2 Citations (Scopus)

Abstract

The rapid development of collaborative activities (particularly beyond geographical boundaries) and the increasing demand for lifelong learning have opened immense opportunities for learners worldwide. Mass Collaborative Learning, as an emerging approach, shifts away from traditional teacher-centered milieu to self-driven learning practices where a large number of learners at various performance levels collectively work toward reaching a common goal. The implementation and development of mass collaborative learning communities however requires both further progress in understanding the involved processes and addressing the key affecting factors. Therefore, as a contribution in this context, a reference model for mass collaborative learning is pursued, aiming to facilitate the understanding of related concepts and highlighting the main internal and external components. Preliminary results of this research work are discussed.

Original languageEnglish
Title of host publicationTechnological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
EditorsLuis M. Camarinha-Matos, Nastaran Farhadi, Fábio Lopes, Helena Pereira
Place of PublicationCham
PublisherSpringer
Pages18-30
Number of pages13
ISBN (Electronic)978-3-030-45124-0
ISBN (Print)978-3-030-45123-3
DOIs
Publication statusPublished - 2020
Event11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020 - Costa de Caparica, Portugal
Duration: 1 Jul 20203 Jul 2020

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume577
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
Country/TerritoryPortugal
CityCosta de Caparica
Period1/07/203/07/20

Keywords

  • ARCON modeling framework
  • Collaborative Networks
  • Mass collaborative learning
  • Reference model

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