RESUMO
The importance of reading proficiency in a second language (L2) is growing worldwide. Reading in a L2 involves many questions about the abilities of reading comprehension, including metacomprehension, a core reading ability in native speakers (L1) that allows them to be aware of their reading comprehension level and to regulate their learning. However, its link to L2 has not been sufficiently researched. This study examines the relationship between the reading comprehension scores of diverse texts and metacomprehension, assessed by the ECOMPLEC test and PROLEC-R reading task, in Portuguese students studying Spanish as a L2. Similar to the results found in L1 research, we found a significant relationship between reading comprehension and metacomprehension in expository and discontinuous texts: students with low reading comprehension scores overestimated their metacomprehension while metacomprehension abilities of students with high reading comprehension abilities were more accurate. These results provide a frame of reference for future studies on metacomprehension in L2.
El dominio de la lectura en una segunda lengua (L2) tiene cada vez más importancia a nivel global. La lectura en una L2 implica muchas cuestiones sobre las capacidades de comprensión lectora, como la metacomprensión, una habilidad de lectura fundamental en hablantes nativos (L1) que les permite ser conscientes de su nivel de comprensión lectora y les ayuda a regular su aprendizaje. Sin embargo, su papel en una L2 no ha sido lo suficientemente investigado. El presente estudio examina la relación entre las puntuaciones de comprensión lectora de varios tipos de textos y la metacomprensión, evaluada a través de la batería ECOMPLEC y la tarea de lectura de la batería PROLEC-R en estudiantes portugueses que estudian español como L2. De manera similar a los resultados encontrados en investigaciones en L1, hemos descubierto una relación significativa entre la comprensión lectora y la metacomprensión en textos expositivos y discontinuos: los estudiantes con bajas puntuaciones de comprensión lectora sobreestimaron su capacidad de metacomprensión, mientras que las habilidades de metacomprensión de los estudiantes con puntuaciones altas en comprensión lectora fueron más adecuadas. Los resultados proporcionan un marco de referencia para futuros estudios sobre metacomprensión en L2.
Assuntos
Humanos , Compreensão , Testes de Linguagem , MetacogniçãoRESUMO
Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.
