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Precision in Progress: Leveraging Data Mining Technique to Empower Career Path Selection for Incoming Senior High School Students

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  • Romeo, Jr. E. Bejar

    (Teacher III, Department of Education, Region XII, Cotabato Division)

Abstract

This research investigated the application of data mining, specifically the Random Forest classification model, to optimize career path selection for incoming Senior High School students in the Philippines. Given the diverse tracks and strands within the SHS program, the traditional decision-making process relies on anecdotal advice, limited exposure, and personal perceptions, often resulting in sub optimal choices. Focused on addressing the complexities introduced by the K-12 educational reform, the study analyzed the data of 1,020 students from three public schools including the Sibsib National High School. The Random Forest model achieved high accuracy (91.2%) and precision (72.6%), with critical attributes identified as Career Prospects, Personal Interests or Skills, and the Monthly Salary Bracket of Parents. While the model excelled overall, there is room for improvement in predicting certain academic tracks, particularly Humanities and Social Sciences (HUMSS). The study recommends refining the model, emphasizing enhancements for specific tracks and continual updates to accommodate evolving student data patterns.

Suggested Citation

  • Romeo, Jr. E. Bejar, 2024. "Precision in Progress: Leveraging Data Mining Technique to Empower Career Path Selection for Incoming Senior High School Students," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 178-191, January.
    • Handle: RePEc:bcp:journl:v:8:y:2024:i:1:p:178-191
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    References listed on IDEAS

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    1. Marivel B. Go & Rodolfo A. Golbin Junior & Severina P. Velos & Johnry P. Dayupay & Feliciana G. Cababat & Jeem Clyde C. Baird & Hazna Quiñanola, 2023. "A data mining approach to classifying e-learning satisfaction of higher education students: a Philippine case," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 33(3), pages 314-329.
    2. Altannar Chinchuluun & Petros Xanthopoulos & Vera Tomaino & P.M. Pardalos, 2010. "Data Mining Techniques in Agricultural and Environmental Sciences," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 1(1), pages 26-40, January.
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