Published on Feb 12, 2016
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.
Description of Computerized Paper Evaluation using Neural Network
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 Computers have revolutionized the field of education. The rise of internet has made computers a real knowledge bank providing distanteducation, 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.An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
The examination system can be divided basically into three groups for each of the following class groups:
• Primary education
• Secondary education
• Higher secondary education
The examination system has to be entirely different for each of the above groups because of their different learning objectives. In this paper the primary education is not dealt because of its simplicity.