Introduction to algorithms
Designing intentional learning environments
SIGDOC '97 Proceedings of the 15th annual international conference on Computer documentation
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
INSPIRE: An INtelligent System for Personalized Instruction in a Remote Environment
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Towards automatic conceptual personalization tools
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Computational foundations for personalizing instruction with digital libraries
International Journal on Digital Libraries - Special Issue on Educational digital libraries on the verge
Creating and delivering adaptive courses with AHA!
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries
Algorithms for robust knowledge extraction in learning environments
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Conceptual personalization technology: promoting effective self-directed, online learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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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.