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Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(8), pp.49-58
  • DOI : 10.9708/jksci.2023.28.08.049
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 21, 2023
  • Accepted : August 21, 2023
  • Published : August 31, 2023

Hyeon Gyu Kim 1

1삼육대학교

Accredited

ABSTRACT

Dropouts of students not only cause financial loss to the university, but also have negative impacts on individual students and society together. To resolve this issue, various studies have been conducted to predict student dropout using machine learning. This paper presents a model implemented using DNN (Deep Neural Network) and LGBM (Light Gradient Boosting Machine) to predict dropout of university students and compares their performance. The academic record and grade data collected from 20,050 students at A University, a small and medium-sized 4-year university in Seoul, were used for learning. Among the 140 attributes of the collected data, only the attributes with a correlation coefficient of 0.1 or higher with the attribute indicating dropout were extracted and used for learning. As learning algorithms, DNN (Deep Neural Network) and LightGBM (Light Gradient Boosting Machine) were used. Our experimental results showed that the F1-scores of DNN and LGBM were 0.798 and 0.826, respectively, indicating that LGBM provided 2.5% better prediction performance than DNN.

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