Social learning networks: Efficiency optimization for MOOC forums

Christopher Greg Brinton, Swapna Buccapatnam, Felix Ming Fai Wong, Mung Chiang, H. Vincent Poor

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

17 Scopus citations

Abstract

A Social Learning Network (SLN) emerges when users exchange information on educational topics with structured interactions. The recent proliferation of massively scaled online (human) learning, such as Massive Open Online Courses (MOOCs), has presented a plethora of research challenges surrounding SLN. In this paper, we ask: How efficient are these networks? We propose a framework in which SLN efficiency is determined by comparing user benefit in the observed network to a benchmark of maximum utility achievable through optimization. Our framework defines the optimal SLN through utility maximization subject to a set of constraints that can be inferred from the network. Through evaluation on four MOOC discussion forum datasets and optimizing over millions of variables, we find that SLN efficiency can be rather low (from 68% to 82% depending on the specific parameters and dataset), which indicates that much can be gained through optimization. We find that the gains in global utility (i.e., average across users) can be obtained without making the distribution of local utilities (i.e., utility of individual users) less fair. We also discuss ways of realizing the optimal network in practice, through curated news feeds in online SLN.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467399531
DOIs
StatePublished - Jul 27 2016
Event35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States
Duration: Apr 10 2016Apr 14 2016

Publication series

NameProceedings - IEEE INFOCOM
Volume2016-July
ISSN (Print)0743-166X

Other

Other35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
CountryUnited States
CitySan Francisco
Period4/10/164/14/16

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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