A Longitudinal Study of Computational Thinking Development in Early Childhood

This three-wave longitudinal study examined change in children’s computational thinking (CT) performance over one year and tested whether gender, age, and family socioeconomic status (SES) predicted initial performance and growth rates. A total of 270 Chinese children (Mage = 56.77 months at T1; 49% girls) completed the Chinese version of TechCheck-K at 6-month intervals, which measures algorithms, modularity, control structures, representation, debugging, and hardware and software. Research Findings: Latent growth curve modeling indicated a linear increase in overall CT scores, averaging 0.796 points per 6-month interval (MT1 = 7.62, SD = 2.07). Gender predicted neither initial performance nor growth rates, whereas age and family SES predicted initial performance but not subsequent change. Exploratory growth mixture modeling identified four trajectory classes: two characterized by moderate initial levels followed by either faster or slower growth, and two characterized by high or low initial levels followed by continued decline. Practice or Policy: The findings reveal substantial interindividual variability in longitudinal CT performance and underscore the potential value of differentiated support, such that children showing persistently low performance or decreasing scores may benefit from additional structured support, whereas those on more favorable trajectories may benefit from challenging, open-ended problem-solving activities.