A Framework for a Multi-Faceted, Educational, Knowledge-Based Recommender System

Abstract

The literature on intelligent or adaptive tutoring systems generally has a focus on how to determine what resources to present to students as they make their way through a course of study. The idea of multi-faceted student modeling is that a variety of measures, both academic and non-academic, might be represented in student models in service of a broader educational context. This paper contains a framework for a multi-faceted, educational, knowledge-based recommender system, including a basic set of descriptors that the model contains, and a taxonomy of inferences that might be made over such models.

Keywords

• recommender system
• intelligent tutoring system
• student model
• Semantic Web