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Abstract BackgroundReal‐world engineering problems are ill‐defined and complex, and solving them may arouse negative epistemic affect (feelings experienced within problem‐solving). These feelings fall into sequenced patterns (affective pathways). Over time, these patterns can alter students' attitudes toward engineering. Meta‐affect (affect or cognition about affect) can shape or reframe affective pathways, changing a student's problem‐solving experience. Purpose/Hypothesis(es)This paper examines epistemic affect and meta‐affect in undergraduate students solving ill‐defined problems called open‐ended modeling problems (OEMPs), addressing two research questions: What epistemic affect and transitions between different affective states do students report? And, how does meta‐affect shape students' affective experiences? Design/MethodWe examined 11 retrospective interviews with nine students performed across two semesters in which students completed OEMPs. Using inductive and deductive coding with discourse analysis, we systematically searched for expressions conveying epistemic affect and for transitions in affect; we performed additional deductive coding of the transcripts for meta‐affect and synthesized these results to formulate narratives related to affect and meta‐affect. ResultsTogether, the expressions, transitions, and meta‐affect suggest different types of student experiences. Depending on their meta‐affect, students either recounted experiences dominated by positive or negative affect, or else they experienced negative emotions as productive. ConclusionsIll‐defined complex problems elicit a wide range of positive and negative emotions and provide opportunities to practice affective regulation and productive meta‐affect. Viewing the OEMPs as authentic disciplinary experiences and/or the ability to view negative emotions as productive can enable overall positive experiences. Our results provide insight into how instructors can foster positive affective pathways through problem‐scaffolding or their interactions with students.
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MirrorUs: Mirroring Peers' Affective Cues to Promote Learner's Meta-Cognition in Video-based Learning
Learners' awareness of their own affective states (emotions) 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 peers' affects on learners' meta-cognition, we proposed two types of cues that aggregated peers' affects that were recognized via facial expression recognition:Locative cues (displaying the spikes of peers' emotions along a video timeline) andTemporal 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 theTemporal 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).
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- PAR ID:
- 10540767
- Publisher / Repository:
- ACM Digital Library
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 7
- Issue:
- CSCW2
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3610079
