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This research investigates the challenges faced by students with Specific Learning Disorders (SLDs) in studying Spanish as a Foreign Language, with a particular emphasis on their perceptions and perspectives. For this purpose, two questionnaires were administered to 76 secondary school students with SLDs in the province of Pesaro and Urbino. The first questionnaire, CHAEA Junior, identified the predominant learning style, while the second provided a detailed examination of the students’ attitudes towards studying Spanish. This analysis included an evaluation of the teaching techniques adopted by instructors, the effectiveness of the materials used, and the characteristics of the learning environment. The combined analysis of the collected quantitative data revealed the presence of elements perceived as non-inclusive, thereby prompting significant pedagogical reflections on teaching practices and the educational ecosystem as a whole.

Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author’s writing style, sociodemographic information, such as the author’s gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.

El objetivo de la presente investigación fue describir la experiencia de hombres y mujeres adolescentes de 15 a 18 años, que habitan en el estado de Zacatecas, respecto a la demostración de afecto paterno y materno. Para ello se realizó un muestreo por cuotas, por nivel escolar de secundaria y bachillerato y género; se obtuvo una muestra total de 80 adolescentes, a los cuales se les pidió que contestaran dos preguntas: ¿Qué hace tu mamá para demostrarte afecto? y ¿qué hace tu papá para demostrarte afecto? Las respuestas fueron sometidas a un análisis de contenido. Se encontró que para los varones, las categorías más comunes de afecto materno, fueron: expresión física de afecto, expresión verbal de afecto, cuidados y protección y expresión material de afecto. Mientras que para el afecto paterno fueron: expresión material de afecto, pasar tiempo con ellos y expresión física de afecto. Por su parte, para las adolescentes, las categorías más comunes de afecto materno, fueron: expresión física de afecto, expresión verbal de afecto y cuidados y protección. Mientras que las de afecto paterno, fueron: expresión física de afecto, expresión verbal de afecto y no demuestra su afecto.