Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy

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

Analysing the Outcome of a Learning Process Conducted Within the System ALS_CORR[LP]

This paper presents the results of an experiment, conducted on a sample of computer science students, using the adaptive learning system called ALS_CORR[LP] 1. Indeed, unlike the traditional LMS, the adaptive learning systems provide a personalized learning experience based on the objectives, the prerequisites or even the learning styles generating thereafter a specific learning path. However their main issue remains the fact, that they assume that the generated learning path is necessarily the leading one, which is far from being true, since we can always detect some failure cases during the evaluation phase. In this paper we conduct a learning experience using the system ALS_CORR[LP] which has the ability to correct the generated learning path by recommending the most relevant learning objects, and update the learner model based on a calculation of similarity in behavior between the struggling learner and the succeeding ones. We analyze later the results of behavior tracking within the system