Aplicación de procesos Markovianos para recomendar acciones pedagógicas óptimas en tutores inteligentes

We describe a project still in development about Intelligent Tutoring Systems and optimal educational actions. Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important 33 Research in Computing Science 111 (2016) pp. 33–45; rec. 2016-03-08; acc. 2016-05-05 Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended actions in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain