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<p>Scientific reasoning, as a core component of scientific thinking, is widely regarded as important for supporting students’ science learning and innovation-related competencies. However, fewer studies have translated theoretical models of scientific reasoning into classroom-based instructional designs supported by digital tools. This study integrated the Data-Mechanism Coordination and Reasoning (DMCR) dual-pathway framework with Scratch graphical programming and PhET virtual simulations. The course design, which emphasizes control of variables (COV), data analysis (DA), and causal decision-making (CDM), was implemented in a 12-week classroom intervention with 104 fifth-grade students. In this sample, we observed statistically significant pre–post increases in students’ task-based measures of scientific reasoning, particularly in COV and DA; these increases were evident across students with higher and lower baseline scores. Students’ performance also differed across the three dimensions: performance was highest in COV, followed by DA, while CDM remained comparatively weaker. The study provides classroom-based evidence on how a DMCR-informed digital learning design may support scientific reasoning practices among fifth-grade students and offers implications for curriculum design and digital pedagogy in science education.</p>

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Integrating Digital Tools into the DMCR Framework to Enhance Upper-Primary Students’ Scientific Reasoning

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Yulong Zhang,
  • Shiyang Pang,
  • Ruyi Jiang

摘要

Scientific reasoning, as a core component of scientific thinking, is widely regarded as important for supporting students’ science learning and innovation-related competencies. However, fewer studies have translated theoretical models of scientific reasoning into classroom-based instructional designs supported by digital tools. This study integrated the Data-Mechanism Coordination and Reasoning (DMCR) dual-pathway framework with Scratch graphical programming and PhET virtual simulations. The course design, which emphasizes control of variables (COV), data analysis (DA), and causal decision-making (CDM), was implemented in a 12-week classroom intervention with 104 fifth-grade students. In this sample, we observed statistically significant pre–post increases in students’ task-based measures of scientific reasoning, particularly in COV and DA; these increases were evident across students with higher and lower baseline scores. Students’ performance also differed across the three dimensions: performance was highest in COV, followed by DA, while CDM remained comparatively weaker. The study provides classroom-based evidence on how a DMCR-informed digital learning design may support scientific reasoning practices among fifth-grade students and offers implications for curriculum design and digital pedagogy in science education.