From visual text to written text: a multimodal study of two essay proposals from Desafio Nota 1000 Program in digital environments

• Autor(a) principal: Valdivino da Silva, Maria Zenaide
• Data de Publicação: 2021
• Outros Autores: Ferreira Queiroga Lins, Fabiana, de Freitas Lopes, Francisco Edson
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Texto digital
• Texto Completo: https://periodicos.ufsc.br/index.php/textodigital/article/view/83429

This article aims to investigate articulations between multimodal texts in drafting proposals for the Program Desafio Nota 1000 - 2nd and 5th editions carried out by the Department of Education, Science and Technology of Paraíba (SEECT/PB) in 2021 - and guidelines for reading and textual production to the practice of students for writing a dissertation-argumentative text in a model proposed by ENEM. For this purpose, we analyzed the multimodality in two writing proposals conveyed in digital environments, from the disposition of verbal-visual motivating texts, in line with the meta-functions of the Grammar of Visual Design (GDV), by Kress and van Leeuwen (1996, 2006, 2021). In a theoretical-methodological perspective, we place the study in the field of Applied Linguistics, based on the functioning of authors such as Hodge and Kress (1988), Cope; Kalantzis (2000), Kress (2001, 2003, 2010), Silva (2016), among others, about Multimodality and Social Semiotics, from a qualitative approach of descriptive and interpretive nature. The results showed that students need to be inserted in an environment of visual literacy practices to produce meanings, based on the relationship of social meanings between verbal and non-verbal texts. Thus, we conclude that the training of readers/producers of interested multimodal texts is essential, given the need for participants to develop greater proficiency in critical / visual reading of motivating texts, thus allowing a consistent written production not only to the ENEM evaluation matrix, but also to the production of meanings inherent to every multimodal text.