Computerized Paper Evaluation Using Neural Network

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This paper addresses the issue of exam paper evaluation using neural network. This paper foresees the possibility of using adaptive real time learning through computers viz. the student is made to feed his answers in a restricted format to the computer to the questions it puts up and the answers are evaluated instantaneously. This is accomplished by connecting the computers to a Knowledge Server. This server has actually connections to various authenticated servers (encyclopedias) that contain valid information about all the subjects. The information in the server is organized in a specific manner. The exam is adaptive in the sense that the computer asks distinct questions to each individual depending upon their specialization. This paper also analyzes the role of existing neural network models like Adaptive Resonance Theory (ART), Back Propagation, Perceptron, Self-Organizing Feature Map (SOFM) can be optimized to implement such an evaluation system.

1. Introduction:

Computers have revolutionized the field of education. The rise of internet has made computers a real knowledge bank providing distant education, corporate access etc. But the task of computers in education can be comprehensive only when the evaluation system is also computerized. The real assessment of students lies in the proper evaluation of their papers. Conventional paper evaluation leaves the student at the mercy of the teachers. Lady luck plays a major role in this current system of evaluation. Also the students don’t get sufficient opportunities to express their knowledge. Instead they are made to regurgitate the stuff they had learnt in their respective text books. This hinders their creativity to a great extent. Also a great deal of money and time is wasted. The progress of distance education has also been hampered by the non-availability of a computerized evaluation system. This paper addresses how these striking deficiencies in the educational system can be removed.

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