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Neuro-Fuzzy Approach to Measure Sociological Impact on Education

Rev. P.R. Jayathilaka, Lakshman Jayaratne

Abstract- Education and Society are closely bound, such that they cannot be separated from each other because education builds up society and on the other hand society is the source of education .Education transfers culture, knowledge and etc of a society from one generation to the next. Hence, better education is for a better society and healthier society becomes the foundation of better education. Education could be formal, informal and non-formal and in this research, formal education is the prime concern. The research is aimed at tracing the correlation between the sociological environment of a Sri Lankan student and his/her educational performance during the grades 10 and 11. A thorough survey was conducted covering a selected sample of population to extract data from the students on their social aspects and conduct and their performance in the G.C.E (O Level) Examination results. A Neural Network with a Fuzzy interface was trained on past data and the performance of the network was evaluated using a test data set. The results with nearly 65% accuracy are encouraging in order to further improve the methodology towards better results. However, the final goal of the research is to prepare the ground to develop a tool which helps counselors to make decisions while helping students to enhance performance at G.C.E (O Level) Examination. Keywords- Education, Fuzzy, Neural Network, Counseling, Sociology, Performance

1. INTRODUCTION

Over the past years, numerous researches had been conducted in relation to the sociological impact on education. They have significantly contributed to understand the correlation between education and society. In the mid-century, there were few theoretical and empirical researches regarding sociology of education. However, towards the later centuries the complexity and the number of the researches were grown. Many of these researches prove the interdependency of society and education (Hallinan, 2006). Hence, this work is to explore the possibilities of improving the educational performance specially at G.C.E. O/L Examination with the social support. The hypothesis of this work is that the society could help in improving one’s educational achievements. Here

the society is meant by family, social associates, school and etc. Education is highly respected by the Sri Lankan society over thousands of years. In general, all parents encourage their children to have a good education. As a result, Sri Lanka maintains a literacy rate of 91.2 % in the world ranking(“List of countries by literacy

rate,” 2013). However, due to many sociological factors some students who are intelligent by birth are unable to pursue their studies well. Around 50% of students who sit for the G.C.E. O/L examination

annually are unable to get through the examination(Statistic, n.d.). A student sit for this examination after being prepared for eleven years and in Sri Lanka G.C.E. O/L is the generally accepted basic education qualification. However, each year nearly fifty percent of the students leave school empty handed, most of whom with the feeling of being rejected. This situation creates many social and political issues. Hence, the primary motivation of this research is to suggest a system to support counselors at school levels to help students to improve their performance at public examinations. The experience of the author as a teacher and a counselor for a period of eight years influence to explore the possibility of identifying those students who do not perform competitively with others at an early stage. Identifying such students might help the teachers and counselors to guide them more effectively towards the success of their educations. Here, the approach to the problem is intelligent computer based. Artificial Neural Networks (ANNS) and Fuzzy Logic are used as the tools. ANNS have been evolved to simulate the functions of the brain in pattern recognition. In the traditional computer environment the solution to the problem is well define but it is not adaptable to new situations because it only does the following of steps from the problem to the solution. However, ANNS is capable of handling such new situations. Fuzzy logic is used to automate managing systems where linguistic terms are heavily used. Fuzzy logic

DOI: 10.5176/2251-3043_3.4.286

GSTF International Journal on Computing (JoC) Vol.3 No.4, April 2014

©The Author(s) 2014. This article is published with open access by the GSTF

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Received 27 Feb 2014 Accepted 10 Mar 2014

DOI 10.7603/s40601-013-0037-6

has been evolved as a popular tool in the automation of control systems. However, its strengths have been realized and used in many other applications. Due to the nature of this research, a fuzzy interface has been designed to strengthen the classification process. These tools measure the social and educational correlation. Then the system tries to identify particular areas, which affect each individual, for example, the impact of peers on an individual. The geographical boundary of the research is limited to Puttalam District in Sri Lanka. It covers two educational administrative areas namely Puttalam Zone and Chilaw Zone. The rest of the article is structured as follows. Literatures related to the education and performance is reviewed. This is to explore the concept of education from different angles and to provide background information to the solution. Then the methodology followed in the research is discussed. It has two approaches. One approach is based on both ANNS and Fuzzy and the other is ANNS only. In the next phase the results are evaluated to see if it is related to the objective of the research. It is concluded with some suggestions to improve the work.

