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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Detalles Bibliográficos
Autor: Lobo Quintero, René Alejandro
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
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Descripción
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.