The Orchestration of computer-supported collaboration scripts with learning analytics

Computer-supported collaborative learning (CSCL) creates avenues for productive collaboration between students. In CSCL, collaborative learning flow patterns (CLFPs) provide pedagogical rationale and constraints for structuring the collaboration process. While structured collaboration facilitates th...

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Detalhes bibliográficos
Autor: Amarasinghe, Ishari
Formato: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/670420
Acesso em linha:http://hdl.handle.net/10803/670420
Access Level:acceso abierto
Palavra-chave:Learning technologies
Computer-Supported Collaborative Learning (CSCL)
Learning analytics
Orchestration
CSCL scripts
Collaborative Learning Flow Patterns (CLFPs)
Pyramid CLFP
Adaptive systems
Dashboards
Tecnología educativa
Aprendizaje Colaborativo Asistido por Ordenador (CSCL)
Analítica de aprendizaje
Orquestación
Patrones de Flujo de Aprendizaje Colaborativo (CLFP)
CLFP Pirámide
Sistemas adaptativos
Cuadros de mando
62
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network_name_str España
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dc.title.none.fl_str_mv The Orchestration of computer-supported collaboration scripts with learning analytics
title The Orchestration of computer-supported collaboration scripts with learning analytics
spellingShingle The Orchestration of computer-supported collaboration scripts with learning analytics
Amarasinghe, Ishari
Learning technologies
Computer-Supported Collaborative Learning (CSCL)
Learning analytics
Orchestration
CSCL scripts
Collaborative Learning Flow Patterns (CLFPs)
Pyramid CLFP
Adaptive systems
Dashboards
Tecnología educativa
Aprendizaje Colaborativo Asistido por Ordenador (CSCL)
Analítica de aprendizaje
Orquestación
Patrones de Flujo de Aprendizaje Colaborativo (CLFP)
CLFP Pirámide
Sistemas adaptativos
Cuadros de mando
62
title_short The Orchestration of computer-supported collaboration scripts with learning analytics
title_full The Orchestration of computer-supported collaboration scripts with learning analytics
title_fullStr The Orchestration of computer-supported collaboration scripts with learning analytics
title_full_unstemmed The Orchestration of computer-supported collaboration scripts with learning analytics
title_sort The Orchestration of computer-supported collaboration scripts with learning analytics
dc.creator.none.fl_str_mv Amarasinghe, Ishari
author Amarasinghe, Ishari
author_facet Amarasinghe, Ishari
author_role author
dc.contributor.none.fl_str_mv Hernández-Leo, Davinia
Jonsson, Anders
Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
dc.subject.none.fl_str_mv Learning technologies
Computer-Supported Collaborative Learning (CSCL)
Learning analytics
Orchestration
CSCL scripts
Collaborative Learning Flow Patterns (CLFPs)
Pyramid CLFP
Adaptive systems
Dashboards
Tecnología educativa
Aprendizaje Colaborativo Asistido por Ordenador (CSCL)
Analítica de aprendizaje
Orquestación
Patrones de Flujo de Aprendizaje Colaborativo (CLFP)
CLFP Pirámide
Sistemas adaptativos
Cuadros de mando
62
topic Learning technologies
Computer-Supported Collaborative Learning (CSCL)
Learning analytics
Orchestration
CSCL scripts
Collaborative Learning Flow Patterns (CLFPs)
Pyramid CLFP
Adaptive systems
Dashboards
Tecnología educativa
Aprendizaje Colaborativo Asistido por Ordenador (CSCL)
Analítica de aprendizaje
Orquestación
Patrones de Flujo de Aprendizaje Colaborativo (CLFP)
CLFP Pirámide
Sistemas adaptativos
Cuadros de mando
62
description Computer-supported collaborative learning (CSCL) creates avenues for productive collaboration between students. In CSCL, collaborative learning flow patterns (CLFPs) provide pedagogical rationale and constraints for structuring the collaboration process. While structured collaboration facilitates the design of favourable learning conditions, orchestration of collaboration becomes an important factor, as learner participation and real-world constraints can create deviations in real time. On the one hand, limited research has examined the orchestration challenges related to collaborative learning situations scripted according to CLFPs in authentic educational contexts to resolve collaboration at different scales. On the other hand, learning analytics (LA) can be used to provide proper technological tooling, infrastructure and support to orchestrate collaboration. To this end, this dissertation addresses the following research question: How can LA support orchestration mechanisms for scripted CSCL? To address this question, this dissertation first focuses on studying the orchestration challenges associated with scripted CSCL situations on small scales (in the classroom learning context) and large scales (in the distance learning context, specifically in massive open online courses [MOOCs]). In the classroom learning context, lack of teacher access to activity regulation mechanisms constituted a key challenge. In MOOCs, sustained student participation in multiple phases of the script was a primary challenge. The dissertation also focuses on studying the design of LA interventions that might address the orchestration challenges under examination. The proposed LA interventions range from human-in-control to machine-in-control in nature given the feasibility and regulation needs of the learning contexts under investigation. Following a design-based research (DBR) methodology, evaluation studies were conducted in naturalistic classrooms and in MOOCs to evaluate the effects of the proposed LA interventions and to understand the conditions for their successful implementation. The results of the evaluation studies conducted in the classroom context shed light on how teachers interpret LA data and how they action the resulting knowledge in authentic collaborative learning situations. In the distance learning context, the proposed interventions were critical in sustaining continuous flows of collaboration. The practical benefits and limitations of deploying LA solutions in real-world settings, as well as future research directions, are outlined.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10803/670420
url http://hdl.handle.net/10803/670420
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 226 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Pompeu Fabra
publisher.none.fl_str_mv Universitat Pompeu Fabra
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405279662112768
