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...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/670420 |
| Acceso en línea: | http://hdl.handle.net/10803/670420 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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. |
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