Imbernón Cuadrado, Luis EduardoManjarrés Riesco, ÁngelesPaz López, Félix de la2024-05-202024-05-202023-11-302076-341710.3390/app132312869https://hdl.handle.net/20.500.14468/12440first-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.enAtribución 4.0 Internacionalinfo:eu-repo/semantics/openAccessartículodistance educationlanguage models (LMs)LSTM modelteaching with Scratchethics in AI