Automatic essay grading using text categorization techniques
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
SRI International: description of the FASTUS system used for MUC-4
MUC4 '92 Proceedings of the 4th conference on Message understanding
Text Categorization for Aligning Educational Standards
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Using Intelligent Feedback to Improve Sourcing and Integration in Students' Essays
International Journal of Artificial Intelligence in Education
Association of domain concepts with educational objectives for e-learning
Proceedings of the Third Annual ACM Bangalore Conference
Automated approaches for detecting integration in student essays
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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A critical need for students in the digital age is to learn how to gather, analyze, evaluate, and synthesize complex and sometimes contradictory information across multiple sources and contexts. Yet reading is most often taught with single sources. In this paper, we explore techniques for analyzing student essays to give feedback to teachers on how well their students deal with multiple texts. We compare the performance of a simple regular expression matcher to Latent Semantic Analysis and to Support Vector Machines, a machine learning approach.