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.
|Title of host publication||Applied Informatics - 5th International Conference, ICAI 2022, Proceedings|
|Editors||Hector Florez, Henry Gomez|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||16|
|State||Published - 2022|
|Event||5th International Conference on Applied Informatics, ICAI 2022 - Arequipa, Peru|
Duration: 27 Oct 2022 → 29 Oct 2022
|Name||Communications in Computer and Information Science|
|Conference||5th International Conference on Applied Informatics, ICAI 2022|
|Period||27/10/22 → 29/10/22|
Bibliographical noteFunding Information:
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.
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Data mining
- Hu moments
- Naïve Bayes
- Pre-writing skills
- Random Forest
- Shape signatures