TY - GEN
T1 - "Mirror, Mirror, on the Wall" - Promoting Self-Regulated Learning using Affective States Recognition via Facial Movements
AU - Chen, Si
AU - Liu, Yixin
AU - Lu, Risheng
AU - Zhou, Yuqian
AU - Lee, Yi Chieh
AU - Huang, Yun
N1 - This material is based upon work supported by the National Science Foundation under Grant No. 2119589. Any opinions, fndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily refect the views of the National Science Foundation. This project was also made possible in part by the Institute of Museum and Library Services LG-70-18-0088-18. The views, fndings, conclusions or recommendations expressed in this article do not necessarily represent those of the Institute of Museum and Library Services.
PY - 2022/6/13
Y1 - 2022/6/13
N2 - Prior research suggests that affective states of self-regulated learning can be used to improve learners' cognitive processes and their learning outcomes. However, little research explored the effect of using facial movements to detect learners' affective states on self-regulated learning. In this work, we designed, implemented, and evaluated Mirror: a self-regulated learning tool that applies facial expression recognition to support learners' reflections in video-based learning. We conducted two studies to identify user needs (with 12 participants) and to evaluate the tool (with 16 participants). The results show that, after watching a video, participants benefited from using Mirror through different reflection processes, e.g., gaining a deeper understanding of their learning experiences through self-observation and attributing causes for their learning affects through self-judgment. Meanwhile, we also identified several ethical concerns, e.g., users' agency of handling the uncertainty of AI, reactivity towards outcome-based AI, over-reliance on "positive"AI results, and fairness of AI informed decision-making.
AB - Prior research suggests that affective states of self-regulated learning can be used to improve learners' cognitive processes and their learning outcomes. However, little research explored the effect of using facial movements to detect learners' affective states on self-regulated learning. In this work, we designed, implemented, and evaluated Mirror: a self-regulated learning tool that applies facial expression recognition to support learners' reflections in video-based learning. We conducted two studies to identify user needs (with 12 participants) and to evaluate the tool (with 16 participants). The results show that, after watching a video, participants benefited from using Mirror through different reflection processes, e.g., gaining a deeper understanding of their learning experiences through self-observation and attributing causes for their learning affects through self-judgment. Meanwhile, we also identified several ethical concerns, e.g., users' agency of handling the uncertainty of AI, reactivity towards outcome-based AI, over-reliance on "positive"AI results, and fairness of AI informed decision-making.
KW - Affective Computing
KW - Emotion
KW - Mixed Methods
KW - Video-based Learning
UR - http://www.scopus.com/inward/record.url?scp=85133647567&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133647567&partnerID=8YFLogxK
U2 - 10.1145/3532106.3533500
DO - 10.1145/3532106.3533500
M3 - Conference contribution
AN - SCOPUS:85133647567
T3 - DIS 2022 - Proceedings of the 2022 ACM Designing Interactive Systems Conference: Digital Wellbeing
SP - 1300
EP - 1314
BT - DIS 2022 - Proceedings of the 2022 ACM Designing Interactive Systems Conference
PB - Association for Computing Machinery
T2 - 2022 ACM Designing Interactive Systems Conference: Digital Wellbeing, DIS 2022
Y2 - 13 June 2022 through 17 June 2022
ER -