Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions

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Date

2019-01-01

Author

Er, Erkan
Gomez-Sanchez, Eduardo
Bote-Lorenzo, Miguel L.
Asensio-Perez, Juan I.
Dimitriadis, Yannis

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With the aim of supporting instructional designers in setting up collaborative learning activities in MOOCs, this paper derives prediction models for student participation in group discussions. The salient feature of these models is that they are built using only data prior to the learning activity, and can thus provide actionable predictions, as opposed to post-hoc approaches common in the MOOC literature. Some learning design scenarios that make use of this actionable information are illustrated.

URI

https://hdl.handle.net/11511/90411

DOI

https://doi.org/10.1145/3330430.3333640

Conference Name

6th ACM Conference on Learning @ Scale (L@S)

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Department of Computer Education and Instructional Technology, Conference / Seminar

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Citation Formats

E. Er, E. Gomez-Sanchez, M. L. Bote-Lorenzo, J. I. Asensio-Perez, and Y. Dimitriadis, “Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions,” presented at the 6th ACM Conference on Learning @ Scale (L@S), Illinois, Amerika Birleşik Devletleri, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/90411.