2. PREVIOUS WORK

2.1 Philosophical, Psychological and Sociological

Aspect of Education Philosophical, psychological and sociological aspects are the key concepts which govern education. Philosophical aspect attempts to set the norms and guidelines to the framework of education exploring the question of ‘why education?’ Psychological aspect

deals with the pathway of education in an individualistic perspective. Simply, this implies the fact that a newborn baby could not be fed with solid food until he or she reaches the maturity required to do so. This is human nature and psychological aspect of education deals with this. That is to see the possibilities of incorporating education into individual beings. Sociological aspect of education is the source and summit of this work and it is the major determinant factor of education. According to the great philosopher Plato, education is the key to create and sustain his Republic, which is an ideal society where the state is constituted with individuals with different capabilities. He speaks of three social classes. The formation of the individual in these classes gave birth to the Platonian concept of

education. For him, education is holistic and it should include every human discipline possible as art, skill development, physical discipline, and music (Sharma, 2002). Jean-Jacques Rousseau, father of the child centered education believes that the education should be catering to the ability, desire and need of a child. Rousseau, whose philosophical approach is naturalistic, believed that child is good by nature and education of a child should be according to the natural laws of its nature. Therefore, Rousseau gave high priority to the child’s need, thoughts, desires, feelings and values. According to Naturalism, parents are natural teachers and it implies that the necessity of a formal agency of education such as school is not compulsory. However, this is not the truth. Compared to other animals, man has a longer period of infancy, requiring constant guidance and protection, which is the chief phenomena on which the concept of school is based (Pathak, 2007). Psychological aspect of education is a distinct discipline, which owns its own theories, principles research methods, problems and techniques because its primary goal is to improvement of education (Woolfolk, 2006). Educational psychology is not a perfect science as its main, which is human behavior, is unpredictable. Hence, as natural sciences it cannot claim objectively its validity or exactness. The key factors involve in educational Psychology are learner, learning process, learning experience, learning situation or the environment and the teacher (Mangal, 2007). Jean Piaget who was a biologist is one of the leading psychologists in the field of educational psychology and proposed the theory of cognitive development. According to Piaget cognitive development is not simply adding new facts and ideas to the present store of knowledge rather it is our thinking process which is changing radically but slowly from birth to death as a result of our effort to sense the world. Vygotsky(1896-1943 )believed that human activities take place in cultural setting and they cannot be understood separately from ones cultural setting. For example, polygamy is accepted among Muslims and it is not the same story with many other nations and also the living together is common in Western countries but it is look down upon by the Eastern mentality. Vygotsky believed that human child was born with elementary mental functions, natural unlearned capacities as attending and sensing and it is through the social interaction that they attained higher mental



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functions as possibility of thinking and problem solving (Bentham, 2002). Pestolozzi, Herbert, and Froebel who emphasis the important of psychology in education, also admits the influences of sociological factors in the child’s

development. Brown says that sociology of education is the study of interaction of an individual with his cultural environment or in other it is the study of the way in which the society influences person’s life and

on the other hand the way the persons change the society. Carter says that educational psychology is the study of those phases of sociology that are significant for educative processes, especially of those that point to valuable programme of learning and control learning progress. Ottoway says that educational sociology starts with an assumption that education is an activity which is going on in the society and society in turn determines the nature of the education(Chandra and Sharma, 2004). 2.2 Social impact, Self Concept and Self Descriptive Questionnaire The term social impact refers to the influence of the

society over an individual which changes his or her

personality. Personality is what makes a person unique.Personality rules person’s feelings, behaviour, thought patterns and so on. Though personality is psychological it is also linked with the biological processes. It is not arbitrary, and it organised different aspect for a better interconnected self. Personality cause things to happen and influences the way the individual interaction(Aurther, 2006) . Man as a social being needs to work for him and for his or her society. In other word, his personality should suites either his or her needs as well as social needs. Otherwise, he would not be accepted by the society. This influence created by social needs eventually changes self –concept or perception of one’s

