Personalizing the Selection of Digital Library Resources to Support Intentional Learning

This paper describes a personalization approach for using online resources in digital libraries to support intentional learning. Personalized resource recommendations are made based on what learners currently know and what they should know within a targeted domain to support their learning process. We use natural language processing and graph based algorithms to automatically select online resources to address students' specific conceptual learning needs. An evaluation of the graph based algorithm indicates that the majority of recommended resources are highly relevant or relevant for addressing students' individual knowledge gaps and prior conceptions.