Learning at Scale

Ido Roll, Daniel M. Russell, Dragan Gašević

Research output: Contribution to journalArticleOtherpeer-review

13 Citations (Scopus)

Abstract

Learning at Scale is a fast growing field that affects formal, informal, and workplace education. Highly interdisciplinary, it builds on solid foundations in the learning sciences, computer science, education, and the social sciences. We define learning at scale as the study of the technologies, pedagogies, analyses, and theories of learning and teaching that take place with a large number of learners and a high ratio of learners to facilitators. The scale of these environments often changes the very nature of the interaction and learning experiences. We identify three types of technologies that support scale in education: dedicated content-agnostic platforms, such as MOOCs; dedicated tools, such as Intelligent Tutoring Systems; and repurposed platforms, such as social networks. We further identify five areas that scale affects: learners, research and data, adaptation, space and time, and pedagogy. Introducing the papers in this special issue on the topic, we discuss the characteristics, affordances, and promise of learning at scale.

Original languageEnglish
Pages (from-to)471-477
Number of pages7
JournalInternational Journal of Artificial Intelligence in Education
Volume28
Issue number4
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Intelligent tutoring systems
  • Learning analytics
  • Learning at scale
  • MOOCs

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