Persona:
Manjarrés Riesco, Ángeles

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0000-0001-5441-3642
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Manjarrés Riesco
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Ángeles
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2021 - 20232
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Autor: Manjarrés Riesco, Ángeles ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Using LSTM to Identify Help Needs in Primary School Scratch Students
    (MDPI, 2023-11-30) Imbernón Cuadrado, Luis Eduardo; Manjarrés Riesco, Ángeles; Paz López, Félix de la
    first-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.
  • Miniatura
    Publicación
    DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels
    (Springer, 2021-10-13) Barbado, Alberto; Fresno Fernández, Víctor Diego; Manjarrés Riesco, Ángeles; Ros Muñoz, Salvador
    Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, dentifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, per- sonality text mining studies of the authors, or the usage of poetry for therapeutic purposes.