Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations

This study presents how predictive analytics can be used to inform the formulation of adaptive collaborative learning groups in the context of Computer Supported Collaborative Learning considering across-spaces learning situations. During the study we have collected data from different learning spac...

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Detalles Bibliográficos
Autores: Amarasinghe, Ishari, Hernández Leo, Davinia, Jonsson, Anders, 1973-
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/37277
Acceso en línea:http://hdl.handle.net/10230/37277
Access Level:acceso abierto
Palabra clave:Computer supported collaborative learning (CSCL)
Adaptive collaborative scripting
Collaborative learning flow patterns (CLFP)
Supervised machine learning
Prediction algorithms
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spelling Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situationsAmarasinghe, IshariHernández Leo, DaviniaJonsson, Anders, 1973-Computer supported collaborative learning (CSCL)Adaptive collaborative scriptingCollaborative learning flow patterns (CLFP)Supervised machine learningPrediction algorithmsThis study presents how predictive analytics can be used to inform the formulation of adaptive collaborative learning groups in the context of Computer Supported Collaborative Learning considering across-spaces learning situations. During the study we have collected data from different learning spaces which depicted both individual and collaborative learning activity engagement of students in two different learning contexts (namely the classroom learning and distance learning context) and attempted to predict individual student’s future collaborative learning activity participation in a pyramid-based collaborative learning activity using supervised machine learning techniques. We conducted experimental case studies in the classroom and in distance learning settings, in which real-time predictions of student’s future collaborative learning activity participation were used to formulate adaptive collaborative learner groups. Findings of the case studies showed that the data collected from across-spaces learning scenarios is informative when predicting future collaborative learning activity participation of students hence facilitating the formulation of adaptive collaborative group configurations that adapt to the activity participation differences of students in real-time. Limitations of the proposed approach and future research direction are illustrated.This work has been partially funded by FEDER, the National Research Agency of the Spanish Ministry of Science, Innovations and Universities MDM-2015-0502, TIN2014-53199-C3-3-R, TIN2017-85179-C3-3-R and “la Caixa Foundation” (CoT project, 100010434). DHL is a Serra Húnter Fellow.Springer20192019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/37277reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésUser Modeling and User-Adapted Interaction; 2019 Apr 23;29:869-92info:eu-repo/grantAgreement/ES/1PE/TIN2014-53199-C3-3-Rinfo:eu-repo/grantAgreement/ES/2PE/TIN2017-85179-C3-3-R© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-019-09233-8info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/372772026-06-12T07:21:37Z
dc.title.none.fl_str_mv Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
title Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
spellingShingle Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
Amarasinghe, Ishari
Computer supported collaborative learning (CSCL)
Adaptive collaborative scripting
Collaborative learning flow patterns (CLFP)
Supervised machine learning
Prediction algorithms
title_short Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
title_full Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
title_fullStr Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
title_full_unstemmed Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
title_sort Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations
dc.creator.none.fl_str_mv Amarasinghe, Ishari
Hernández Leo, Davinia
Jonsson, Anders, 1973-
author Amarasinghe, Ishari
author_facet Amarasinghe, Ishari
Hernández Leo, Davinia
Jonsson, Anders, 1973-
author_role author
author2 Hernández Leo, Davinia
Jonsson, Anders, 1973-
author2_role author
author
dc.subject.none.fl_str_mv Computer supported collaborative learning (CSCL)
Adaptive collaborative scripting
Collaborative learning flow patterns (CLFP)
Supervised machine learning
Prediction algorithms
topic Computer supported collaborative learning (CSCL)
Adaptive collaborative scripting
Collaborative learning flow patterns (CLFP)
Supervised machine learning
Prediction algorithms
description This study presents how predictive analytics can be used to inform the formulation of adaptive collaborative learning groups in the context of Computer Supported Collaborative Learning considering across-spaces learning situations. During the study we have collected data from different learning spaces which depicted both individual and collaborative learning activity engagement of students in two different learning contexts (namely the classroom learning and distance learning context) and attempted to predict individual student’s future collaborative learning activity participation in a pyramid-based collaborative learning activity using supervised machine learning techniques. We conducted experimental case studies in the classroom and in distance learning settings, in which real-time predictions of student’s future collaborative learning activity participation were used to formulate adaptive collaborative learner groups. Findings of the case studies showed that the data collected from across-spaces learning scenarios is informative when predicting future collaborative learning activity participation of students hence facilitating the formulation of adaptive collaborative group configurations that adapt to the activity participation differences of students in real-time. Limitations of the proposed approach and future research direction are illustrated.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/37277
url http://hdl.handle.net/10230/37277
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv User Modeling and User-Adapted Interaction; 2019 Apr 23;29:869-92
info:eu-repo/grantAgreement/ES/1PE/TIN2014-53199-C3-3-R
info:eu-repo/grantAgreement/ES/2PE/TIN2017-85179-C3-3-R
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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