Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students.

ABSTRACT: Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children's academic achievements and ability to adjust to school. We classified executive function difficulties (EFDs) in longitudinal trajectories in Korean children from 7 to 10 years old. We found predictors of EFDs using latent class growth analysis and Bayesian network learning methods with Panel Study data. Three types of latent class models of executive function difficulties were identified: low, intermediate, and high EFDs. The modeling performance of the high EFD group was excellent (AUC = .91), and the predictors were the child's gender, temperamental emotionality, happiness, DSM (Diagnostic and Statistical Manual of Mental Disorders) anxiety probl

SUBMITTER: Goh EK 

PROVIDER: S-EPMC9589973 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature