@article{ART002987875},
author={Hyeon Gyu Kim},
title={Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={8},
pages={49-58},
doi={10.9708/jksci.2023.28.08.049}
TY - JOUR
AU - Hyeon Gyu Kim
TI - Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 8
PB - The Korean Society Of Computer And Information
SP - 49
EP - 58
SN - 1598-849X
AB - 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.
KW - Dropout prediction;DNN;LGBM;Oversampling;SMOTE
DO - 10.9708/jksci.2023.28.08.049
ER -
Hyeon Gyu Kim. (2023). Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students. Journal of The Korea Society of Computer and Information, 28(8), 49-58.
Hyeon Gyu Kim. 2023, "Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students", Journal of The Korea Society of Computer and Information, vol.28, no.8 pp.49-58. Available from: doi:10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim "Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students" Journal of The Korea Society of Computer and Information 28.8 pp.49-58 (2023) : 49.
Hyeon Gyu Kim. Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students. 2023; 28(8), 49-58. Available from: doi:10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim. "Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students" Journal of The Korea Society of Computer and Information 28, no.8 (2023) : 49-58.doi: 10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim. Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students. Journal of The Korea Society of Computer and Information, 28(8), 49-58. doi: 10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim. Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students. Journal of The Korea Society of Computer and Information. 2023; 28(8) 49-58. doi: 10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim. Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students. 2023; 28(8), 49-58. Available from: doi:10.9708/jksci.2023.28.08.049
Hyeon Gyu Kim. "Performance Comparison of Neural Network and Gradient Boosting Machine for Dropout Prediction of University Students" Journal of The Korea Society of Computer and Information 28, no.8 (2023) : 49-58.doi: 10.9708/jksci.2023.28.08.049
