Interpreting symptoms of cognitive load in speech input
UM '99 Proceedings of the seventh international conference on User modeling
Using Knowledge Tracing in a Noisy Environment to Measure Student Reading Proficiencies
International Journal of Artificial Intelligence in Education
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Better student assessing by finding difficulty factors in a fully automated comprehension measure
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Automated Assessment of Oral Reading Prosody
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Two methods for assessing oral reading prosody
ACM Transactions on Speech and Language Processing (TSLP)
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Generating diagnostic multiple choice comprehension cloze questions
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Toward unobtrusive measurement of reading comprehension using low-cost EEG
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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The ability to detect fluctuation in students' comprehension of text would be very useful for many intelligent tutoring systems. The obvious solution of inserting comprehension questions is limited in its application because it interrupts the flow of reading. To investigate whether we can detect comprehension fluctuations simply by observing the reading process itself, we developed a statistical model of 7805 responses by 289 children in grades 1-4 to multiple-choice comprehension questions in Project LISTEN's Reading Tutor, which listens to children read aloud and helps them learn to read. Machine-observable features of students' reading behavior turned out to be statistically significant predictors of their performance on individual questions.