과학교육에서 활용가능한 데이터 자동기록 유형 범주화 및 예시 코드 개발

초록

This study categorizes sensor-based automatic data logging into four types—periodic logging, value- threshold event logging, differential-threshold event logging, and extremum logging—and proposes a Micro:bit-based implementation framework that is applicable to school science education. Based on the researcher’s practical experience, initial classifications were established, and the final categorization was refined through a review process using generative AI. For each type, sample codes were developed using the Micro:bit platform to demonstrate how these logging strategies can enable new forms of scientific inquiry. The proposed types and sample codes were implemented with simple algorithms that teachers and students can easily understand and modify, thereby supporting the selection and application of data collection strategies that are aligned with specific inquiry goals.

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