An Analysis of Latent Profiles of Educational Support in Parents of Early School-Aged Children: Predictors and Differences in Children’s School Adjustment

Boram, No; Park, Saerom; Park, Hye Jun; Choi, Naya

doi:10.5723/kjcs.2020.41.1.15

Korean J Child Stud

2020

DOI: 10.5723/kjcs.2020.41.1.15

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An Analysis of Latent Profiles of Educational Support in Parents of Early School-Aged Children: Predictors and Differences in Children’s School Adjustment

No Boram¹,

Saerom Park²,

Hye Jun Park³

et al.

Abstract: Objectives: This study aimed to investigate the types of educational support for mothers of early school-aged children, identify the influence of education belief and parenting capability on the different types, and compare the level of children's school adjustment by the type of educational support. Methods: A latent profile analysis, multiple logistic regression, and one-way ANOVA were conducted using data on 380 children from the Panel Study on Korean Parent Educational Involvement (PSKPEI) in 2017 (1 st gr… Show more

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Cited by 3 publications

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“…(Jervis & Polland, 2007, as cited in J.-H. Lee & Kim, 2020. (No, Park, Park, & Choi, 2020). (Hill & Tyson, 2009) (J. Y.…”

mentioning

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Investigating Predictors of First Graders’ School Adjustment in Korean Elementary School Using Latent Profile Analysis and Machine Learning

고¹,

전²

2020

Korean J Child Stud

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Objectives:The purpose of this study is to evaluate the factors of first graders' school adjustment in Korean elementary school by using latent profile analysis and machine learning to predict and classify among the first-graders who have difficulty in school adjustment. Methods: Data from 1,010 children in the first grade of elementary school were used in the seventh to eighth surveys of the 2014-2015 Korean Children's Panel. The output variable of machine learning is the school adjustment type of the eighth survey data, and the type and number of each school adjustment were determined by latent profile analysis of its sub-variables. The input variables for machine learning were the children's gender and school readiness of the seventh data. In addition, the children's difficulty in executive function, school preference, parents' interest in school life, teacher's teaching efficacy, and work stress included in the eighth data were selected as input variables. As a model for predicting children's school adjustment type, four machine learning algorithms are used. Results: Three types of school adjustment were identified through latent profile analysis, with the lowest level of school adjustment being 13.1% of all children. "Difficulty in executive function" was the predictor of the lowest level of school adjustment in all machine learning models. The model that best classifies children at the lowest level of school adjustment was the gradient-boosted decision tree model, with an accuracy of 94%, a sensitivity of 70%, a specificity of 97%, and an AUC of .89. Conclusion: When using latent profile analysis and machine learning model, the difficulty in adjusting to school in the first graders of elementary school was most predicted by difficulty of the executive function; however, school readiness was not. This study suggested that cooperation between early childhood education and primary education based on empirical analysis is necessary for successful primary connection.

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“…(Jervis & Polland, 2007, as cited in J.-H. Lee & Kim, 2020. (No, Park, Park, & Choi, 2020). (Hill & Tyson, 2009) (J. Y.…”

mentioning

confidence: 99%

Investigating Predictors of First Graders’ School Adjustment in Korean Elementary School Using Latent Profile Analysis and Machine Learning

고¹,

전²

2020

Korean J Child Stud

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An Analysis of Latent Profiles of Father-Child Interaction: Classification Predictors and Differences in Children’s Socio-Emotional Development

Park

Boram

2021

Journal of Comparative Family Studies

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The purpose of this study was to (1) classify subgroups of father-child interaction based on the type of interaction activity (routine, learning, and play interaction), (2) examine the effects of socio-demographic factors, father’s psycho-emotional factors, and maternal factors on the different types of father-child interaction groups, and (3) analyze differences in socio-emotional development of first graders in elementary school according to the type of father-child interaction. Analysis of 1,469 families (mothers, fathers, and children) was conducted using latent profile analysis (research question 1), complex sample multinomial logistic regression (RQ 2), and complex sample general linear modeling (RQ 3). Samples originated from the eighth wave (2015) of the Panel Study on Korean Children (PSKC). The main results were as follows. First, three distinct latent groups of father-child interaction based on the quantitative level of daily interaction were found: high-interaction (HI, 7.85%), medium-interaction (MI, 51.73%), and low-interaction (LI, 40.42%). Second, factors such as father’s happiness, positive evaluation of work-family balance, and mother-child interaction level were significant correlates for the classification of father-child interaction. Third, first graders in the HI group showed the highest levels of self-esteem in comparison to the other two groups and reported a higher level of subjective happiness in comparison to the LI group. These results bring to attention the importance of father-child interaction affecting the outcomes of children’s socio-emotional development.

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The Effect of Bidirectional Associations Between Mothers’ Social Competence and Understanding of Their Elementary School Children’s School Life on the Career Guidance

Park¹,

Kim²,

Lee³

et al. 2022

JFBL

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An Analysis of Latent Profiles of Educational Support in Parents of Early School-Aged Children: Predictors and Differences in Children’s School Adjustment

Cited by 3 publications

References 8 publications

Investigating Predictors of First Graders’ School Adjustment in Korean Elementary School Using Latent Profile Analysis and Machine Learning

Investigating Predictors of First Graders’ School Adjustment in Korean Elementary School Using Latent Profile Analysis and Machine Learning

An Analysis of Latent Profiles of Father-Child Interaction: Classification Predictors and Differences in Children’s Socio-Emotional Development

The Effect of Bidirectional Associations Between Mothers’ Social Competence and Understanding of Their Elementary School Children’s School Life on the Career Guidance

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