Dissertation/ Thesis
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The Orchestration of computer-supported collaboration scripts with learning analytics

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: The Orchestration of computer-supported collaboration scripts with learning analytics
Συγγραφείς: Amarasinghe, Ishari
Συνεισφορές: Hernández-Leo, Davinia, Jonsson, Anders, Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
Πηγή: TDX (Tesis Doctorals en Xarxa)
Στοιχεία εκδότη: Universitat Pompeu Fabra
Έτος έκδοσης: 2021
Συλλογή: UPF Digital Repository (Universitat Pompeu Fabra, Barcelona)
Θεματικοί όροι: 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
Time: 62
Περιγραφή: 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 ...
Τύπος εγγράφου: doctoral or postdoctoral thesis
Περιγραφή αρχείου: 226 p.; application/pdf
Γλώσσα: English
Relation: http://hdl.handle.net/10803/670420; http://hdl.handle.net/10230/46224
Διαθεσιμότητα: http://hdl.handle.net/10803/670420
http://hdl.handle.net/10230/46224
Rights: L'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/openAccess
Αριθμός Καταχώρησης: edsbas.570B9440
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  Data: The Orchestration of computer-supported collaboration scripts with learning analytics
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  Data: Hernández-Leo, Davinia<br />Jonsson, Anders<br />Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
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  Data: TDX (Tesis Doctorals en Xarxa)
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  Data: 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 ...
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RecordInfo BibRecord:
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      – Text: English
    Subjects:
      – SubjectFull: Learning technologies
        Type: general
      – SubjectFull: Computer-Supported Collaborative Learning (CSCL)
        Type: general
      – SubjectFull: Learning analytics
        Type: general
      – SubjectFull: Orchestration
        Type: general
      – SubjectFull: CSCL scripts
        Type: general
      – SubjectFull: Collaborative Learning Flow Patterns (CLFPs)
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      – SubjectFull: Adaptive systems
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      – SubjectFull: Aprendizaje Colaborativo Asistido por Ordenador (CSCL)
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      – SubjectFull: Sistemas adaptativos
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      – SubjectFull: Cuadros de mando
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