한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제29권10호
  • /
  • Pages.113-120
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  • 2024
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Exploring Predictive Models for Student Success in National Physical Therapy Examination: Machine Learning Approach

  • Bokyung Kim (Dept. of Physical Therapy, Changshin University) ;
  • Yeonseop Lee (Dept. of Physical Therapy, Daewon University) ;
  • Jang-hoon Shin (Industry-Academy Cooperation Foundation, Sahmyook University) ;
  • Yusung Jang (Dept. of Physical Therapy, Gangdong University) ;
  • Wansuk Choi (Dept. of Physical Therapy, Kyungwoon University)
  • 투고 : 2024.08.23
  • 심사 : 2024.09.27
  • 발행 : 2024.10.31
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초록

본 연구는 물리치료학과 학생들의 국가시험 합격률을 예측하는 데 있어 머신러닝 모델의 효과성을 검증하고자 한다. 기존의 성적 예측 방법은 주로 과거 학업 성적이나 인구 통계 데이터를 기반으로 하지만, 본 연구는 모의시험 점수를 머신러닝 및 딥러닝 기법으로 분석하여 보다 정확한 예측을 시도하였다. 한국의 5개 대학에서 총 1,242명의 학생 데이터를 수집하고 전처리한 후, 다양한 모델을 활용하여 분석을 진행하였다. ChatGPT4의 도움을 받아 생성 및 개선된 모델을 데이터셋에 적용한 결과, H2OAutoML (GBM2) 모델이 98.4%의 정확도로 가장 우수한 성능을 보였으며, TabNet, LightGBM, RandomForest 모델 역시 높은 성능을 나타냈다. 본 연구는 H2OAutoML(GBM2)이 국가시험 합격 여부를 예측하는 데 있어 뛰어난 효과를 발휘함을 보여주며, 이러한 AI지원 모델들이 의학 교육 및 정책에 크게 기여할 수 있음을 시사한다.

This study aims to assess the effectiveness of machine learning models in predicting the pass rates of physical therapy students in national exams. Traditional grade prediction methods primarily rely on past academic performance or demographic data. However, this study employed machine learning and deep learning techniques to analyze mock test scores with the goal of improving prediction accuracy. Data from 1,242 students across five Korean universities were collected and preprocessed, followed by analysis using various models. Models, including those generated and fine-tuned with the assistance of ChatGPT-4, were applied to the dataset. The results showed that H2OAutoML (GBM2) performed the best with an accuracy of 98.4%, while TabNet, LightGBM, and RandomForest also demonstrated high performance. This study demonstrates the exceptional effectiveness of H2OAutoML (GBM2) in predicting national exam pass rates and suggests that these AI-assisted models can significantly contribute to medical education and policy.

키워드

과제정보

This research was supported by a Research Grant of Kyungwoon University in 2024.

참고문헌

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