personality(Bhatti, 2011). According to Rao, Self-concept is crucial as it is operate as the core function of a person. It is the mechanism of the personality development and the drive for self actualization or improvement or perfection, which is the ultimate goal of an individual (Rao, 2002). When it comes to the performance in education this fact holds a greater truth. Quoting Burns, Downey says that self esteem or one’ realization of his or her

personality greatly affects his or her entire educational life. Poor self concept creates low self esteem and

finally leads to low motivation and under achievement. Pupil with low self-esteem exhibits behaviour of a fairly negative kind and unwillingness to accept blame for failure. On the contrary the pupil with positive self concept is socially well adjusted and they are working with confidence and realistic and optimistic about their future. Therefore the author suggests that teachers should pay more attention in this regard (Downey et al., 1986). Hence it is obvious that there is a correlation between personality and the educational performance and proper personality measurement tool could help to project once educational achievements. However, as it involves, conceptual and methodological problems, to make valid measurement of self-esteem is difficult. Conceptual confusion is created as a result of concurrent use of the term in ordinary language and academic psychology and also, due to the lack of common notion of the term, because, the concept of self-esteem goes by variety of names such as self-worth, self-regard, self-acceptance, self-respect and etc. Hence this confusion is required to be minimized, in order to get a clear understanding of the term and to have a common ground for both academic and ordinary usage. Otherwise it would be possible to create miscommunication between the researches and the participant of the research. Methodological problems arises when it comes to make a standardize measure of self esteem basing on the assumption that single measure would accommodate all needs. People like Rosenberg and Gergen give more importance to the global self as key to self-evaluation while Fleming, Shavelson and others concentrate on the facets of self-esteem or the component and sub components which contribute to the global self-esteem. During the course of time many different theoretical approaches to measure self concept is being constructed and number of studies being carried out on the ways to evaluate self-esteem. However, despite the many approaches to evaluate the self-esteem, self reporting has been recognized as an exclusive method of measuring self concept because it is difficult to conceive behavioral or psychological measure that would capture self esteem directly due to the subjective nature of self-esteem. Self reporting measures could be either direct or indirect. An advantage of one over the other is highly debatable. Some favors direct face valid questionnaire which uses items that could be scored higher or less additively while others prefer more indirect measures using complexly scored questionnaire such as self-ideal discrepancy score. However researches prefer the



23

former and at present many prefer simple self reporting measures. The Self-Esteem Scale by Rosenberg was originally designed to measure adolescent’s global feeling of his

or her self worth or self acceptance. The Feelings of Inadequacy Scale by Janis and Field was developed to quantify the feelings a person’s inferiority. Coopersmith proposed The Self-Esteem Inventory to evaluate different attitude pertaining to self. Piers-Harris Children’s Self Esteem Scale is another popular

instrument to measure the self concept of children and adolescents specially relating to their behavior. Self Descriptive Questionnaire (SDQ) proposed by Herbert Mash is hierarchical multifaceted model to measure multidimensional self concept (Robinson et al., 1991). 2.3 Neural Network Approach Neural Network is a prototype model of human brain, which is of a natural remarkable parallel processing computer. As humans do recognize patterns, the neural networks are capable of pattern recognition. This capability is acquired through a process of learning as humans do. For example a small child learns the letter “A” through a process of learning and in the same way

the neural networks also recognize patterns. Raw input data which is collected through a questionnaire, is catagorical and qualitative. They are

also distorted with psychological state of the person at

the time of answering the questionnaire. Hence, to predict a result with such qualitative, categorical and noisy data is really a challenge. Statistical tools and neural network tools could be utilize to process these data to get the expected results. However, according to many researches, neural network has proved to be better than statistical tool when it comes to deal with the data similar to this research. Van Learhoven in his research, Real-time Analysis of Data from Many Sensors with Neural Networks, stressed the fact that neural networks play a better role with tracking records on noisy data than statistical methods or expert systems (Van Laerhoven et al., 2001). Saiful Anwar, who did a research under the title Comparing Accuracy Performance of ANN, MLR, and GARCH Model in Predicting Time Deposit Return of Islamic Bank, agreed that Artificial Neural Networks perform better when it comes to the prediction comparing to traditional statistical approaches (Saiful, n.d.). Comrei who conducted a research on Comparing Neural Networks and Regression Models for Ozone Forecasting presents neural network as a better approach to forecast non linear and complex process as Ozone formation (Comrie, n.d.).

