Evaluating Preschoolers' Block Programming Using Complexity and Personality Traits

Programming learning at an early age effectively fosters logical thinking and self-centeredness, but an appropriate evaluation method for young learners has yet to be established. Herein we propose a new learning evaluation method that incorporates problem constructs and the complexity metrics used in software engineering quality assessments. Specifically, we investigate the relationships between changes in block pro-gramming complexity, personality traits, and learning effects. Evaluation rubrics and log data assess complexity, while person-ality traits are based on Big-5. Then the learning effects of 34 kindergarten children participating in workshops are analyzed in terms of complexity and personality traits. After learning, first-time programmers tend to show a large increase in complexity. The correlation with the rubric score is
$\rho=0.43$
, and the correlation with log data is
$\mathbf{r}s=0.92$
. Furthermore, analysis using the Big-5 gives
$\rho=0.609$
for the rate of increase in extraversion and complexity, indicating a strong relationship between learning effects and personality traits. In the future, this data will be used to build AI tools for automatic evaluations, feedback, and learning curriculum recommendations.