Goh Eun-Kyoung, Jeon Hyo-Jeong
Human Life Research Center, Dong-A University, Saha-gu, Busan 49315, Korea.
Department of Child Studies, Dong-A University, Saha-gu, Busan 49315, Korea.
J Intell. 2022 Sep 23;10(4):74. doi: 10.3390/jintelligence10040074.
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 problems, and the mother's depression as well as coparenting conflict recognized by the mother. The results show that using latent class growth analysis and Bayesian network learning are helpful in classifying the longitudinal EFD patterns in elementary school students. Furthermore, school-age EFD is affected by emotional problems in parents and children that continue from early life. These findings can support children's development and prevent risk by preclassifying children who may experience persistent EFD and tracing causes.
执行功能是一种调节行为或思维以完成任务的心理能力。这对儿童的学业成绩和适应学校的能力在发展过程中至关重要。我们对7至10岁韩国儿童执行功能困难（EFDs）的纵向轨迹进行了分类。我们使用面板研究数据，通过潜在类别增长分析和贝叶斯网络学习方法，找到了EFDs的预测因素。确定了三种执行功能困难的潜在类别模型：低、中、高EFDs。高EFD组的建模性能优异（AUC = 0.91），预测因素包括儿童的性别、气质性情绪、幸福感、《精神疾病诊断与统计手册》（DSM）焦虑问题、母亲的抑郁以及母亲所认识到的共同养育冲突。结果表明，使用潜在类别增长分析和贝叶斯网络学习有助于对小学生的纵向EFD模式进行分类。此外，学龄期EFD受到父母和儿童从早年就持续存在的情绪问题的影响。这些发现可以通过对可能经历持续性EFD的儿童进行预分类并追踪原因，来支持儿童的发展并预防风险。
