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Exploring Predictive Models for Student Success in National Physical Therapy Examination: Machine Learning Approach

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.113-120
  • DOI : 10.9708/jksci.2024.29.10.113
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 23, 2024
  • Accepted : September 27, 2024
  • Published : October 31, 2024

Bokyung Kim 1 Yeonseop Lee 2 Jang-hoon Shin 3 Yusung Jang 4 Choi, Wan Suk 5

1창신대학교
2대원대학교
3삼육대학교
4강동대학교
5경운대학교

Accredited

ABSTRACT

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.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.