How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system?

Abstract

The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the circulatory system with MetaTutor, 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 scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.