Literaturnachweis - Detailanzeige
| Autor/inn/en | Huang, Karina; Bryant, Tonya; Schneider, Bertrand |
|---|---|
| Titel | Identifying Collaborative Learning States Using Unsupervised Machine Learning on Eye-Tracking, Physiological and Motion Sensor Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019). |
| Quelle | (2019), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Cooperative Learning; Artificial Intelligence; Eye Movements; Learning Analytics; Motion; Physiology; Attention; Human Posture |
| Abstract | With the advent of new data collection techniques, there has been a growing interest in studying co-located groups of students using Multimodal Learning Analytics to automatically identify collaborative learning states. In this paper, we analyze a multimodal dataset (N=84) made of eye-tracking, physiological and motion sensing data. We leverage unsupervised machine learning algorithms to find (un)productive collaborative states. We found a three-states solution where different states (and transitions between them) were significantly correlated with task performance, collaboration quality and learning gains. We interpret these findings in light of collaborative learning theories and discuss their implications for studying groups of students using MMLA. [For the full proceedings, see ED599096.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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