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Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

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Rebecca FergusonAuthors Info & Claims

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

Pages 221 - 224

https://doi.org/10.1145/2876034.2893419

Published: 25 April 2016 Publication History

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Abstract

The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge.

References

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Cited By

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Hoyos AVelasquez J(2020)Teaching Analytics: Current Challenges and Future DevelopmentIEEE Revista Iberoamericana de Tecnologias del Aprendizaje10.1109/RITA.2020.297924515:1(1-9)Online publication date: Feb-2020
https://doi.org/10.1109/RITA.2020.2979245
McAndrew PAnastopoulou SScanlon ELuckin RKlemmer SKoedinger K(2018)Transformative approaches in distance online educationProceedings of the Fifth Annual ACM Conference on Learning at Scale10.1145/3231644.3232261(1-4)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3231644.3232261
Longo AZappatore M(2017)A Microservice-Based MOOL in Acoustics Addressing the Learning-at-Scale Scenario2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2017.23(391-400)Online publication date: Jul-2017
https://doi.org/10.1109/COMPSAC.2017.23

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Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

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Published In

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

April 2016

446 pages

ISBN:9781450337267

DOI:10.1145/2876034

General Chair:
Jeff Haywood
The University of Edinburgh, UK
,
Program Chairs:
Vincent Aleven
Carnegie Mellon University, USA
,
Judy Kay
University of Sydney, Australia
,
Ido Roll
University of British Columbia, Canada

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 April 2016

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L@S 2016: Third (2016) ACM Conference on Learning @ Scale

April 25 - 26, 2016

Scotland, Edinburgh, UK

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L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Hoyos AVelasquez J(2020)Teaching Analytics: Current Challenges and Future DevelopmentIEEE Revista Iberoamericana de Tecnologias del Aprendizaje10.1109/RITA.2020.297924515:1(1-9)Online publication date: Feb-2020
https://doi.org/10.1109/RITA.2020.2979245
McAndrew PAnastopoulou SScanlon ELuckin RKlemmer SKoedinger K(2018)Transformative approaches in distance online educationProceedings of the Fifth Annual ACM Conference on Learning at Scale10.1145/3231644.3232261(1-4)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3231644.3232261
Longo AZappatore M(2017)A Microservice-Based MOOL in Acoustics Addressing the Learning-at-Scale Scenario2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2017.23(391-400)Online publication date: Jul-2017
https://doi.org/10.1109/COMPSAC.2017.23

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Recommendations

Learning analytics community exchange: evidence hub

Where is the evidence?: a call to action for learning analytics

Effects of Learning Analytics on Students' Self-Regulated Learning in Flipped Classroom

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