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

Chayoung Kim and Taejung Park
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Chayoung Kim: College of Liberal Arts and Interdisciplinary Studies, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si 16227, Gyeonggi-do, Korea
Taejung Park: Department of Life-Long Education & Counseling, College of Future Convergence, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Korea

Sustainability, 2022, vol. 14, issue 9, 1-13

Abstract: 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.

Keywords: lifelong learning intention; machine learning; gradient boosting machine (GBM); grid search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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