Abstract
Pre-writing skills are a set of essential skills to learn to write. Commonly, in South America’s public schools, a teacher has a class with approximately 30 or more students. As a result, the teacher has the challenging task to detect if a child has difficulties in pre-writing essential activities. In light of the above, in this paper, we present an analysis to determine the feasibility of using computer vision and data mining techniques to determine if a child fails to meet, meets few, or meets a pre-writing skill. We conducted the process with the open corpus “UPS-Writing-Skills,” containing the HU moments and the shape signature descriptors extracted from a collection of 358 images drawn by children.
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Acknowledgments
This work has been funded by the “Sistemas Inteligentes de Soporte a la Educación (v5)” research project, the Cátedra UNESCO “Tecnologías de apoyo para la Inclusión Educativa” initiative, and the Research Group on Artificial Intelligence and Assistive Technologies (GI-IATa) of the Universidad Politécnica Salesiana, Campus Cuenca.
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Jara-Gavilanes, A., Ávila-Faicán, R., Robles-Bykbaev, V., Serpa-Andrade, L. (2022). Rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and Data Mining Techniques in the Ecuadorian Context. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_22
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