Dataset (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)
To test their models, they used 5 different datasets:
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ASSIST2009 - " The dataset contains 4, 151 students with 325, 637 question and answering interactions from 26, 688 questions of 110 skills."
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ASSIST2015 - "This dataset contains 19, 840 student responses for 100 skills with a total of 683, 801 question-and-answering interactions"
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STATIC2011 - "This dataset is obtained from an engineering statics course with 189, 927 interactions from 333 students and 1, 223 question tags of 156 skills"
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SYNTHETIC - 2000 students question-and-answer trajectories were synthesized.
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FSAI-F1toF3 - "It consists of 51, 283 question-and-answer interactions from 310 students on 2, 266 questions of 99 skills"
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Dataset (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)
Implementation (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)
Results (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)