Developing Prediction Model for Children’s Social Competence Using Machine Learning

Abstract

Objectives: This study aims to identify the types of latent classes of children’s social competence, and to develop a model using machine learning to predict the type and identify relatively important variables.Methods: Data were collected from 466 children aged three to five years and their mothers. Children’s social competence was classified by level. Latent class analysis, machine learning model construction, and performance evaluation were performed using R 3.6.1 and R-Studio 1.2.5033. The machine learning algorithms used were logistic regression, lasso logistic regression, random forest, and gradient-boosted decision tree models.Results: First, according to the characteristics of the latent class of children’s social competence, it was classified into two types: ‘high level’ and ‘low level’. Second, a machine learning algorithm was applied according to the latent class. The best performing model was the random forest model. Third, the most important variable in predicting the social competence type was identified as ‘harm avoidance’ in the children’s temperament. Fourth, another major variable was a ‘shift’ in the children’s executive functions.Conclusion: This study is meaningful as it suggests the possibility of predicting and discriminating children’s social competence and various developmental aspects by applying machine learning, the latest technique, to predict the types of children’s social competence.