02053nas a2200253   4500000000100000000000100001008004100002260001200043653003100055653002300086653003400109653003300143653002300176100003100199700002800230700003100258700003200289245007500321856008100396300001200477490000600489520129000495022001401785       2021                            d  c06/202110aBayesian Knowledge Tracing10aBloom’s Taxonomy10aComputer-Assisted Instruction10aIntelligent Tutoring Systems10aMarzano's Taxonomy1 aFrancisco Cervantes-Pérez1 aJoaquin Navarro-Perales1 aAna L. Franzoni-Velázquez1 aLuis de-la-Fuente Valentín00aBayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_24.pdf  a234-2390 v63 aIn 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.  a1989-1660