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|Title:||Towards Automated Tracking of Affect: Testing the Use of Continuous Self-Reports and Multimodal Metrics|
|Publisher:||International Society of the Learning Sciences|
|Citation:||Sung, G., Hassan, J., & Schneider, B. (2022). Towards automated tracking of affect: Testing the use of continuous self-reports and multimodal metrics. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 2080-2081). International Society of the Learning Sciences.|
|Abstract:||The affective states of students while studying outside of class hold rich information about how students are doing in a course and how instruction could be improved. In this paper, we test the co-occurrence of webcam-based nonverbal metrics with self-reported student affective states during an instructional video. Preliminary results suggest that low-level, selfreported affect can be a promising ground truth data for research, and that nonverbal metrics created from webcam streams can be associated with this self-reported affect.|
|Appears in Collections:||ISLS Annual Meeting 2022|
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