Classroom study shows that iterative knowledge building-modeling advances systems thinking in elementary students, indicating the epistemic benefit of discourse analytics in science learning.
This study investigates recursive cycles of knowledge building-modeling (KBM) supported by knowledge building analytics to foster reflection, model improvement, and systems thinking. A total of 43 grade 5 students in China constructed models of Earth Science systems that served as objects of inquiry in Knowledge Forum. Models were refined as students engaged in knowledge building discourse, using knowledge building analytics to visualize the interconnectedness of ideas and progressively enhance models that link Earth Science phenomena to sustainability. Successive model iterations showed increased modeling sophistication and progression of systems thinking from individual variables to cause–effect relationships, secondary effects, temporal accounts, and predictions of behaviors. Students also demonstrated significant gains in their domain knowledge of Earth Science. Qualitative analyses identified mediating processes and system-level dynamics: (1) idea generation and improvement through collaborative knowledge building discourse, (2) analytics used for meta-reflection and conceptual reorganization, and (3) model construction for system expansion and refinement. These findings illustrate how students appropriated KBM and analytics as epistemic frameworks to engage in recursive design and refinement for systems thinking and knowledge advances. The research demonstrates how computer-supported collaborative learning (CSCL) enriched with model-based inquiry and knowledge building analytics can foster students’ engagement with complex systems.
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Soliman et al. (2026) studied this question.
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