Opioid and Neuropathic Pain Medication Use After ACDF for Degenerative Cervical Spine Disease-Nationwide FinSpine Register Study.

Epistemic emotions, such as surprise, curiosity and confusion, constitute a vital subset of academic emotions (Muis et al., ; Pekrun et al., ) and have been increasingly studied, particularly for their relations to cognitive engagement, self‐regulation strategies and academic outcomes (Chevrier et al., ; Vogl et al., ; Yang et al., ). These emotions are typically defined as those directly associated with the quality of knowledge and knowledge‐generation processes. They often arise when learners encounter discrepancies between new task‐related information and their prior knowledge, beliefs or expectations (Muis et al., ; Pekrun et al., ). The intensity and nature of these discrepancies give rise to distinct emotional responses: for example, students may feel surprise when faced with unexpected information, which can then develop into curiosity or confusion depending on how manageable the discrepancy is (Muis et al., ; Rosman & Mayer, ; Vogl et al., ). Once the inconsistency is resolved, learners may experience positive emotions such as enjoyment or satisfaction (Muis et al., ). These dynamic shifts indicate that epistemic emotions are not static but fluctuate as learners engage with challenges over time. To better understand these fluctuations, this study adopts Zimmerman's () cyclical phase model of self‐regulated learning (SRL), which divides the learning process into forethought, performance and self‐reflection phases. As students engage in the three SRL phases, they conduct various epistemic activities, such as elaboration and evaluation. This creates an opportunity to observe the emotions students experience and how these emotions change as they transition between different SRL phases. In particular, we explore two temporal dynamics of epistemic emotions: (1) how a specific emotion varies across SRL phases and (2) how transitions occur between different emotions. While theoretical models have proposed such temporal trajectories, e.g., a shift from surprise to confusion, empirical evidence remains scarce. Additionally, further investigation into how emotional trajectories differ between high‐ and low‐performing student groups remains a critical area. By addressing these differences, researchers and developers can design emotion‐aware modules capable of monitoring the temporal fluctuations of epistemic emotions. Based on the emotional trajectories, such systems could then provide tailored and timely interventions to scaffold optimized learning processes. Moreover, most prior research has relied heavily on self‐reports to measure epistemic emotions, overlooking complementary insights from physiological measures. Electrodermal activity (EDA), which reflects skin conductance changes due to sweat gland activity, has been increasingly used to indicate emotional arousal in learning settings (Boucsein, ). For instance, researchers have employed mean EDA levels and the frequency of EDA peaks to infer learners' arousal states (Huber & Bannert, ; Nguyen et al., ; Zhang et al., ). Although EDA excels at capturing dynamic arousal, it cannot distinguish between specific emotional states, highlighting the importance of integrating self‐reports for a fuller understanding of emotional dynamics during SRL. Therefore, this study combines self‐reported data with EDA measures to investigate the temporal dynamics of epistemic emotions across SRL phases in the context of diagnostic problem‐solving.