There is another strong reason to use neural network model despite the statistic model. The reason is the nature of the research itself. This research mainly deals with two types of data sources. The data provided by the personality testing instruments function as the input data source while the related examination data provides the targets. The success of the research depends on the accuracy of the creation of correlation between these two data sources. In fact it is not merely analyzing data, which is the main concern of statistic rather it is a statistical inference. In other words, it involves in analyzing the data of the two sources while comparing their correlation (Sucharita n.d.). Hence, it is obvious that the neural network approach take precedence over statistic approach in the scenario of this particular research.

3. METHODOLOGY

3.1 The Data Sample

The research data was gathered from two educational zones namely Puttalam and Chilaw. These zones cover the entire Puttalam district. In other words the Puttlam district is the area chosen for the research. According to the Census of Population and Housing 2001 the entire population in Sri Lanka is 18,797,257 (However in 2009 the total population is 20450000) and the population density is 300(Person per sq. K.M.). The population of Puttalam District is 709,677 and the population density is 246. The male literacy rate in Puttlam district is 90.7 and the female literacy rate is 91.2. The population, aged 5-34 in Puttalam District is 393135. From this population the percentage of school attending is 39.1 and the percentage of attending universities is 0.2 and the percentage of attending vocational training institutes is 0.3. It is noticeable that the percentage of university entrance is very low compared to the other districts because except Badulla and Nuwar Eliya Districts the university entrance is more than 0.3 percent for the rest of the Districts. This is one of the reasons to choose the Puttalam District as the research area and it is also a fair representation of the all ethnic, religious and social groups such as farming and fishing (Statistic, n.d.). Northern part and the north-western of the district comprise of villages where cultivation takes a priority. Wannathawilluwa is famous for cashew and villages like Karuwalagaswawe, Thabbowa are famous for cultivation of green-gram and cawpea. Paddy cultivation is also prominent in these areas. Villages like Kaluwaragaswewa are very often subjected to wild elephant attacks.



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Katpitiya Peninsula, Chilaw beach and practically all the other coastal villages are fishing areas where the livelihood of people is fishing. Families in these coastal villages most probably undermine the value of education as their livelihood is tightly bonded with the sea. Increasing number of school leavers in these areas prove this fact. People of all religions live in the district. Shrine of St. Anne’s at Talawila is famous among Catholics and the Munnesaran Temple in Chilaw is a popular place of worship among Hindu and Buddhists devotees. Many ethnic groups are too found in the district. Sirambiadiya is a village in Puttalam district where Negro people who have an African origin live. In Puttalam District there are newly emerging social issues due to migrant workers. Many people migrate especially to Italy for jobs. Nainamadama, Wennappuwa, Ulhitiyawa and Chilaw are some famous towns and suburbs for this. In the beginning many of them were illegal migrants and they crept to western countries by boats. However, at present they tend to follow legal procedures in migrating to such countries. This has made several social issues and it highly affects the education in those areas. Many of them leave their children with their grandparents or uncle and aunts. Very often these children get little attention for their education, because many of these grandparents are very old and find it difficult to cope up with the present day challenges in education and as a result they do the bare minimum. It is not very much different with the children who are left with their uncles and aunts because they are much worried about their own children and the priority is given to them. Another issue which is coming up with this foreign employment is broken families. There is an increase in divorce cases in these areas. In 2010 there nearly 400 divorce cases were filed at Marwila Primary Courts. According to many researches the divorce effect strongly on children’s education (Woolfolk, 2006).

