Contrastive modelling of self‐regulated inquiry behaviours across nations: Evidence from TIMSS 2023 Earth Science

Abstract In an era where science education increasingly values inquiry competencies over rote outcomes, understanding students' strategic and non‐strategic engagement during digital assessments has become critical. This study applies contrastive representation learning to TIMSS 2023 Grade 8 Earth Science process data from five countries to uncover latent pathways of inquiry behaviour. Real‐time digital traces—including screen visit frequency, revisit patterns and time‐on‐task—were embedded into a structured latent space, revealing two distinct clusters that map onto strategic and non‐strategic inquiry profiles when interpreted through SRL theory. K‐means clustering and UMAP visualization confirmed a strong alignment between behavioural profiles and Earth Science achievement outcomes. Cross‐national analyses demonstrated that strategic and non‐strategic engagement structures were highly generalizable across diverse education systems, despite contextual differences. Key behavioural indicators differentiating high‐ and low‐performing profiles were identified, offering new insights into metacognitive regulation during inquiry tasks. By integrating contrastive learning, clustering validation, interpretability and ethical considerations, this study advances process‐driven learning analytics for science education. The findings support the development of adaptive assessments, real‐time scaffolding tools and culturally responsive instructional strategies, providing a blueprint for how AI‐driven methods can enhance inquiry‐based learning while safeguarding equity and learner agency in global science education contexts.