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Título
Quantifying Collaboration Quality in Face-to-Face Classroom Settings Using MMLA
Autor
Año del Documento
2020
Editorial
Springer
Descripción
Producción Científica
Documento Fuente
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
Zusammenfassung
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.
Materias Unesco
58 Pedagogía
Palabras Clave
Análisis de aprendizaje
Learning Analytics
Patrocinador
European Union via the European Regional Development Fund and in the context of CEITER and Next-Lab (Horizon 2020 Research and Innovation Programme, grant agreements no. 669074 and 731685)
Junta de Castilla y León (Project VA257P18)
Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)
Junta de Castilla y León (Project VA257P18)
Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)
Patrocinador
info:eu-repo/grantAgreement/EC/H2020/669074
info:eu-repo/grantAgreement/EC/H2020/731685
info:eu-repo/grantAgreement/EC/H2020/731685
Version del Editor
Propietario de los Derechos
© 2020 Springer
Idioma
eng
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
openAccess
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