Evaluating Preschoolers' Block Programming Using Complexity and Personality Traits - Ono, Yui; Saito, Daisuke; Washizaki, Hironori (Proceedings / Conference on Software Engineering Education & Training, 2024)
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 ρ=0.43 , and the correlation with log data is rs=0.92 . Furthermore, analysis using the Big-5 gives ρ=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.