Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Adapting to Student Uncertainty Improves Tutoring Dialogues
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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
User Modeling and User-Adapted Interaction
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Modeling mental workload using EEG features for intelligent systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Gaze tutor: A gaze-reactive intelligent tutoring system
International Journal of Human-Computer Studies
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Mental workload, engagement and emotions: an exploratory study for intelligent tutoring systems
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Proceedings of the 14th ACM international conference on Multimodal interaction
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Modelling human tutors' feedback to inform natural language interfaces for learning
International Journal of Human-Computer Studies
When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?
International Journal of Artificial Intelligence in Education - Best of AIED 2011
Knowledge Elicitation Methods for Affect Modelling in Education
International Journal of Artificial Intelligence in Education
Hi-index | 0.00 |
We hypothesize that student affect is a useful predictor of spoken dialogue system performance, relative to other parameters. We test this hypothesis in the context of our spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our system and corpora, which have been annotated with several student affective states, student correctness and discourse structure. We then discuss unigram and bigram parameters derived from these annotations. The unigram parameters represent each annotation type individually, as well as system-generic features. The bigram parameters represent annotation combinations, including student state sequences and student states in the discourse structure context. We then use these parameters to build learning models. First, we build simple models based on correlations between each of our parameters and learning. Our results suggest that our affect parameters are among our most useful predictors of learning, particularly in specific discourse structure contexts. Next, we use the PARADISE framework (multiple linear regression) to build complex learning models containing only the most useful subset of parameters. Our approach is a value-added one; we perform a number of model-building experiments, both with and without including our affect parameters, and then compare the performance of the models on the training and the test sets. Our results show that when included as inputs, our affect parameters are selected as predictors in most models, and many of these models show high generalizability in testing. Our results also show that overall, the affect-included models significantly outperform the affect-excluded models.