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
Can a Computer Listen for Fluctuations in Reading Comprehension?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Automatic system for the detection and analysis of errors to support the personalized feedback
Expert Systems with Applications: An International Journal
Generating diagnostic multiple choice comprehension cloze questions
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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The multiple choice cloze (MCC) question format is commonly used to assess students' comprehension. It is an especially useful format for ITS because it is fully automatable and can be used on any text. Unfortunately, very little is known about the factors that influence MCC question difficulty and student performance on such questions. In order to better understand student performance on MCC questions, we developed a model of MCC questions. Our model shows that the difficulty of the answer and the student's response time are the most important predictors of student performance. In addition to showing the relative impact of the terms in our model, our model provides evidence of a developmental trend in syntactic awareness beginning around the 2nd grade. Our model also accounts for 10% more variance in students' external test scores compared to the standard scoring method for MCC questions.