Publicación: Using LSTM to Identify Help Needs in Primary School Scratch Students
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2023-11-30
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Atribución 4.0 Internacional
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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MDPI
Resumen
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.
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Categorías UNESCO
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distance education, language models (LMs), LSTM model, teaching with Scratch, ethics in AI
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Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial