Indicators of ADHD symptoms in virtual learning context using machine learning technics

  1. Laura Patricia Mancera Valetts 1
  2. Silvia Margarita Baldiris Navarro 2
  3. Viviana Betancur Chicué 2
  1. 1 Universidad Manuela Beltrán Docente investigadora
  2. 2 Universidad Manuela Beltrán Docente
Revista:
Revista Escuela de Administración de Negocios

ISSN: 2590-521X,  0120-8160

Año de publicación: 2015

Número: 79

Páginas: 22-37

Tipo: Artículo

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Otras publicaciones en: Revista Escuela de Administración de Negocios

Resumen

This paper presents a user model for students performing virtual learning processes. This model is used to infer the presence of Attention Deficit Hyperactivity Disorder (ADHD) indicators in a student. The user model is built considering three user characteristics, which can be also used as variables in different contexts. These variables are: behavioral conduct (BC), executive functions performance (EFP), and emotional state (ES). For inferring the ADHD symptomatic profile of a student and hislher emotional alterations, these features are used as input in a set of classification rules. Based on the testing of the proposed model, training examples are obtained. These examples are used to prepare a classification machine learning algorithm for performing, and improving, the task of profiling a student. The proposed user model can provide the first step to adapt learning resources in e-learning platforms to people with attention problems, specifically, young-adult students with ADHD.

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