MirrorUs: Mirroring Peers' Affective Cues to Promote Learner's Meta-Cognition in Video-based Learning

Learners' awareness of their own affective states (affects) can improve their meta-cognition, which is a critical skill of being aware of and controlling one's cognitive, motivational, and affect, and adjusting their learning strategies and behaviors accordingly. To investigate the effect of AI-generated peers' affects on learners' meta-cognition, we proposed two types of cues that aggregated peers' affects that were recognized via facial expression recognition: \textit{Locative} cues (displaying the spikes of peers' emotions along a video timeline) and \textit{Temporal} cues (showing the positivities of peers' emotions at different segments of a video). We conducted a between-subject experiment with 42 college students through the use of think-aloud protocols, interviews, and surveys. Our results showed that the two types of cues improved participants' meta-cognition differently. For example, interacting with the \textit{Temporal} cues triggered the participants to compare their own affective responses with their peers and reflect more on why and how they had different emotions with the same video content. While the participants perceived the benefits of using AI-generated peers’ cues to improve their awareness of their own learning affects, they also sought more explanations from their peers to understand the AI-generated results. Our findings not only provide novel design implications for promoting learners' meta-cognition with privacy-preserved social cues of peers' learning affects, but also suggest an expanded design framework for Explainable AI (XAI).