RT info:eu-repo/semantics/bookPart
T1 Quantifying Collaboration Quality in Face-to-Face Classroom Settings Using MMLA
A1 Chejara, Pankaj
A1 Prieto, Luis P.
A1 Ruiz Calleja, Adolfo
A1 Rodríguez Triana, María Jesús
A1 Kant Shankar, Shashi
A1 Kasepalu, Reet
K1 Análisis de aprendizaje
K1 Learning Analytics
K1 58 Pedagogía
AB The estimation of collaboration quality using manual observation and coding is a tedious and difficult task. Researchers have proposed the automation of this process by estimation into few categories (e.g., high vs. low collaboration). However, such categorical estimation lacks in depth and actionability, which can be critical for practitioners. We present a case study that evaluates the feasibility of quantifying collaboration quality and its multiple sub-dimensions (e.g., collaboration flow) in an authentic classroom setting. We collected multimodal data (audio and logs) from two groups collaborating face-to-face and in a collaborative writing task. The paper describes our exploration of different machine learning models and compares their performance with that of human coders, in the task of estimating collaboration quality along a continuum. Our results show that it is feasible to quantitatively estimate collaboration quality and its sub-dimensions, even from simple features of audio and log data, using machine learning. These findings open possibilities for in-depth automated quantification of collaboration quality, and the use of more advanced features and algorithms to get their performance closer to that of human coders.
PB Springer
YR 2020
FD 2020
LK http://uvadoc.uva.es/handle/10324/43239
UL http://uvadoc.uva.es/handle/10324/43239
LA eng
NO Chejara P., Prieto L.P., Ruiz-Calleja A., Rodríguez-Triana M.J., Shankar S.K., Kasepalu R. Quantifying Collaboration Quality in Face-to-Face Classroom Settings Using MMLA. In: Nolte A., Alvarez C., Hishiyama R., Chounta IA., Rodríguez-Triana M., Inoue T. (eds) Collaboration Technologies and Social Computing. CollabTech 2020. Lecture Notes in Computer Science, vol 12324. Springer, 2020. https://doi.org/10.1007/978-3-030-58157-2_11
NO Producción Científica
DS UVaDOC
RD 24-nov-2024