students into meaningful groups and for making specific career recommendations. Experimental evaluation showed better recommendation accuracy and better student satisfaction than traditional counseling methods. The proposed system is supportive to smart education by enabling structured, data-driven and personalized career planning.
Keywords
Career Guidance, Psychometric Analysis, K-Means Clustering, Educational Data Mining, Personalised Recommendation System.
How to cite this paper
@article{1716180,
author = {Arti B. R., Narmadha M., P. M. C. Nisha},
title = {Personalized Career Guidance for School Students for Smart Education},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {926-929},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716180.pdf},
abstract = {Career selection is a vital decision which has a strong impact on the academic and professional future of a student. Traditional forms of career counseling methods offer generic advice and do not give appropriate regard to individual aptitude, personality traits and performances at school. This paper suggests a Personalized Career Guidance System which combines psychometric analysis and clustering methods for obtaining customized career guidance to school students. The system uses available academic information, cognitive capabilities and psychological characteristics as input parameters for assigning students into meaningful groups and for making specific career recommendations. Experimental evaluation showed better recommendation accuracy and better student satisfaction than traditional counseling methods. The proposed system is supportive to smart education by enabling structured, data-driven and personalized career planning.},
keywords = {Career Guidance, Psychometric Analysis, K-Means Clustering, Educational Data Mining, Personalised Recommendation System.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716180}
}