Influence of Learner's Emotional Interactions on Self-Aware Cognitive Decisions and Achievement in Sophisticated Educational Technology
MetaTutorIVH, an advanced learning technology, is making waves in the field of human biology education. Unlike traditional learning technologies, MetaTutorIVH employs facial expression recognition to track student emotions, offering a unique approach to learning.
Recent studies have revealed that transitions between emotions like confusion, frustration, and joy during learning with MetaTutorIVH significantly impact students’ performance and retrospective confidence judgments. While experiencing confusion can prompt deeper problem-solving, frustration, if unresolved, may hinder performance. Joy, as a positive emotion, tends to increase motivation and reinforce learning outcomes.
Interestingly, no significant differences in performance were observed in the presence of affective states like confusion, frustration, and joy. However, the impact of emotions on students' retrospective confidence judgments is substantial. Confusion and joy positively affect confidence, while frustration and transition from confusion to frustration negatively impact it.
These impacts on confidence remain even after accounting for individual differences in multiple-choice confidence. The findings suggest that MetaTutorIVH's focus on emotional tracking and analysis could potentially improve learning outcomes by enhancing student confidence.
Moreover, transitions from confusion to frustration were observed at a significantly high likelihood within MetaTutorIVH. This observation underscores the importance of MetaTutorIVH's analysis of transitions between emotions for their role in learning.
By adapting instructional strategies to mitigate negative emotions like frustration, MetaTutorIVH aims to maintain engagement and improve learning outcomes. For instance, it provides hints or encouragement when it detects frustration, thereby helping students navigate phases of confusion and frustration more effectively.
These findings align with those from meta-cognitive training programs, which show that understanding and regulating cognitive and affective states enhances meta-cognitive skills and mastery. In summary, emotional transitions influence learning by affecting engagement, cognitive effort, and motivational regulation, all of which impact students’ performance and confidence in MetaTutorIVH-supported environments.
Artificial-intelligence within MetaTutorIVH, a technology designed for education-and-self-development, significantly affects students' performance by impacting their confidence levels, particularly during transitions from confusion to frustration. This technology, through its unique approach of tracking student emotions, aims to improve learning outcomes by adapting instructional strategies that mitigate negative emotions like frustration.
MetaTutorIVH's use of artificial-intelligence in studying emotional transitions can potentially enhance learning, as these transitions have a substantial impact on students' retrospective confidence judgments and learning outcomes. This suggests that the integration of artificial-intelligence in learning technologies like MetaTutorIVH could be a valuable strategy for promoting education-and-self-development.