spelling The Orchestration of computer-supported collaboration scripts with learning analyticsAmarasinghe, IshariLearning technologiesComputer-Supported Collaborative Learning (CSCL)Learning analyticsOrchestrationCSCL scriptsCollaborative Learning Flow Patterns (CLFPs)Pyramid CLFPAdaptive systemsDashboardsTecnología educativaAprendizaje Colaborativo Asistido por Ordenador (CSCL)Analítica de aprendizajeOrquestaciónPatrones de Flujo de Aprendizaje Colaborativo (CLFP)CLFP PirámideSistemas adaptativosCuadros de mando62Computer-supported collaborative learning (CSCL) creates avenues for productive collaboration between students. In CSCL, collaborative learning flow patterns (CLFPs) provide pedagogical rationale and constraints for structuring the collaboration process. While structured collaboration facilitates the design of favourable learning conditions, orchestration of collaboration becomes an important factor, as learner participation and real-world constraints can create deviations in real time. On the one hand, limited research has examined the orchestration challenges related to collaborative learning situations scripted according to CLFPs in authentic educational contexts to resolve collaboration at different scales. On the other hand, learning analytics (LA) can be used to provide proper technological tooling, infrastructure and support to orchestrate collaboration. To this end, this dissertation addresses the following research question: How can LA support orchestration mechanisms for scripted CSCL? To address this question, this dissertation first focuses on studying the orchestration challenges associated with scripted CSCL situations on small scales (in the classroom learning context) and large scales (in the distance learning context, specifically in massive open online courses [MOOCs]). In the classroom learning context, lack of teacher access to activity regulation mechanisms constituted a key challenge. In MOOCs, sustained student participation in multiple phases of the script was a primary challenge. The dissertation also focuses on studying the design of LA interventions that might address the orchestration challenges under examination. The proposed LA interventions range from human-in-control to machine-in-control in nature given the feasibility and regulation needs of the learning contexts under investigation. Following a design-based research (DBR) methodology, evaluation studies were conducted in naturalistic classrooms and in MOOCs to evaluate the effects of the proposed LA interventions and to understand the conditions for their successful implementation. The results of the evaluation studies conducted in the classroom context shed light on how teachers interpret LA data and how they action the resulting knowledge in authentic collaborative learning situations. In the distance learning context, the proposed interventions were critical in sustaining continuous flows of collaboration. The practical benefits and limitations of deploying LA solutions in real-world settings, as well as future research directions, are outlined.El aprendizaje colaborativo asistido por ordenador (CSCL) ofrece oportunidades para la colaboración productiva entre estudiantes. En CSCL, los patrones de flujo de aprendizaje colaborativo (CLFP) proporcionan un fundamento pedagógico y restricciones para estructurar el proceso de colaboración. Si bien la colaboración estructurada facilita el diseño de condiciones de aprendizaje favorables, la orquestación de dicha colaboración estructurada se convierte en un factor importante, ya que la participación del alumno y los condicionantes del mundo real pueden crear desviaciones en el momento de su realización. Por un lado, existe una investigación limitada sobre los desafíos de la orquestación de aprendizaje colaborativo guiado según los CLFP en contextos educativos auténticos a diferentes escalas. Por otro lado, la analítica del aprendizaje (LA) se puede utilizar para proporcionar las herramientas tecnológicas, la infraestructura y el apoyo adecuados para orquestar la colaboración. Con este fin, esta tesis doctoral plantea la siguiente pregunta de investigación: ¿Cómo puede LA apoyar los mecanismos de orquestación de guiones de CSCL? Para abordar esta pregunta, la tesis doctoral se centra, primero, en estudiar los desafíos de la orquestación en situaciones CSCL guiadas a pequeña escala (en el contexto del aula) y a gran escala (en el contexto de aprendizaje a distancia, específicamente en cursos masivos abiertos en línea [MOOC]). En el contexto del aula, un reto imporante es la falta de acceso de los docentes a los mecanismos de regulación de la actividad. En los MOOC, el reto principal es sostener la participación de los estudiantes a lo largo de las diversas fases del guión. La tesis doctoral también se centra en estudiar el diseño de intervenciones de LA que podrían abordar los retos de orquestación detectados. Dadas las necesidades de viabilidad y regulación de los contextos de aprendizaje investigados, las intervenciones de LA propuestas van desde acciones automáticas donde la “máquina está en control” a intervenciones que implican “control por humanos”. Siguiendo una metodología de investigación basada en el diseño (DBR), se han realizado estudios en aulas y en MOOCs para evaluar los efectos de las intervenciones de LA propuestas y comprender las condiciones para su buena implementación. Los resultados de la evaluación realizada en el contexto del aula arrojan luz sobre cómo los profesores interpretan los datos de LA y cómo actúan en consecuencia en situaciones auténticas de aprendizaje colaborativo. En el contexto de la educación a distancia, las intervenciones propuestas fueron fundamentales para mantener flujos continuos de colaboración. La tesis docotral describe los beneficios prácticos y las limitaciones a la hora de implementar soluciones de LA en entornos reales, así como las direcciones de investigación futuras.Programa de doctorat en Tecnologies de la Informació i les ComunicacionsUniversitat Pompeu FabraHernández-Leo, DaviniaJonsson, AndersUniversitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions202120222020info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion226 p.application/pdfapplication/pdfhttp://hdl.handle.net/10803/670420TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésL'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-nd/4.0/http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/6704202026-06-14T12:46:07Z
score 15,300724

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