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Titlebook: Artificial Intelligence in Education; 16th International C H. Chad Lane,Kalina Yacef,Philip Pavlik Conference proceedings 2013 Springer-Ver

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楼主: polysomnography
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Comparing Student Models in Different Formalisms by Predicting Their Impact on Help Success ensuing student performance, we train a classifier to predict tutor decision outcomes (success or failure) based on situation features, such as student and task. We define a decision policy that selects whichever tutor action the trained classifier predicts in the current situation is likeliest to
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Individualized Bayesian Knowledge Tracing Modelsentation, there are only skill-specific parameters. However, a large body of research strongly suggests that student-specific variability in the data, when accounted for, could enhance model accuracy [5,6,8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on
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Wassim J. Aloulou,Riyadh Alshaeel a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge.
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What Are Annual Reports All About?,ctive questions suitable for group discussion. We evaluate the questions generated by this system against a panel of experienced judges, and find that our approach fares significantly better than the baseline system.
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Automatically Generating Discussion Questionsctive questions suitable for group discussion. We evaluate the questions generated by this system against a panel of experienced judges, and find that our approach fares significantly better than the baseline system.
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Amy Schuman,Stacy Stutz,John L. Wardability and cognitive drive. We found that students in the high cognitive ability + high cognitive drive cluster reported more confusion after receiving false feedback compared to the other clusters. These students also performed better on tasks requiring knowledge transfer, but only when they were meaningfully confused.
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