Journal of Practical Engineering Education (실천공학교육논문지)

Korean Institute for Pratical Engineering Education (한국실천공학교육학회)
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Correlation between Vocational Training Evaluation Data and Employment Outcomes: A Study on Prediction Approaches through Machine Learning Models

직업훈련생 평가 데이터와 취업 결과의 상관관계: 머신러닝 모델을 통한 예측 방안 연구

  • Jae-Sung Chun (Department of Computer Engineering Korea University of Technology and Education) ;
  • Il-Young Moon (Department of Computer Engineering Korea University of Technology and Education)
  • 천재성 (한국기술교육대학교 컴퓨터공학과) ;
  • 문일영 (한국기술교육대학교 컴퓨터공학과)
  • Received : 2024.04.03
  • Accepted : 2024.05.20
  • Published : 2024.06.30
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Abstract

This study analyzed various machine learning models that predict employment outcomes after vocational training using pre-assessment data of disabled vocational trainees. The study selected and utilized the most appropriate machine learning models based on a data set containing various personal characteristics, including trainees' gender, age, and type of disability. Through this analysis, the goal is to improve the employment rate and job satisfaction of disabled trainees using only pre-assessment data. As a result, it presents a universal approach that can be applied not only to people with disabilities, but also to vocational trainees from a variety of backgrounds. This is expected to make an important contribution to the development and implementation of tailored vocational training programs, ultimately helping to achieve better employment outcomes and job satisfaction.

본 연구는 장애인 직업훈련생의 사전 평가 데이터를 활용하여 직업 훈련 후 취업 결과를 예측하는 다양한 머신러닝 모델을 분석하였다. 연구는 훈련생의 성별, 연령, 장애 유형 등을 포함하는 다양한 개인적 특성을 포함한 데이터 세트에 기반하여, 가장 적합한 머신러닝 모델들을 선별하고 활용하였다. 이러한 분석을 통해, 사전 평가 데이터만을 사용하여 장애인 훈련생의 취업률 및 직업 만족도 향상을 목적으로 한다. 결과적으로, 장애인뿐만 아니라 다양한 배경을 가진 직업훈련생들에게도 적용할 수 있는 범용적인 접근법을 제시한다. 이는 맞춤형 직업 훈련 프로그램의 개발과 구현에 중요한 기여를 할 것으로 기대되며, 궁극적으로는 더 나은 취업 결과와 직업 만족도를 달성하는 데 도움이 될 것이다.

Keywords

References

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