Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach.

Sequencing contents, like tasks, hints, and feedbacks, is an open issue for Intelligent Tutoring Systems. The common approach is based on domain analysis by experts, who characterize each content with skills involved and a difficulty level. In addition, Machine Learning based sequencers require a specific dataset collection to create users' models and a sequencing policy, which needs to be tested online with strong ethical requirements and a high number of users. In this paper we design a simulated learning environment with customizable scenarios. We also show that a performance prediction method can be used to crate offline fully personalized students' models and sequence contents without domain engineering/authoring effort. The performance prediction method is enhanced by a score-based policy inspired by Vygotsky's concept of Zone of Proximal Development and shows promising results compared to curriculum based policies in the designed simulated environment.