Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions


Creative Commons License

Er E., Gomez-Sanchez E., Bote-Lorenzo M. L. , Asensio-Perez J. I. , Dimitriadis Y.

6th ACM Conference on Learning @ Scale (L@S), Illinois, United States Of America, 24 - 25 June 2019 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1145/3330430.3333640
  • City: Illinois
  • Country: United States Of America

Abstract

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.