Adelina Escobar-Acevedo1, Josefina Guerrero-García1, Rafael Guzmán-Cabrera2
1Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias de la Computación, México. adelina.escobar@alumno.buap.mx
2Universidad de Guanajuato, Campus Irapuato-Salamanca, División de Ingenierías, Departamento de Ingeniería Eléctrica.
Rupkatha Journal, Vol. 14, Issue 3, September-October 2022, Pages 1–8. https://doi.org/10.21659/rupkatha.v14n3.17
First published: October 17, 2022 | Area: ELT | License: CC BY-NC 4.0
(This article is published under the General Area)
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A Model Text Recommendation System for Engaging English Language Learners: Facilitating Selections on CEFR
Abstract
A pedagogically informed multimodal education system is defined by how well reading tasks are assigned to students in a contemporary classroom. A source that becomes a provider of readings is the web, where it is possible to find information on practically all areas of knowledge and in a wide variety of languages. However, selecting the appropriate material for the level and theme becomes a tedious job to which language teachers must devote a significant amount of their time. Selecting suitable readings to accompany the teaching-learning process is thus not a ‘trivial’ task. Basic-level texts for language competence are easy to recognize and obtain but as is seen in the case of the Common European Framework of Reference for Languages recommendations (CEFR), selection of appropriate texts that impart language competencies, especially of vocabulary and grammar at higher levels of communicativeness, selection becomes increasingly complex for teachers. Furthermore, the suggested readings should be raked by complexity in accordance with student capabilities. We suggest, that automatic classifiers based on CEFR levels may help in this process of selections from the already available corpora of authentic texts on the web. The existing facility of access of readers to such material on the web may come to the aid of automated classifiers. Teachers use interest to motivate reading in classrooms, but automatic recommendation systems will allow specific or even individualized recommendations. The authors explore the impact of such multimodal methods on the acquisition of better linguistic and communicative skills.
Keywords: English Language Learners, CEFR Language level, Linguistic Features, Text Complexity.