Prior research has focused extensively on how emotion tendencies (e.g., duration, frequency, intensity, and valence) affect students’ performance, but little is known about emotion variability (i.e., the fluctuations in emotion states) and how emotion variability affects performance. In this paper, emotion variability was examined among 21 medical students in the context of solving two patient cases of different complexity with BioWorld, a computer-based intelligent tutoring system. Specifically, we examined the influences of task complexity on emotion variability, emotion variability in self-regulated learning (SRL) phases, and the differences in emotion variability between high and low performers. We found that students’ emotion variability varies depending on the SRL phases (i.e., forethought, performance, and self-reflection) and task complexity. High performing students had smaller emotion variability than low performers across the three SRL phases, but the differences in emotion variability were not statistically significant. Moreover, emotion variability in the forethought phase contributed most to high performance when compared to the emotional variability in the performance and self-reflection phases. This study advances theoretical development about emotion variability and provides insights that help explain the mixed results that existed in extant literature.

先前的研究广泛地集中在情绪倾向（例如持续时间，频率，强度和价数）如何影响学生的表现上，但是对情绪变异性（即情绪状态的波动）以及情绪变异性如何影响表现知之甚少。在本文中，通过使用基于计算机的智能辅导系统BioWorld解决了两个复杂程度不同的患者案例，研究了21名医学生的情绪变异性。具体来说，我们研究了任务复杂性对情绪变异性，自我调节学习（SRL）阶段的情绪变异性以及高绩效者与低绩效者之间的情绪变异性的影响。我们发现，学生的情绪变异性取决于SRL阶段（即，前瞻性，表现，和自我反省）和任务的复杂性。在三个SRL阶段中，表现优异的学生的情绪变异性低于表现较差的学生，但情绪变异性的差异在统计学上并不显着。此外，与表演和自我反思阶段的情绪变异相比，前瞻性阶段的情绪变异对高性能的贡献最大。这项研究推进了关于情绪变异性的理论发展，并提供了有助于解释现存文献中混合结果的见解。与表演和自我反思阶段的情绪变异相比，前瞻阶段的情绪变异对高性能的贡献最大。这项研究推进了关于情绪变异性的理论发展，并提供了有助于解释现存文献中混合结果的见解。与表演和自我反思阶段的情绪变异相比，前瞻阶段的情绪变异对高性能的贡献最大。这项研究推进了关于情绪变异性的理论发展，并提供了有助于解释现存文献中混合结果的见解。