Since quality education is a pillar of the development of a society and since information and communication technologies (ICT) have permeated the different human activities, including education, it is necessary to evaluate how to achieve quality education in ICT-mediated environments, reducing the identified problems of virtual education, among them the accompaniment, feedback and adaptation to the student’s way of learning. Different disciplines ranging from pedagogy to neuroscience have studied human learning. This article presents an early stage in the development of the larger project called “model for the measurement of academic performance based on non-invasive techniques of the determinants of learning” and focuses on showing in a general way the model, which non-invasive techniques are applicable to determine the student’s behavior in a virtual learning environment and the possible benefits to all those involved in the teaching/learning process, such as the student, the tutor, the institution and finally the model itself.

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Identification of Determinant Factors in Learning Applicable in Virtual Contexts and Measurable with Noninvasive Techniques

  • Andrés Vargas García,
  • Julio Cesar Chavarro Porras

摘要

Since quality education is a pillar of the development of a society and since information and communication technologies (ICT) have permeated the different human activities, including education, it is necessary to evaluate how to achieve quality education in ICT-mediated environments, reducing the identified problems of virtual education, among them the accompaniment, feedback and adaptation to the student’s way of learning. Different disciplines ranging from pedagogy to neuroscience have studied human learning. This article presents an early stage in the development of the larger project called “model for the measurement of academic performance based on non-invasive techniques of the determinants of learning” and focuses on showing in a general way the model, which non-invasive techniques are applicable to determine the student’s behavior in a virtual learning environment and the possible benefits to all those involved in the teaching/learning process, such as the student, the tutor, the institution and finally the model itself.