Learning at Scale
Open accessAbstract
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.
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Scalable, adaptive online courses could give hundreds of thousands of learners access to learning resources that they would not usually have in their local educational facilities.
“For instance, scalable, adaptive online courses could give hundreds of thousands of learners access to learning resources that they would not usually have in their local educational facilities [694].”
From Tackling climate change with machine learning (2023) · cites this paper as [694]
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“The large diversity of learners means that we know more about how learners with different attributes engage and learn, and thus are able to offer diversified educational experiences that match learners’ goals and background (Kizilcec and Cohen 2017; Roll and Winne 2015).”
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- Authors
- Roll, I., Russell, D. M., & Gašević, D.
- Affiliations
- University of British Columbia (CA)
- Google (United States) (US)
- Monash University (AU)
- Corresponding author
- Roll, Ido
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 28
- Issue
- 4
- Pages
- 471-477
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W2809185978
- Cited by
- 49
- References
- 30
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 10 · 2021: 6 · 2020: 5 · 2019: 4
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- learning at scale, MOOCs, intelligent tutoring systems, social networks in education, learning analytics, adaptive learning, pedagogy
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