RT info:eu-repo/semantics/article
T1 Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data
A1 Prieto, Luis P.
A1 Sharma, Kshitij
A1 Kidzinski, Łukasz
A1 Rodríguez Triana, María Jesús
A1 Dillenbourg, Pierre
K1 activity detection
K1 eye-tracking
K1 multimodal learning analytics
K1 sensors
K1 teaching analytics
AB The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time) on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7–0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.
PB Wiley
YR 2018
FD 2018-01-24
LK https://uvadoc.uva.es/handle/10324/74412
UL https://uvadoc.uva.es/handle/10324/74412
LA eng
NO Journal of Computer Assisted Learning, April 2018, vol. 34, n. 2, p.193-203
NO Producción Científica
DS UVaDOC
RD 23-feb-2025