Computational platform for the generation of educational activities through the Mimio Teach tool

Authors

  • Diana Lancheros Cuesta Universidad de La Salle
  • Mayra Daniela Vargas Universidad de La Salle

DOI:

https://doi.org/10.25044/25392190.996

Keywords:

recommendation system, adaptation, psychomotor skills, educational software

Abstract

The recommendation systems are computer models that allow the user to select the best options at the level of content and preferences. Taking into account the above has become an application for children from 6 to 8 years, which allows us to generate recommendations for teaching activities, taking into account a student profile that psychomotor skill, interacting with the technological tool Mimio Teach.

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References

Nasser, K. (2013). Diagnosing Learning Disabilities in a Special Education By an Intelligent Agent Based System. International Journal of Advanced Computer Science and Applications.

Egido Ramos, B. (2014). Las dificultades de la lecto-escritura: dislexia y disgrafía. pautas de intervención y estudio de un caso en educación primaria. Universidad de Valladolid.

Pallarés, A. (2012). Problemas asociados a la dislexia. Revista Neurológica.

J.A., V., M., E., & Giráldez, M. (2009). Perfil psicomotor de niños de 5 a 12 años diagnosticados clínicamente de trastorno por déficit de atención/hiperactividad en Colombia. Obtenido de https://www.neurologia.com/articulo/2008619: https://www.neurologia.com/articulo/2008619

Téllez-de-Reyes, M. C. (2014). Estudio de la psicomotricidad de niños y niñas en edad preescolar. Obtenido de UNIR: https://reunir.unir.net/handle/123456789/2401

Jokisuu, E., Langdon, P., & John, C. (2011). Modelling Cognitive Impairment to Improve Universal Access. Lecture Notes in Computer Science, 6766.

Lancheros-Cuesta, D., Carrillo-Ramos, A., & Pavlich-Mariscal, J. (2014). Content adaptation for students with learning difficulties: Design and case study. International Journal of Web Information Systems, 10 (2), 106-130.

Zhong, T., Li, X., Tu, X., Zhao, S., & Shaoqing, Z. (2016). The prediction of attentional status with task difficulty based on EEG signals. Computer and Communications (ICCC), 2016 2nd IEEE International Conference on.

Al-barrak, L., Kanjo, E., & Younis, E. M. (2017). NeuroPlace: Categorizing urban places according to mental states. PLoS ONE.

Published

2019-12-30

How to Cite

Lancheros Cuesta, D., & Vargas, M. D. (2019). Computational platform for the generation of educational activities through the Mimio Teach tool. Teknos Revista científica, 19(2), 35–40. https://doi.org/10.25044/25392190.996
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