["British Journal of Educational Technology, EarlyView. ", "\nAbstract\nThe rapid growth of massive online learning has intensified interest in self‐regulated learning (SRL) and socially shared regulation of learning (SSRL), yet empirical insights into their interplay in online collaborative learning (OCL) remain limited. This study employed a three‐layer analytical method combining cluster analysis, content analysis and complex network analysis to investigate how SSRL patterns evolve in relation to individual SRL profiles. Data from 60 undergraduates in a 16‐week course included over 16,000 trace entries (e.g. posts, replies, peer feedback) were collected and analysed. Results revealed that (1) three profiles of SRL were identified, based on learners' time investment, study regularity and help‐seeking behaviours; (2) groups with higher SSRL behavioural interaction displayed a more diverse and balanced role composition; and (3) distinct SSRL patterns emerged across SRL profiles over time. Individuals with high SRL profiles more frequently connected idea sharing with strategy using and process monitoring, while those with low SRL profiles relied on limited strategies focused mainly on idea sharing. These findings deepen the understanding of how individual regulation shapes group dynamics in online collaboration and suggest that instructional design should consider learners' SRL profiles when scaffolding their collaborative regulation processes.\n\n\nPractitioner Notes\n\nWhat is already known about this topic\n\n\n\nSelf‐regulated learning (SRL) and socially shared regulation of learning (SSRL) are both essential for effective online collaborative learning (OCL), with prior research recognising their theoretical interplay.\n\nTrace data (e.g. login frequency, timestamp and posts) offer valuable opportunities to uncover how learners engage in and coordinate regulatory behaviours throughout online collaboration.\n\n\n\n\nWhat this paper adds\n\n\n\nIntegrating SRL profiling with mixed network analysis, this study shows how individual SRL profiles drive divergent SSRL development, highlighting shared coordination patterns and evolving interactive roles across phases.\n\nGroups with higher SSRL interaction exhibited more diverse and balanced role composition, with central roles (e.g. leaders, animators) facilitating stronger collaborative regulation processes.\n\nTime‐series epistemic network analysis (ENA) provides a novel analytical lens to track the evolution of SSRL patterns across collaborative phases, linking SRL profiles with group‐level regulatory dynamics.\n\n\n\n\nImplications for practice and/or policy\n\n\n\nTailoring support to SRL profiles allows educators to offer targeted scaffolds, using prompts and planning tools for low SRL learners, while encouraging high SRL learners to lead coordination and monitoring in SSRL.\n\nRole‐driven group design promotes collaboration structured by learners' SRL profiles.\n\n\n\n\n\n"]