Contributions of causality processing models to the study of discourse comprehension and the facilitation of student learning
Discourse comprehension involves the establishment of semantic or meaningful causal connections. The aim of this paper is to review four models that have contributed to the study of the establishment of these connections: the Causal Chain Model, the Causal Network Model, the Causal Inference Maker,...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/122825 |
| Acceso en línea: | http://hdl.handle.net/11336/122825 |
| Access Level: | acceso abierto |
| Palabra clave: | CAUSAL CONNECTIONS COHERENCE DISCOURSE COMPREHENSION INFERENCES LEARNING https://purl.org/becyt/ford/5.1 https://purl.org/becyt/ford/5 |
| Sumario: | Discourse comprehension involves the establishment of semantic or meaningful causal connections. The aim of this paper is to review four models that have contributed to the study of the establishment of these connections: the Causal Chain Model, the Causal Network Model, the Causal Inference Maker, and the Landscape Model. These models contribute to the facilitation of student learning, given that they provide useful tools for improvement of texts structure in order to promote the establishment of meaningful connections and the revision of students’ prior incorrect ideas, and for the design of interventions that promote the generation of inferences and the monitoring of comprehension. The presentation of their key ideas, of empirical support for their psychological validity, and of applications to education will allow us to highlight the contributions that these models make to our understanding of the importance of the processing of causality for discourse comprehension and the facilitation of student learning. |
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