Technologies for learning writing in L1 and L2 for the 21st century: Effects on writing metacognition, self-efficacy, and argumentative structuring

Arroyo Gonzalez, Rosario ; Fernandez Lancho, Eric ; de la Hoz Ruiz, Javier

Resumen: Quality in higher education assumes the challenge of developing in all citizens of the 21st century the cognitive, motivational, and socio-cultural dimensions that provide them with communication competences including the use of information and communication technologies, for the dissemination of sustainable scientific knowledge in different languages. Hence this paper evaluates a didactic-technological process called “Ensayo Científico Multilingüe” or ECM (“Multilingual Scientific Essay”), which guides the construction of argumentative texts in a shared didactic space in the native language (L1) and in the first foreign language (L2).

Background
Although the use of information and communication technologies (ICTs) in educational contexts stands out as an index of quality, some studies indicate that these technologies, by themselves, do not produce changes in learning. The possibility that ICTs can contribute to a university-quality education is by providing measures that allow verification of the effects on the real improvement of the learning of communication competences of students and, especially, in the learning of written communication for the purpose of scientific dissemination. In order to do so, this research is based on the Metasociocognitive Written Composition (MWC) model that explains university writing as a complex process in which, metacognitively, cognitive-linguistic competences and socio-cultural experiences interact with motivational factors to achieve outreach objectives within a disciplinary field. This interaction always takes place by applying a specific textual genre.

Idioma: Inglés

DOI: 10.28945/4705

Año: 2021

Publicado en: Journal of Information Technology Education:Research 20 (2021), 87 - 116
ISSN: 1547-9714

Factor impacto CITESCORE: 3.5 - Social Sciences (Q1) - Computer Science (Q2)

Factor impacto SCIMAGO: 0.628 - Education (Q2) - Computer Science (miscellaneous) (Q2)

Tipo y forma: Artículo (Versión definitiva)