3.2. Data Gathering Tool

TABLE 3.1- Questionnaire

The questionnaire was designed under the inspiration of Self Descriptive Questionnaire, which is introduced by Herbert Marsh. The Original questionnaire has basically hundred and thirty three questions. However, the data gathering instrument in this research is limited to fifteen questions. One of the reasons to limit the questionnaire to fifteen questions is the time factor. Some principals of the schools selected as samples were reluctant to co-operate the research saying they do not have time for such things. Some students show their unwillingness when they saw the amount of questions that they have to answer. At this point student from my school helped me to compose some reliable instrument without disturbing the core of the original tool. Several questionnaires were tested. Finally, the data-gathering instrument was designed with fifteen questions. However, a new set of questions was introduced to this tool apart from the original. It is an inquiry of the time spends for several activities of a student, which is illustrated in the table 3.2.

01) Others accept me as a handsome/ pretty person___ 02) My health does not disturb my educational activities___ 03) I am a good sportsman/sportswoman___ 04) I like to be reborn as I am again___ 05) I have lots of friends___ 06) My parents and teachers are pleased with my friends___ 07) I am among the best students in the class___ 08) I am among the weak students in the class___ 09) Teachers are pleased with me___ 10) When it comes to responsibility teacher always rely on me___ 11) My siblings help me in my educational activities___ 12) My siblings loves me___ 13) I love my mother more than the father___ 14) I love my father more than the mother___ 15) I am happy___



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TABLE 3.2 Daily routine

Activity Hours Study Hours Helping Parents Sports Watching Entertainment Programms Watching Educational Programms Reading Educational Books Reading Entertaining Books With Friends

The primary idea of introducing a new question is to test the reliability of the instrument. Student asked to answer with the hours spend on each activities. These hours are carefully process to recognize the answer pattern of the student. For example, some students have marked more hours for study and altogether it is more than twenty-four hour. This would hint the system about the reliability of the answers. 3.3 Two approaches

Fig 3.1 Out Line of the First approach

Here, input one is the fifteen questions, which describe the socio psychological state of the student. They are going to the pattern recognition subsystem, which is the neural network through the fuzzy interface. Fuzzy interface tries to generalize the results. Input two is the hours mentioned in the table2. This is directly fed into the neural network assuming that it would help the system to recognize the answering pattern of the student.

Fig.3.2 the Second approach.

In the second approach to the problem, the data from the questionnaire is directly fed to the neural network.The purpose of the second method is to do a comparative study over the importance of fuzzy system for the success of the research and to ensure the validity of the reasoning to use fuzzy system in the research. In the first methods the inputs were between 0 to 1 range and in the case of the second approach first fifteen questions received answers from 1 to 4 and the daily routine section get different answers ranging from 0 to 10. 3.4 MatLab MatLab is an acronym created combining the first three letters in matrix laboratory, which is a mathematical computing environment developed by MathWorks. It allows matrix manipulations, implementation of algorithms, plotting of function, etc. At present it has attracted millions of user across industrial and academic fields. It supports users from various backgrounds such as engineering, science and economics. The implementation of the research is also done through two powerful Toolboxes in MatLab namely Fuzzy Toolbox and Neural Network Toolbox.

4. RESULTS The expected outcome is a system capable of recognizing the co-relation between sociological factors with educational performance and having ability to predict one’s educational performances

basing on his or her social factors. Hence, the system would assist the person concerned to alter the social factors, which are adjustable, or the system would be able to suggest the factors to be change in order to create a healthier social environment which suites for a better performance at one’s educational life

especially at G.C.E. O/L Examination. The research was conducted as follows.

4.1 Results from the Fuzzy and Neural Network Approach



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Under this method both the target and the data of the input layer are fed to the neural network through the fuzzy interface. Under the first method several networks were experimented. The transfer functions and the performance functions utilized in the networks are illustrated in Table4.1.

