Abstract
This chapter presents a probabilistic and dynamic learner model based on multi-entity Bayesian networks and artificial intelligence. There are several methods for modelling the learner in AHES, but they're based on the initial profile of the learner created in his entry into the learning situation. They do not handle the uncertainty in the dynamic modelling of the learner based on the actions of the learner. The main purpose of this chapter is the management of the learner model based on MEBN and artificial intelligence, taking into account the different actions that the learner could take during his/her whole learning path. The approach that the authors followed in this chapter is marked initially by modelling the learner model in three levels: they started with the conceptual level of modelling with the unified modelling language, followed by the model based on Bayesian networks to be able to achieve probabilistic modelling in the three phases of learner modelling.
TopIn this section, we will come back to the steps to follow when modeling the learner in an adaptive education system using UML, from the user's Meta model and in the use case diagram. Gathering all the learner’s actions in the adaptive system.
Key Terms in this Chapter
Learner Profile: A part of the learner model that only contain the static information of the learner that could be gathered before developing a learner model.
E-Learning: A concept that describes the cognitive science principles of effective multimedia learning using electronic educational technology.
Multi-Entity Bayesian Networks: A logic system that integrates first order logic (FOL) with Bayesian probability theory.
Adaptive Hypermedia Systems: On-line information and help systems, as well as institutional information systems, that provide hyperlinks that are most relevant to the user in an effort to shape the user's cognitive load.
Learner Situation: A part of a learning path that the learner takes to achieve a diploma or a certification.
Bayesian Networks: Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.