Using artificial intelligent techniques to build adaptative tutoring systems
Denysde Medina Sotolongo, Natalia Martínez Sánchez, Zoila Zenaida García Valdivia · 2007
When a tutoring aims to guide students in teaching/learning process, it needs to know what knowledge student has and what goals student is currently trying to achieve. The Bayesian framework offers a number of techniques for inferring individual's knowledge state from evidence of mastery of concepts or skills. Using Bayesian networks, we have devised probabilistic student models for MacBay, a tutoring system that is an authoring tool. MacBay's models provide prediction of student's action during teaching/learning process. We combined Concept Maps and Bayesian networks in order to obtain a Concept Map with intelligent behavior, where the intelligence is considered as capacity to adapt interaction to its user's specific needs. In this paper we describe way in that we do this combination and inference process.