Self organized schemata behavior and meaning formation to evaluate E-learning

Claudia Jaquelina Gonzalez, Ernesto Octavio Lopez, Guadalupe Elizabeth Morales

Resultado de la investigación

1 Cita (Scopus)

Resumen

This paper presents a comprehensive method to implement e-assessment using computer simulated schemata behavior that is based on natural semantic networks obtained from students before and after an academic course. Specifically, a computer system capable of obtaining natural semantic nets from students (as opposed to idiosyncratic semantic nets designed by a researcher) empower implementation of a constrains satisfaction neural net capable of emulate schemata behavior that can be used to formative e-assessment of learning. Empirical support to this approach to evaluate students long term retention of knowledge is presented trough a set of semantic priming studies considering different knowledge domains. Implications to innovate the XXI digital classroom are discussed.

Idioma originalEnglish
Título de la publicación alojadaACM International Conference Proceeding Series
EditorialAssociation for Computing Machinery (ACM)
Páginas32-37
Número de páginas6
ISBN (versión impresa)9781450364317
DOI
EstadoPublished - 26 may 2018
Evento2018 International Conference on Distance Education and Learning, ICDEL 2018 - Beijing
Duración: 26 may 201828 may 2018

Serie de la publicación

NombreACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Distance Education and Learning, ICDEL 2018
PaísChina
CiudadBeijing
Período26/5/1828/5/18

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Citar esto

    Gonzalez, C. J., Lopez, E. O., & Morales, G. E. (2018). Self organized schemata behavior and meaning formation to evaluate E-learning. En ACM International Conference Proceeding Series (pp. 32-37). (ACM International Conference Proceeding Series). Association for Computing Machinery (ACM). https://doi.org/10.1145/3231848.3231877