TABLE 4.I-Network Properties of fuzzy and NNA

Net

wo

rk

Lay

ers

Nu

ero

ne

in

hid

den

lay

er

Tra

inin

g

Fu

nct

ion

Per

form

ance

F

un

ctio

n

Tra

nsf

er

Fu

nct

ion

1 2 10 trainlm mse tansig 2 2 10 trainlm msereg tansig

3 2 10 trainlm sse tansig

4 2 10 trainlm mse logsig

5 2 10 trainlm mse purelin

6 2 10 traingda mse tansig

7 2 10 traincgf mse tansig

8 2 10 traincgb sse tansig

Fig 4.1 Results of Network 1to 8

Figure 4.1 is the outcome of the networks arranged according to the table 4.1. According to the figure 4.1, the network1, which is the default network, is relatively closer to the targets. However, the success

of predicting the results is 30%. Next successful Network is the network 5, in which the success of predicting the results is 25%. Prediction rate of the network success is nearly 10% in the network 6 and approximately 17% in the network 7 while the successful rate is less than 10% in the network8. Hence, the challenge is to find the way to improve this to meet the expectation of the research. . Training the network is tricky, because with the same data set network could produce different results with each new cycle of training. Practically the training of a network is done several times until it reaches the required state. In addition, Neural Network Tool Box in Matlab uses a special technique called ‘early stopping’ in order to improve generalization, because the over training may cause the network to be trained to the extent of targets being merge with the inputs. This situation causes the network incapable of recognizing inputs other than that of the training set. This situation is minimized with the technique of early stopping. These simple facts are an indication that the figure 4.1 is just a snap shot of a single training iteration of network1 to network8. However, it gives a clue regarding the direction through which the experiment should be carried out. These networks from one to eight were tried with different possible combinations of performance transfer and training functions. However, the number of hidden layers and the neurons in each layer was constant during the experiments with the above networks from one to eight. Hence, the experimentation of developing a successful system had to consider the number of layers in the network. The importance of the hidden layers is quite visible with the linearly separable problem. Linearly separable problem is a state where the two classes cannot be separated with a single straight line. However, in many cases the single hidden layer solves this problem. The networks one to eight have a single hidden layer but still it seems to be inadequate. The problem is with the data with which the system is dealing with, because these data are qualitative. It is even worse because they represent a psychological state of a student at the moment of answering the questionnaire. Hence, the classification of such data is really challenging because they are highly none interrelated. Then the experiment was conducted with different number of hidden layers and neurons. Finally the successful rate of prediction could be raised to the percentage of 61.5.



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4.2.1 Network with 61.5% success:

TABLE 4.2-Network Properties of the 61.5% success L

ayer

s

Nu

ero

ne

in h

idd

en

lay

er

Tra

inin

g

Fu

nct

ion

Per

form

ance

Fu

nct

ion

Tra

nsf

er

Fu

nct

ion

3 13 trainrp msereg purelin

Fig 4.2 61.5% successful network

TABLE 4.3-Test Data shown in fig 4.2

61.5% Successful Network

Targ

et

Out

put

Targ

et

Out

put

Targ

et

Out

put

Targ

et

Out

put

0.89

917

0.10

159

0.20

945

0.62

075

0.2

0177

0.10

16

0.2

0522

0.10

163

0.20

522

0.10

159

0.89

917

0.89

916

0.2

6965

0.89

916

0.3

0018

0.89

917

0.20

945

0.10

159

0.20

522

0.10

254

0.2

0522

0.10

159

0.8

9917

0.89

917

0.89

917

0.89

917

0.69

997

0.89

917

0.8

9917

0.32

103

0.8

9917

0.62

275

0.20

522

0.10

159

0.89

917

0.53

982

0.2

0945

0.89

917

0.3

0018

0.10

16

0.20

945

0.10

186

0.20

522

0.10

159

0.2

0522

0.10

159

0.6

9997

0.10

16

0.27

047

0.1

016

0.20

945

0.16

8

0.2

6965

0.10

159

0.6

9997

0.89

821

0.89

917

0.1

0163

0.30

004

0.89

917

0.2

0522

0.89

836

0.8

9917

0.89

912

0.89

917

0.8

9916

0.30

018

0.10

162

0.2

0522

0.10

159

0.8

9917

0.89

917

0.20

522

0.11

099

0.89

917

0.89

916

0.2

0522

0.10

16

0.8

9917

0.89

741

Table 4.3 shows the target and the output data for the network with a 61.5 success of prediction. Here, target is the expected results and output is the actual output of the network. In figure 5.4, ‘0’ to ‘0.2’ area represents the student

