Family and child characteristics in reading achievement milestones using machine-learning-based survival analysis

Abstract

Objective

This study aimed to identify early reading achievers and uncover family and child factors that mitigate reading skill disparities.

Background

Literacy standards guide educational policy to prevent literacy issues in at-risk children. Many studies lack accurate methods to measure reading milestones, relying on static approaches that miss dynamic longitudinal processes.

Method

This study used machine-learning-based survival analysis on Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 (ECLS-K: 2011) data to analyze children's time to reach reading milestones, examining how family structure, socioeconomic status, gender, and behavioral problems relate to reading achievements.

Results

Being female, from a higher-income family, and not exhibiting behavioral problems increased the likelihood of surpassing reading milestones. Higher socioeconomic status had a stronger positive relation with reading achievement in two-parent families. Externalizing behaviors had a stronger negative relation with reading achievement in girls than boys. The survival tree analysis showed children from two-parent families with incomes at or above 200% of the poverty threshold reached reading milestones earlier. Among these children, those with lower externalizing behaviors achieved them the earliest.

Conclusion

This study supports the family systems theory and the bioecological model, indicating family and child factors, and their interplay, relate to children's reading achievement.

Implications

Machine-learning-based survival analysis enhances the assessment of reading milestones, facilitating early diagnosis, targeted interventions, and effective family policies.