Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Content Creation
IEEE Transactions on Learning Technologies
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Tagging educational content with knowledge components (KC) is key to providing useable reports to teachers and for use by assessment algorithms to determine knowledge component mastery. With many systems using fine-grained KC models that range from dozens to hundreds of KCs, the task of tagging new content with KCs can be a laborious and time consuming one. This can often result in content being left untagged. This paper describes a system to assist content developers with the task of assigning KCs by suggesting knowledge components for their content based on the text and its similarity to other expert-labeled content already on the system. Two approaches are explored for the suggestion engine. The first is based on support vector machines text classifier. The second utilizes K-nearest neighbor algorithms employed in the Lemur search engine. Experiments show that KCs suggestions were highly accurate.