Learning English as a second language: towards the “Mayday” intelligent educational system
PEG 91 Selected papers of the sixth international annual conference of the PEG group on Knowledge based environments for teaching and learning
Does feedback enhance computer-assisted language learning?
Computers & Education - Special issue on exploring the nature of research in computer-related applications in education
An intelligent tutoring system for introductory C language course
Computers & Education
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition
Pattern Recognition Letters
Emotion Classification Using Web Blog Corpora
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
International Journal of Artificial Intelligence in Education
Fast hierarchical goal schema recognition
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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In this work, an adaptive method for feedback strategy selection is proposed in the context of intelligent tutoring systems. This uses a combination of machine learning methods to automatically select the best feedback strategy for students engaging in a foreign language learning context. Experiments show that our adaptive multi-strategy feedback model allows students to achieve correct answers by reducing their errors. Results also show the promise of the method compared with traditional methods of feedback generation. The approach is not only capable of dynamically adapting a feedback strategy, but also guiding the tutorial conversation so that student's correct answers can be obtained with a minimum feedback. Our approach also suggested that combining SVM and CRF models are promising to get effective feedback correction from student tutoring, showing that our multi-strategy selection approach outperformed the traditional meta-linguistic rules based feedback strategies. Experiments also showed a good correlation between the best strategy generated by our model and the decision taken by a human tutor.