Facebook como Herramienta para Promover la Socialización en Cursos Tradicionales de Inteligencia Artificial. Cálculo del engagement empleando números neutrosóficos triangulares / Facebook as a Tool to Promote Socialization in Traditional Courses of Artif

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Rebeca Escobar Jara
Maikel Leyva Vázquez
Cesar Ernesto Roldan Martínez

Abstract

Objective. Social learning analytics is a subset of learning analysis as it attempts to demonstrate how new skills and ideas are not just individual achievements, but are developed, carried forward and transmitted through interaction and collaboration.Design / Methodology / Approach. The present work analyzes the impact of Facebook, as a tool to promote so-cialization in the traditional courses of Artificial Intelligence. Additionally the engagement in the post of the Face-book page @ MaikelLeyvaOficial generated by the different types of publications is analyzed using the WA opera-tor and the AHP method.Results / Discussion. This information coincides with what is reported in the literature. Regarding the type of publication of the videos, it generally generates the greatest deceit amongthe users and is the preferred one in the survey.Conclusions Additionally, Facebook generates mostly positive perceptions, as well as a sense of community that re-inforces this social network as an alternative to the online learning management system.

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Facebook como Herramienta para Promover la Socialización en Cursos Tradicionales de Inteligencia Artificial. Cálculo del engagement empleando números neutrosóficos triangulares / Facebook as a Tool to Promote Socialization in Traditional Courses of Artif. (2018). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 2(2), 9-21. https://fs.unm.edu/NCML2/index.php/112/article/view/13
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How to Cite

Facebook como Herramienta para Promover la Socialización en Cursos Tradicionales de Inteligencia Artificial. Cálculo del engagement empleando números neutrosóficos triangulares / Facebook as a Tool to Promote Socialization in Traditional Courses of Artif. (2018). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 2(2), 9-21. https://fs.unm.edu/NCML2/index.php/112/article/view/13

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