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Learning analytics support to teachers' design and orchestrating tasks

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dc.contributor.author Amarasinghe, Ishari
dc.contributor.author Michos, Konstantinos
dc.contributor.author Crespi, Francsico
dc.contributor.author Hernández Leo, Davinia
dc.date.accessioned 2023-03-01T13:51:18Z
dc.date.available 2023-03-01T13:51:18Z
dc.date.issued 2022
dc.identifier.citation Amarasinghe I, Michos K, Crespi F, Hernández-Leo D. Learning analytics support to teachers' design and orchestrating tasks. J Comput Assist Learn. 2022. 16 p. DOI: 10.1111/jcal.12711
dc.identifier.issn 0266-4909
dc.identifier.uri http://hdl.handle.net/10230/55996
dc.description Data de publicació electrònica: 20 de juliol de 2022
dc.description.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.
dc.description.sponsorship This work has been partially funded by the Spanish Ministry of Science and Innovation and the National Research Agency (PID2020-112584RBC33/MICIN/AEI/10.13039/501100011033), and the Volkswagen Stiftung (Courage, ref. 95 566). D. Hernández-Leo (Serra Húnter) acknowledges the support by ICREA under the ICREA Academia program.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley
dc.relation.ispartof Journal of Computer Assisted Learning. 2022. 16 p.
dc.rights This is the peer reviewed version of the following article: Amarasinghe I, Michos K, Crespi F, Hernández-Leo D. Learning analytics support to teachers' design and orchestrating tasks. J Comput Assist Learn. 2022. 16 p, which has been published in final form at http://dx.doi.org/10.1111/jcal.12711. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title Learning analytics support to teachers' design and orchestrating tasks
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1111/jcal.12711
dc.subject.keyword computer-supported collaborative learning
dc.subject.keyword learning analytics
dc.subject.keyword learning design
dc.subject.keyword orchestration
dc.subject.keyword Pyramid collaborative learning flow pattern
dc.subject.keyword scripts
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112584RBC33
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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