Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search

The purpose of this study is to explore the factors that have the most decisive influence on actual learning intention that leads to participation in adult education. For developing the predictive model, we used tree-based machine learning, with the longitudinal big data (2017-2020) of Korean adults. Based on the gradient boosting machine (GBM) results, among the eleven variables used, the most influential variables in predicting the possibility of lifelong education participation were self-pay education expenses and then highest level of education completed. After the grid search, not only the importance of the two variables but also the overall figures including the false positive rate improved. In future studies, it will be possible to improve the performance of the machine learning model by adjusting the hyper-parameters that can be directly set by less computational methods.

Automated summary

This study aims to identify the key factors that influence the actual learning intention leading to participation in adult education. Using longitudinal big data from Korean adults (2017-2020), a predictive model was developed using tree-based machine learning. The results revealed that self-pay education expenses and the highest level of education completed were the most influential variables in predicting the likelihood of lifelong education participation. After grid search, the importance of these variables as well as overall figures, including the false positive rate, improved. Future studies could further enhance the performance of the machine learning model by adjusting hyperparameters using less computational methods.