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Recommended Citation: Mullier, D J, D J Moore, and D J Hobbs.  A Neural-Network system for Automatically Assessing Students.  In The Proceedings of the Information Systems Education Conference 2001, v 18 (Cincinnati): §09c.
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A Neural-Network system for Automatically Assessing Students

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Refereed
 		D. J. Mullier    [a1] [a2]
Faculty of Information and Engineering Systems
Leeds Metropolitan University    [u1] [u2]
Leeds, UK    [c1] [c2]

D. J. Moore    [a1] [a2]
Faculty of Information and Engineering Systems
Leeds Metropolitan University    [u1] [u2]
Leeds, UK    [c1] [c2]

D. J. Hobbs    [a1] [a2]
University of Bradford    [u1] [u2]
Bradford, UK    [c1] [c2]

This paper is concerned with an automated system for grading students into an ability level in response to their ability to complete tutorials. This is useful in that the student is more likely to improve their knowledge of a subject if they are presented with tutorial material at or just beyond their ability. However, dynamically responding to a student's changing knowledge about a subject usually requires the presence of a human teacher, an altogether expensive resource. The system discussed here can grade both a student and the questions in a tutorial with minimal input from the human teacher. In order to accomplish this a specialist neural network is employed. The design and operation of our system is discussed along with arguments as to why a neural network approach is suitable for this problem.

Keywords: automatic assessment, neural network, fuzzy logic

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