Publicação
A preliminary proposal of a conceptual Educational Data Mining framework for Science Education: Scientific competences development and self-regulated learning
| Resumo: | The present paper is part of a wider study, focussed on the development of a digital educational resource for Science Education in primary school, integrating an Educational Data Mining framework. The proposed conceptual framework aims to infer the impact of the adopted learning approach for the development of scientific competences and students’ self-regulated learning. Thus, students’ exploration of learning sequences and students' behaviour towards available help, formative feedback and recommendations will be analysed. The framework derives from the proposed learning approach, as well as from the literature review. Before introducing it, the authors present an overview of the digital educational resource learning approach and the adopted Educational Data Mining methods. Finally, we present the proposed conceptual Educational Data Mining framework for Science Education, focussing its relevance on the development of students' scientific competences and self-regulated learning. |
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| Autores principais: | Tavares, Rita |
| Outros Autores: | Vieira, Rui; Pedro, Luís |
| Assunto: | Educational data mining Latent knowledge estimation Causal data mining Domain structure discovery Digital educational resources Scientific competences Selfregulated learning |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | The present paper is part of a wider study, focussed on the development of a digital educational resource for Science Education in primary school, integrating an Educational Data Mining framework. The proposed conceptual framework aims to infer the impact of the adopted learning approach for the development of scientific competences and students’ self-regulated learning. Thus, students’ exploration of learning sequences and students' behaviour towards available help, formative feedback and recommendations will be analysed. The framework derives from the proposed learning approach, as well as from the literature review. Before introducing it, the authors present an overview of the digital educational resource learning approach and the adopted Educational Data Mining methods. Finally, we present the proposed conceptual Educational Data Mining framework for Science Education, focussing its relevance on the development of students' scientific competences and self-regulated learning. |
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