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Abstract Details
Activity Number:
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399
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract - #300528 |
Title:
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Theory of Self-Learning Q-Matrix
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Author(s):
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Jingchen Liu*+ and Gongjun Xu and Zhiliang Ying
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Companies:
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Columbia University and Columbia University and Columbia University
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Address:
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1255 Amsterdam Ave, New York, NY, 10027, USA
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Keywords:
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Cognitive assessment ;
diagnostic classification model ;
Q-matrix ;
self-learning ;
consistency
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Abstract:
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Cognitive assessment is a growing area in psychological and educational measurement, where tests are given to assess mastery/deficiency of attributes or skills. A key issue is the correct identification of attributes associated with items in a test. In this paper, we set up a mathematical framework under which theoretical properties may be discussed. We establish sufficient conditions to ensure that the attributes required by each item are learnable from the data.
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