Examining the Predictive Relationship Between Personality and Emotion Traits and Learners’ Agent-Direct Emotions

Abstract

The current study examined the relationships between learners’ (N = 124) personality traits, the emotions they experience while typically studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with two Pedagogical Agents (PAs - agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for three agent-directed emotions (pride, boredom, and neutral) though the relationships differed between the two PAs. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions toward specific PAs (with different roles). Suggestions are provided for adapting PAs to support learners’ (with certain characteristics) experience of positive emotions (e.g., enjoyment) and minimize their experience of negative emotions (e.g., boredom). Such an approach presents a scalable and easily implemented method for creating emotionally-adaptive, agent-based learning environments, and improving learner-PA interactions to support learning.