Mining for Evidence of Collaborative Learning in Question & Answering Systems

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

Question and Answering systems and crowd learning are becoming an increasingly popular way of organising and exchanging expert knowledge in specific domains. Since they are expected to have a significant impact on online education , we will investigate to which degree the necessary conditions for collaborative learning emerge in open Q&A platforms like Stack Exchange, in which communities grow organically and learning is not guided by a central authority or curriculum, unlike MOOCs. Starting from a pedagogical perspective, this paper mines for circumstantial evidence to support or contradict the pedagogical criteria for collaborative learning. It is observed that although there are technically no hindrances towards true collaborative learning, the nature and dynamics of the communities are not favourable for collaborative learning.

The findings in this paper illustrate how the collaborative nature of feedback can be measured in online platforms, and how users can be identified that need to be encouraged to participate in collaborative activities. In this context, remarks and suggestions are formulated to pave the way for a more collaborative and pedagogically sound platform of knowledge sharing.
Original languageEnglish
Title of host publicationFFMI Workshop at Educational Data Mining Conference
Number of pages5
Publication statusPublished - 2014
EventThe 7th International Conference on Educational Data Mining (EDM 2014) - London, United Kingdom
Duration: 4 Jul 20147 Jul 2014

Conference

ConferenceThe 7th International Conference on Educational Data Mining (EDM 2014)
Country/TerritoryUnited Kingdom
CityLondon
Period4/07/147/07/14

Keywords

  • collaborative learning
  • artificial intelligence
  • question & answering systems
  • computer assisted instruction

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