Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Addressing the assessment challenge with an online system that tutors as it assesses
User Modeling and User-Adapted Interaction
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Modeling multiple distributions of student performances to improve predictive accuracy
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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In typical assessment student are not given feedback, as it is harder to predict student knowledge if it is changing during testing. Intelligent Tutoring systems, that offer assistance while the student is participating, offer a clear benefit of assisting students, but how well can they assess students? What is the trade off in terms of assessment accuracy if we allow student to be assisted on an exam. In a prior study, we showed the assistance with assessments quality to be equal. In this work, we introduce a more sophisticated method by which we can ensemble together multiple models based upon clustering students. We show that in fact, the assessment quality as determined by the assistance data is a better estimator of student knowledge. The implications of this study suggest that by using computer tutors for assessment, we can save much instructional time that is currently used for just assessment.