Machine Learning
Modern Information Retrieval
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Text classification using string kernels
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatic detection of learning styles for an e-learning system
Computers & Education
Content Matters: An Investigation of Feedback Categories within an ITS
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Deeper natural language processing for evaluating student answers in intelligent tutoring systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Assessing Student Paraphrases Using Lexical Semantics and Word Weighting
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
IEEE Transactions on Learning Technologies
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This article describes the problem of detecting the student mental models, i.e. students' knowledge states, during the self-regulatory activity of prior knowledge activation in MetaTutor, an intelligent tutoring system that teaches students self-regulation skills while learning complex science topics. The article presents several approaches to automatically detecting students' mental models in MetaTutor based on paragraphs generated by students during prior knowledge activation. Three major categories of methods (content-based overlap methods; cohesion analysis of text; and tf-idf based weighted representations) were developed and combined with machine learning algorithms in order to automatically infer the underlying parameters. A detailed comparison among the methods and across all machine learning algorithms is provided. The evaluation of the proposed methods is performed by eomparing the methods' predictions with human judgments on a set of 309 prior knowledge activation paragraphs collected from experiments with the MetaTutor system on college students. According to the experiments, a word-weighting method, which uses tf-idf values calculated from the corpus, combined with a Bayes Nets machine learning algorithm, offers the most accurate results. Second best performance is given by a Latent Semantic Analysis-based approach enhanced with lexical features and combined with the machine learning algorithm of Logistic Regression.