category, which is qualified to select Science stream for A/L, and ‘0.2’ to ‘0.4’ represents Mathematics

category. Commerce category is denoted by ‘0.4’

to’0.6’ area while Arts student Category is represented by the range ‘0.6’ to ‘0.8’ and beyond that is of those

who have failed. For the convenience of analyzing the network, both Science and Mathematics categories are merged together and Art and Commerce categories are considered as a single category. According to the test data, the number of students who failed the examination is thirteen out of forty students. The recognition of the failed-students by the network is eight out of the thirteen and seven students out of forty were wrongly identified as failed-students. Hence, concerning the ‘fail’ class, twelve out of forty

is wrongly identify by the network which is 30%. Twenty-four students out of the test data set belong to the Science-Mathematics Category and the trained network recognizes eighteen students out of it. The network has marked six students belong to Science-Mathematics Category as those who are of failed category and one students as is of Art-Commerce Category. Test Data have only three students fit in to the Art-Commerce category and the network failed to



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identify all of them. However, the overall success of the network is nearly 61.5%. The success of this network over the others is perhaps due to the Resilient Backpropagation algorithm. Typically, sigmoid transfer functions are used in the hidden layers in multilayer networks. Sigmoid functions must approach zero as the input gets large. This causes a problem when using steepest descent to train a multilayer network with sigmoid function. The gradient can have a very small magnitude causing small changes in the weights and biases even though the weights and biases are far from their optimal values. This fact is obvious with the predicted results of network 1 to 8. The targets were ranged from 0 to 0.9 and many networks could not reach 0.8 which is the beginning of the failed category. However, this could be resolved with the training algorithm Resilient Backpropagation. Here only the sign of the derivative is used and it is to determine the direction of the weight update. The magnitude of the derivative has no effects on the weight update. The size of the weight and bias update value is determined by a separate update value. According to the sign of the derivative the size is either increased or decreased. If the derivative is zero the update value remains the same. Whenever the weights are oscillating the weight change will be reduced and the magnitude of the weight change will be increased as the weight continues to change in the same direction for several iterations. Hence, the network with the Resilient Backpropagation training algorithm was able to be successful in predicting the result over other networks. 4.3 Results from the Neural network Only Approach For this method, the input data are directly fed to the network bypassing the fuzzy interface. The network is arranged as in the table 4.4

TABLE 4.4-Network Properties of the Neural network Only Approach.

Net

wo

rk

Lay

ers

Nu

ero

ne

in h

idd

en l

ayer

Tra

inin

g

Fu

nct

ion

Per

form

ance

F

un

ctio

n

Tra

nsf

er

Fu

nct

ion

9 2 10 trainlm mse tansig

10 2 10 trainlm msereg tansig

11 2 10 trainlm sse tansig

Fig 4.3 Results of Network,9,10,11

The trained network complied with the Neural network Only Approach is capable of predicting the result up to 17%. This is nearly a 13% of drawback in comparison to the Fuzzy and Neural Network Approach . The other two networks which utilize ‘msereg’ and ‘sse’ as their performance functions are far behind the expected targets. Under the first approach the next stage was to experiment with networks which have different training and transfer functions. However, taking into consideration the results of the default network, the next stage of the experiment bypassed the other steps of the experiment conducted under the Fuzzy and Neural network Approach and continued with the training of 61.5%



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successful network under the Fuzzy and Neural Network Approach with the training data set under the Neural Network Only Approach.

Fig 4.4 61.5% successful network under the Neural Network Only Approach

Although the network identified eleven incidences in the failure category out of fourteen under the Fuzzy and Neural network Approach, in the Neural network Only Approach it has failed to identify a single. Overall success of the network in result prediction is less than 25%. Hence with the predicted results up to the current stage of the experiment carried out under the second approach to the solution stresses the success of the first approach over the second approach. Therefore, the future experiments related to the research would be concentrated on the Fuzzy and Neural network Approach.

