Evaluating tutors that listen: an overview of project LISTEN
Smart machines in education
Does Learner Control Affect Learning?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Two methods for assessing oral reading prosody
ACM Transactions on Speech and Language Processing (TSLP)
Looking beyond transfer models: finding other sources of power for student models
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Model-based clustering analysis of student data
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Detecting the moment of learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
The fine-grained impact of gaming (?) on learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Content learning analysis using the moment-by-moment learning detector
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
STEMscopes: contextualizing learning analytics in a K-12 science curriculum
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Proceedings of the 17th Panhellenic Conference on Informatics
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A basic question of instruction is how much students will actually learn from it. This paper presents an approach called learning decomposition,whichdetermines the relative efficacy of different types of learning opportunities. This approach is a generalization of learning curve analysis, and uses non-linear regression to determine how to weight different types of practice opportunities relative to each other. We analyze 346 students reading 6.9 million words and show that different types of practice differ reliably in how efficiently students acquire the skill of reading words quickly and accurately. Specifically, massed practice is generally not effective for helping students learn words, and rereading the same stories is not as effective as reading a variety of stories. However, we were able to analyze data for individual student's learning and use bottom-up processing to detect small subgroups of students who did benefit from rereading (11 students) and from massed practice (5 students). The existence of these has two implications: 1) one size fits all instruction is adequate for perhaps 95% of the student population using computer tutors, but as a community we can do better and 2) the ITS community is well poised to study what type of instruction is optimal for the individual.