Representational and advisory guidance for students learning scientific inquiry
Smart machines in education
Feedback on Children's Stories via Multiple Interface Agents
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Adaptive feedback generation to support teachers in web-based distance education
User Modeling and User-Adapted Interaction
A latent semantic analysis methodology for the identification and creation of personas
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysing Semantic Flow in Academic Writing
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Exploring individual differences in student writing with a narrative composition support environment
CL&W '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids
Text categorization for assessing multiple documents integration, or John Henry visits a data mine
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Automatic question generation for literature review writing support
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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Learning and reasoning from multiple documents requires students to employ the skills of sourcing (i.e., attending to and citing sources) and information integration (i.e., making connections among content from different sources). Sourcer's Apprentice Intelligent Feedback mechanism (SAIF) is a tool for providing students with automatic and immediate feedback on their use of these skills during the writing process. SAIF uses Latent Semantic Analysis (LSA), a string-matching technique and a pattern-matching algorithm to identify problems in students' essays. These problems include plagiarism, uncited quotation, lack of citations, and limited content integration. SAIF provides feedback and constructs examples to demonstrate explicit citations to help students improve their essays. In addition to describing SAIF, we also present the results of two experiments. In the first experiment, SAIF was found to detect source identification and integration problems in student essays at a comparable level to human raters. The second experiment tested the effectiveness of SAIF in helping students write better essays. Students given SAIF feedback included more explicit citations in their essays than students given sourcing-reminder instructions or a simple prompt to revise.