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...
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| Vydané v: | Proceedings / Conference on Software Engineering Education & Training s. 1 - 5 |
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IEEE
29.07.2024
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| ISSN: | 2377-570X |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ono, Yui Saito, Daisuke Washizaki, Hironori |
| Author_xml | – sequence: 1 givenname: Yui surname: Ono fullname: Ono, Yui email: yui.yui.o@fuji.waseda.jp organization: Waseda University,Tokyo,Japan – sequence: 2 givenname: Daisuke surname: Saito fullname: Saito, Daisuke email: d.saito@fuji.waseda.jp organization: Takachiho University,Tokyo,Japan – sequence: 3 givenname: Hironori surname: Washizaki fullname: Washizaki, Hironori email: washizaki@waseda.jp organization: Waseda University,Tokyo,Japan |
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| Snippet | Programming learning at an early age effectively fosters logical thinking and self-centeredness, but an appropriate evaluation method for young learners has... |
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| SubjectTerms | Artificial intelligence Block programming Complexity Complexity theory Conferences Correlation Performance evaluation Personality traits Preschool programming education Programming profession Quality assessment |
| Title | Evaluating Preschoolers' Block Programming Using Complexity and Personality Traits |
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