Motivation-driven mechanics in computer-supported collaborative learning scripts
This dissertation explores motivation within Collaborative Learning Flow Patterns (CLFP), focusing on the Pyramid CLFP. It investigates how incorporating game mechanics (GM) can foster both intrinsic and extrinsic motivation to improve collaborative learning experiences. The study is structured arou...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/689827 |
| Acceso en línea: | http://hdl.handle.net/10803/689827 |
| Access Level: | acceso abierto |
| Palabra clave: | Collaborative learning Pyramid Collaborative Learning Flow Pattern Learning analytics CSCL CLFP Aprendizaje colaborativo Flujo de Aprendizaje Colaborativo Pyramid Analítica del aprendizaje 62 |
| Sumario: | This dissertation explores motivation within Collaborative Learning Flow Patterns (CLFP), focusing on the Pyramid CLFP. It investigates how incorporating game mechanics (GM) can foster both intrinsic and extrinsic motivation to improve collaborative learning experiences. The study is structured around three main objectives. Firstly, it examines how GM within Pyramid CLFP can induce a flow state, using Self-Determination Theory and game-based learning insights to optimize engagement. The second objective proposes developing Narrative Scripts (NS) that blend GM with collaborative scripts to elevate satisfaction, attention, and interest among students. Testing this approach revealed its capacity to alter student perceptions and enhance intrinsic motivation. The third objective delves into extrinsic motivation, analyzing reward-based gamification and its impact on participation. It further explores how the interplay between GM, extrinsic motivation, and the Group Awareness Tool within PyramidApp influences student engagement and learning outcomes. |
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