Bayesian knowledge tracing for navigation through Marzano’s taxonomy

  1. Francisco Cervantes-Pérez 1
  2. Joaquin Navarro-Perales 2
  3. Ana L. Franzoni-Velázquez 3
  4. Luis de la Fuente Valentín 4
  1. 1 Universidad Internacional de La Rioja en México
  2. 2 2 Universidad Nacional Autónoma de México
  3. 3 3 Instituto Tecnológico Autónomo de México
  4. 4 Universidad Internacional de La Rioja
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    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2021

Volumen: 6

Número: 6

Páginas: 234-239

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.05.006 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom’s taxonomy. For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses. These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano’s Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them. The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner. 3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels. One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult.

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Fuente de los datos: Dialnet