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Questions as data: illuminating the potential of learning analytics through questioning an emergent field

In providing a meta-analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused q...

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Autores principales: Mason, Jon, Chen, Weiqin, Hoel, Tore
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302922/
https://www.ncbi.nlm.nih.gov/pubmed/30613245
http://dx.doi.org/10.1186/s41039-016-0037-1
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author Mason, Jon
Chen, Weiqin
Hoel, Tore
author_facet Mason, Jon
Chen, Weiqin
Hoel, Tore
author_sort Mason, Jon
collection PubMed
description In providing a meta-analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused questioning session for collecting relevant data beyond the content of the papers themselves. In December 2014, approximately 40 participants attended the workshop held in Nara, Japan, and contributed to the collection of open research questions. Six papers were presented covering topics including scope; interoperability standards; privacy and control of individual data, extracting data from learning content and processes; and the development of conceptual frameworks. These papers established a base from which the group generated a set of questions that invite further investigation. Utilising the first stage of the Question Formulation Technique, a pedagogical approach designed to stimulate student inquiry, a prominent finding from the workshop that questions emerging from focused inquiry provide a useful set of data in their own right. With an explicit workshop focus on learning analytics interoperability, this paper reports on the emergent issues identified in the workshop and the kinds of questions associated with each issue in the context of current research in the field of learning analytics. The study considers the complexity arising from the fact that data associated with learning is itself becoming a digital learning resource while also enabling analysis of learner behaviours and systems usage.
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spelling pubmed-63029222019-01-04 Questions as data: illuminating the potential of learning analytics through questioning an emergent field Mason, Jon Chen, Weiqin Hoel, Tore Res Pract Technol Enhanc Learn Research In providing a meta-analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused questioning session for collecting relevant data beyond the content of the papers themselves. In December 2014, approximately 40 participants attended the workshop held in Nara, Japan, and contributed to the collection of open research questions. Six papers were presented covering topics including scope; interoperability standards; privacy and control of individual data, extracting data from learning content and processes; and the development of conceptual frameworks. These papers established a base from which the group generated a set of questions that invite further investigation. Utilising the first stage of the Question Formulation Technique, a pedagogical approach designed to stimulate student inquiry, a prominent finding from the workshop that questions emerging from focused inquiry provide a useful set of data in their own right. With an explicit workshop focus on learning analytics interoperability, this paper reports on the emergent issues identified in the workshop and the kinds of questions associated with each issue in the context of current research in the field of learning analytics. The study considers the complexity arising from the fact that data associated with learning is itself becoming a digital learning resource while also enabling analysis of learner behaviours and systems usage. Springer Singapore 2016-05-21 2016 /pmc/articles/PMC6302922/ /pubmed/30613245 http://dx.doi.org/10.1186/s41039-016-0037-1 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Mason, Jon
Chen, Weiqin
Hoel, Tore
Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title_full Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title_fullStr Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title_full_unstemmed Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title_short Questions as data: illuminating the potential of learning analytics through questioning an emergent field
title_sort questions as data: illuminating the potential of learning analytics through questioning an emergent field
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302922/
https://www.ncbi.nlm.nih.gov/pubmed/30613245
http://dx.doi.org/10.1186/s41039-016-0037-1
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