Learning with human and virtual instructors who display happy or bored emotions in video lectures

In this study, we investigate whether the affective state (happy or bored) of a human or virtual instructor in an instructional video on statistics yields different learning processes and outcomes. The positivity principle states that the emotional state of the instructor is recognized by students (hypothesis 1) and affects their emotional state (hypothesis 2), motivational state (hypothesis 3), and learning outcomes (hypothesis 4). The equivalence principle states that people respond to the emotional tone of computerized onscreen agents in the same way as for human instructors (hypothesis 5). In a 2 × 2 between subjects design, participants were assigned to watch a statistics video in one of four groups: Happy Human (HH), Happy Agent (HA), Bored Human (BH), and Bored Agent (BA). Then, they rated the emotional state of the instructor, rated their own emotional state, rated their motivational state, and took a posttest. The findings support predictions 1, 2, and 3 of the positivity principle but not prediction 4, perhaps because an immediate test was not sensitive enough. The equivalence principle (hypothesis 5) was partially supported as the human and agent groups showed similar rating patterns although the effects were stronger for human instructors for recognizing the instructor's emotion. As education transitions online, it is important to note that students are happier and more motivated when they learn from happy instructors than from bored instructors.