["British Journal of Educational Technology, EarlyView. ", "\nAbstract\n\nTo ensure learning efficiency in game‐based learning (GBL), learners must regulate cognitive, affective, metacognitive and motivational (CAMM) processes, collectively known as self‐regulated learning (SRL). SRL is dynamic and non‐linear, characterized by regulatory patterns and CAMM interactions that lead to macro‐level SRL behaviours. In this paper, we explore SRL as a complex system by analysing patterns and interactions among CAMM processes during GBL and examine their relation to learning outcomes. Thirty‐seven (n = 37) healthy adults used Antidote COVID‐19, a GBL environment designed to increase emotional engagement and biology knowledge. To define momentary CAMM processes, subjective measures of cognitive, affective and metacognitive states via think‐ and emote‐alouds were synchronized with two physiological correlates of motivation: intensity of facial expressions (arousal) and skin conductance response. Sequence analysis and recurrence quantification defined the regularity of patterns within CAMM components, and transfer entropy estimated concurrent interactions between CAMM components. Our results indicated a quadratic relationship between the turbulence (a measure of regularity) of cognitive, affective and metacognitive (CAM) state transitions (via think‐ and emote‐aloud methods) provided the best fit, explaining 31% of variability in learning. This highlights an optimal balance between stability and flexibility in CAM state transitions that were beneficial for learning. However, physiological correlates of motivation were not predictive regarding learning performance. This lack of predictive capability of the considered measures may reflect their limitations in capturing nuanced motivation dynamics relevant to learning or suggest that motivation's role in learning is predominantly mediated through cognitive and metacognitive engagement rather than directly influencing outcomes.\n\n\n\n\nPractitioner notes\nWhat is already known about this topic\n\nGame‐based learning (GBL) is a promising intervention for improving science knowledge, but it places high self‐regulatory demands on learners.\nLearners must manage cognitive, affective, motivational and metacognitive (CAMM) processes to ensure GBL efficiency; otherwise known as self‐regulated learning (SRL).\nPrior research has shown that CAMM processes are each important for learning in games, but they are often studied in isolation rather than as interacting processes over time.\n\nWhat this paper adds\n\nThis study conceptualizes SRL in GBL as a complex system utilizing mixed multimodal data, emphasizing patterns and interactions among CAMM processes rather than static averages.\nResults show that learning is maximized when learners exhibit an optimal balance between stability and flexibility in their cognitive, affective and metacognitive state transitions, neither overly rigid nor overly random regulation patterns.\nCognitive, affective and metacognitive processes (assessed via think–/emote‐alouds) were strongly related to learning gain, whereas physiological indicators of motivation alone were not predictive.\n\nImplications for practice and/or policy\n\nDesigners of GBL environments should support adaptive regulation, encouraging learners to flexibly shift SRL strategies and emotions while maintaining coherence in their learning process.\nEducators and researchers should be cautious about relying solely on physiological measures (eg, arousal) for assessing learning effectiveness, as these may not capture meaningful regulatory dynamics.\nPolicies and evaluation frameworks for educational games should prioritize tools and analytics that capture process‐level SRL patterns over time, rather than focusing exclusively on outcomes or isolated behavioural indicators.\n\n\n\n\n"]