An Experience Applying Reinforcement Learning in a Web-Based Adaptive and Intelligent Educational System

Ana Iglesias (1) , Paloma Martinez (1) , Fernando Fernandez (1)

1 UC3M - Universidad Carlos III de Madrid [Madrid]

The definition of effective pedagogical strategies for coaching and tutoring students according to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn automatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artificial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies.

Mots clés: adaptive and intelligent web-based educational systems (AIWBES); reinforcement learning; curriculum sequencing

Domaines: Environnements Informatiques pour l'Apprentissage Humain; Education

Ana Iglesias, Paloma Martinez, Fernando Fernandez. An Experience Applying Reinforcement Learning in a Web-Based Adaptive and Intelligent Educational System. Informatics in Education - International Journal, 2003, 2, No. 2, pp.223-240. ⟨hal-00588795⟩