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Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation

Despite the potential of learning analytics for personalized learning, it is seldom used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study uses learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argum...

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Autores principales: Han, Jeongyun, Kim, Kwan Hoon, Rhee, Wonjong, Cho, Young Hoan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539901/
https://www.ncbi.nlm.nih.gov/pubmed/33046948
http://dx.doi.org/10.1016/j.compedu.2020.104041
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author Han, Jeongyun
Kim, Kwan Hoon
Rhee, Wonjong
Cho, Young Hoan
author_facet Han, Jeongyun
Kim, Kwan Hoon
Rhee, Wonjong
Cho, Young Hoan
author_sort Han, Jeongyun
collection PubMed
description Despite the potential of learning analytics for personalized learning, it is seldom used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study uses learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argumentation (FCA). This study developed two dashboards for students and instructors, which enabled students to monitor their FCA process through adaptive feedback and helped the instructor provide adaptive support at the right time. The effectiveness of the dashboards was examined in a university class with 88 students (56 females, 32 males) for 4 weeks. The dashboards significantly improved the FCA process and outcomes, encouraging students to actively participate in FCA and create high-quality arguments. Students had a positive attitude toward the dashboard and perceived it as useful and easy to use. These findings indicate the usefulness of learning analytics dashboards in improving collaborative learning through adaptive feedback and support. Suggestions are provided on how to design dashboards for adaptive support in F2F learning contexts using learning analytics.
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spelling pubmed-75399012020-10-08 Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation Han, Jeongyun Kim, Kwan Hoon Rhee, Wonjong Cho, Young Hoan Comput Educ Article Despite the potential of learning analytics for personalized learning, it is seldom used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study uses learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argumentation (FCA). This study developed two dashboards for students and instructors, which enabled students to monitor their FCA process through adaptive feedback and helped the instructor provide adaptive support at the right time. The effectiveness of the dashboards was examined in a university class with 88 students (56 females, 32 males) for 4 weeks. The dashboards significantly improved the FCA process and outcomes, encouraging students to actively participate in FCA and create high-quality arguments. Students had a positive attitude toward the dashboard and perceived it as useful and easy to use. These findings indicate the usefulness of learning analytics dashboards in improving collaborative learning through adaptive feedback and support. Suggestions are provided on how to design dashboards for adaptive support in F2F learning contexts using learning analytics. The Authors. Published by Elsevier Ltd. 2021-04 2020-10-07 /pmc/articles/PMC7539901/ /pubmed/33046948 http://dx.doi.org/10.1016/j.compedu.2020.104041 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Han, Jeongyun
Kim, Kwan Hoon
Rhee, Wonjong
Cho, Young Hoan
Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title_full Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title_fullStr Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title_full_unstemmed Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title_short Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
title_sort learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539901/
https://www.ncbi.nlm.nih.gov/pubmed/33046948
http://dx.doi.org/10.1016/j.compedu.2020.104041
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