Pilot-Testing a Tutorial Dialogue System That Supports Self-Explanation
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Utterance classification in AutoTutor
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains Modalities
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Analysis of a textual entailer
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Predicting student knowledge level from domain-independent function and content words
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Interventions to regulate confusion during learning
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Hi-index | 0.00 |
Self-explanations (SE) are an effective method to promote learning because they can help students identify gaps and inconsistencies in their knowledge and revise their faulty mental models. Given this potential, it is beneficial for intelligent tutoring systems (ITS) to promote SEs and adaptively respond based on SE quality. We developed and evaluated classification models using combinations of SE content (e.g., inverse weighted word-overlap) and contextual cues (e.g., SE response time, topic being discussed). SEs were coded based on correctness and presence of different types of errors. We achieved some success at classifying SE quality using SE content and context. For correct vs. incorrect discrimination, context-based features were more effective, whereas content-based features were more effective when classifying different types of errors. Implications for automatic assessment of learner SEs by ITSs are discussed.