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Learning analytics support to teachers' design and orchestrating tasks


Amarasinghe, Ishari; Michos, Konstantinos; Crespi, Francisco; Hernández‐Leo, Davinia (2022). Learning analytics support to teachers' design and orchestrating tasks. Journal of Computer Assisted Learning:Epub ahead of print.

Abstract

Background:
Data-driven educational technology solutions have the potential to support teachers in different tasks, such as the designing and orchestration of collaborative learning activities. When designing, such solutions can improve teacher understanding of how learning designs impact student learning and behaviour; and guide them to refine and redesign future learning designs. When orchestrating educational scenarios, data-driven solutions can support teacher awareness of learner participation and progress and enhance real time classroom management.

Objectives:
The use of learning analytics (LA) can be considered a suitable approach to tackle both problems. However, it is unclear if the same LA indicators are able to satisfactorily support both the designing and orchestration of activities. This study aims to investigate the use of the same LA indicators for supporting multiple teacher tasks, that is, design, redesign and orchestration, as a gap in the existing literature that requires further exploration.

Methods:
In this study, first we refer to the previous work to study the use of different LA to support both tasks. Then we analyse the nature of the two tasks focusing on a case study that uses the same collaborative learning tool with LA to support both tasks.

Implications:
The study findings led to derive design considerations on LA support for teachers’ design and orchestrating tasks.

Abstract

Background:
Data-driven educational technology solutions have the potential to support teachers in different tasks, such as the designing and orchestration of collaborative learning activities. When designing, such solutions can improve teacher understanding of how learning designs impact student learning and behaviour; and guide them to refine and redesign future learning designs. When orchestrating educational scenarios, data-driven solutions can support teacher awareness of learner participation and progress and enhance real time classroom management.

Objectives:
The use of learning analytics (LA) can be considered a suitable approach to tackle both problems. However, it is unclear if the same LA indicators are able to satisfactorily support both the designing and orchestration of activities. This study aims to investigate the use of the same LA indicators for supporting multiple teacher tasks, that is, design, redesign and orchestration, as a gap in the existing literature that requires further exploration.

Methods:
In this study, first we refer to the previous work to study the use of different LA to support both tasks. Then we analyse the nature of the two tasks focusing on a case study that uses the same collaborative learning tool with LA to support both tasks.

Implications:
The study findings led to derive design considerations on LA support for teachers’ design and orchestrating tasks.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Education
Dewey Decimal Classification:370 Education
Scopus Subject Areas:Social Sciences & Humanities > Education
Physical Sciences > Computer Science Applications
Uncontrolled Keywords:Computer-supported collaborative learning, learning analytics, learning design, orchestration, Pyramid collaborative learning flow pattern, scripts
Language:English
Date:20 July 2022
Deposited On:28 Oct 2022 10:40
Last Modified:26 Dec 2023 08:00
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0266-4909
OA Status:Green
Publisher DOI:https://doi.org/10.1111/jcal.12711
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)