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The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cog-nitive and metacognitive self-regulated learning processes as they learned about the circulatory system withMetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scoresof emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted theaccuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted theaccuracy of taking notes, a cognitive learning strategy. These results have implications for understanding thebeneficial role of negative emotions during learning with advanced learning technologies. Future directionsinclude providing students with feedback about the benefits of both positive and negative emotions duringlearning and how to regulate specific emotions to ensure the most effective learning experience with advancedlearning technologies