Computerized Paper Evaluation using Neural Network
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computer science crazy
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#1
22-09-2008, 10:29 AM


Definition
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.

Introduction
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.

Neural Network - Basics
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.
Role of Neural Network:

Tasks cut out for the neural network:
a. Analyze the sentence written by the student.
b. Extract the major components of each sentence.
c. Search the reference for the concerned information.
d. Compare the points and allot marks according to the weightage of that point.
e. Maintain a file regarding the positives and negatives of the student.
f. Ask further questions to the student in a topic he is more clear off.
g. If it feels of ambiguity in sentences then set that answer apart and continue with other answers and ability to deal that separately with the aid of a staff.
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suvarna123
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#2
30-09-2009, 04:14 PM

please send me working and implementation of "computerized paper evaluation using neural network".please send me more and more on this topic.I want additional information except u have provided.
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computer science crazy
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30-09-2009, 06:51 PM


.doc   computerized paper evaluation using neural network.doc (Size: 145 KB / Downloads: 264)

and also use to see
ducati.doc.ntu.ac.uk/uksim/journal/issue-1/RichardCant/paper.doc
scribddoc/19799718/COMPUTRISED-PAPER-EVALUATION-USING-NEURAL-NETWORK
scribddoc/18998119/Computrised-Paper-Evaluation-Using-Neural-Network



for Computerized Paper Evaluation using Neural Network
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madhualuwala
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13-03-2011, 11:14 PM

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seminar class
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15-03-2011, 11:22 AM


.docx   evaluation of papers using neuron net.docx (Size: 18.69 KB / Downloads: 93)
ABSTRACT
This paper addresses the issue of exam paper evaluation using neural network. This paperforesees the possibility of using adaptive real time learning through computers viz. the student ismade to feed his answers in a restricted format to the computer to the questions it puts up and theanswers are evaluated instantaneously. This is accomplished by connecting the computers to aKnowledge Server. This server has actually connections to various authenticated servers(encyclopedias) that contain valid information about all the subjects. The information in theserver is organized in a specific manner. The exam is adaptive in the sense that the computerasks distinct questions to each individual depending upon their specialization. This paper alsoanalyzes the role of existing neural network models likeBack Propagation, Perceptron, Self-Organizing Feature Map (SOFM) can be optimized to implement such an evaluation system.
CHAPTER 1
INTRODUCTION

Computers have revolutionized the field of education. The rise of internet has made computers areal knowledge bank providing distant education, corporate access etc. But the task of computersin education can be comprehensive only when the evaluation system is also computerized. Thereal assessment of students lies in the proper evaluation of their papers. Conventional paperevaluation leaves the student at the mercy of the teachers. Lady luck plays a major role in thiscurrent system of evaluation. Also the students don’t get sufficient opportunities to express theirknowledge. Instead they are made to regurgitate the stuff they had learnt in their respective textbooks. This hinders their creativity to a great extent. Also a great deal of money and time iswasted. The progress of distance education has also been hampered by the non-availability of acomputerized evaluation system. This paper addresses how these striking deficiencies in theeducational system can be removed.
CHAPTER 2
CONVENTIONAL EVALUATION SYSTEM

The evaluation system at present involves the students writing their answers for the questionsasked, in sheets of paper. This is sent for correction to the corresponding staff. The evaluatormay be internal or external depending on the significance of the exam. The evaluator uses thekey to correct the paper and the marks are awarded to the students based on the key.
2.1.DEMERITS:
2.1.1. EVALUATORS BIASNESS:

This has been the major issue of concern for the students. When the staff is internal, there isalways a chance for him to be biased towards few of his pupils. This is natural to happen and theevaluator cannot be blamed for that.
2.1.2. IMPROPER EVALUATION:
Every evaluator will try to evaluate the papers given to him as fast as possible. Depending on theevaluation system he’ll be given around ten minutes to correct a single paper. But rarely doesone take so much time in practice. They correct the paper by just having an out look of the paper.This induces the students to write essays so that marks can be given for pages and not forcontents. So students with real knowledge are not really rewarded.
2.1.3. APPEARANCE OF THE PAPER:
The manual method of evaluation is influenced very much by the appeal of the paper. If the
student is gifted with a good handwriting then he has every chance of outscoring his colleagues.
2.1.4. TIME DELAY:
Usually manual correction takes days for completion and the students get their resultsonly after months of writing exams. This introduces unnecessary delays in transition to thehigher classes.
2.1.5. NO OPPURTUNITY TO PRESENT STUDENT IDEAS:
The students have really very little freedom in presenting their ideas in the conventional system.
The student has to write things present in his text book
CHAPTER 3
PROPOSED SYSTEM
3.1. BASIS:

Having listed out the demerits of the current evaluation system, the need for a new one becomes
the need of the hour.
This proposal is all about computerizing the evaluation system by
applying the concept of Artificial Neural Networks.
3.2. SOFTWARE:
The software is built on top of the neural net layers below. This software features all therequirements of a regular answer sheet, like the special shortcuts for use in Chemistry likesubjects where subscripts to equation are used frequently and anything else required by thestudent.
3.3 A SIMPLE NEURON:
Figure1. A Simple Neuron
An artificial neuron is a device with many inputs and one output. The neuron has two modes ofoperation; the training mode and the using mode. In the training mode, the neuron can be trainedto fire (or not), for particular input patterns. In the using mode, when a taught input pattern isdetected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.
3.4. FIRING RULES:
The firing rule is an important concept in neural networks and accounts for their highflexibility. A firing rule determines how one calculates whether a neuron should fire for anyinput pattern. It relates to all the input patterns, not only the ones on which the node was trained.
CHAPTER 4
NEURAL NETWORK

An Artificial Neural Network (ANN) is an information processing paradigm that isinspired by the way biological nervous systems, such as the brain, process information. The keyelement of this paradigm is the novel structure of the information processing system. It iscomposed of a large number of highly interconnected processing elements (neurons) working inunison to solve specific problems.
Figure 2 : A Neural Network
The commonest type of artificial neural network consists of three groups, or layers, of units:a layer of "input" units is connected to a layer of "hidden" units, which is connected to a layer of"output" units.
The activity of the input units represents the raw information that is fed into the network.
The activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units.
The behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output uni
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#6
23-09-2011, 07:43 PM

please send me complete report on Computerized Paper Evaluation using Neural Network.
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24-09-2011, 09:50 AM

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30-09-2011, 01:53 PM

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31-12-2011, 07:20 PM

hi!!
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03-02-2014, 03:21 PM

(31-12-2011, 07:20 PM)ramya indian Wrote: hi!!
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