5 CONCLUSION AMD FUTURE WORK

The hypothesis of the research was that the sociological factors do affect the educational performance of a student and based on sociological data it is possible to predict one’s educational

performances. The goal of the research was to find the truth of the hypothesis. If it is a truth, to prepare the grounds to develop a tool, which helps to boost the educational counseling process in Sri Lankan Schools. 5.1 Challenges and Problems encountered The success of the system depends on the relevance, quality and the accuracy of the data. This system requires two type of data namely, target and input data. Target data was prepared from the results and the input data was collected from the questionnaire. Hence, the data collection has to be done twice. One was before the examination and the other was after the examination. It has to be done with the same student. In this regards, the cooperation from the school

administration was very important. The research lost considerable amount of data due to the lack of cooperation from some of schools. This situation limited the data set to 199 students and it greatly affected the success of the research. As the target data was prepared from the G.C.E O/L Examination which come once a year, opportunities to collect data is limited. Hence, it provides less space for mistakes. For an example, if an adjustment to be done in the questionnaire after a test with the system and realizing the drawbacks of the questionnaire, it has to be waiting for another year to correct the mistake. 5.2 False-acceptance and False-rejection Here, false-acceptance is the acceptance of a student to ‘pass class’ though the true class is ‘fail’. False-rejection is the opposite of false-acceptance. However, in the context of the purpose of the research, which is to help those who would fail the examination, misidentification of those who would get through the examination as failed-student, is not a big issue because they also get a good attention and would be able to perform even better at the examination. However, false acceptance is a serious issue because they would get a wrong impression about themselves and make their situation worse. However, the misidentification of this nature is five out of forty, which is 12.5 %( table 4.3). As it is shown in the table 4.3 the false rejection is seven out of forty. In the point of view of the ultimate purpose of the research, which is to improve the educational performance at the G.C.E. O/L Examination, network claims a success of 87.5%. However, the overall performance of the network is only 61.5%. Hence, there is a possibility of 38.5% that the system could mislead the counselor in decision-making. 5.3 Achievements Despite various challenges, gathered data was processed through the fuzzy and neural network interfaces. Finally, the neural-fuzzy system was able to trace the correlation between the sociological factors and the performance at the G.C.E. O/L examination to a certain degree. As it is calculated, the successful rate of the research is 65%. Hence, it could be assumed that the hypothesis is a truth to a certain degree and the result prediction is possible if the process is carefully devised. 5.4 Lessons Learnt With the experimental results it is suggested that the improvement of the network is hard and it has come to



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a point practically immovable. Hence, in order to improve the situation it seems that it has to make some changes from the beginning. As the first step the data gathering tool has to be checked. Some changes should be made to evaluate the truthfulness of the student who answers the questionnaire. Output of the questionnaire is the psychological state of the person at the time of the questionnaire given hence it should be able to track the mentality of the person concern. For example if he or she had a bad conversation of her or his mother he may tend to give lover marks to the question related. Perhaps the student may try to hide his or her true self. This could be done in introducing questions appealing to such situations. Another suggestion is to let the student his or her self to give marks instead of limiting their freedom. Because the student can mark only four stages and there could be many states between them. Hence, they could ask to answer with any value between zero and ten and then it could be normalized to a value between zero and one. This would give them more freedom of expressing themselves. Then the research would be carried with the same procedure suggested by the current research. If the input data represent more patterns it would be able to better represent the performance patterns relating to students.

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Rev. P. R. Jayathilake is currently a postgraduate student reading for his MPhil at University of Colombo School of Computing (UCSC), Sri Lanka. His research interests include Analyzing Sociological Data and its Impact on Education based on Student Performance in Examination.

Dr Lakshman Jayaratne - (Ph.D. (UWS), B.Sc.(SL), MACS, MCS(SL), MIEEE) obtained his B.Sc (Hons) in Computer Science from the University of Colombo, Sri Lanka in 1992. He obtained his PhD degree in Information Technology in 2006 from the University of Western Sydney,

Sydney, Australia. He is working as a Senior Lecturer at the University of Colombo, School of Computing (UCSC), University of Colombo. He has wide experience in actively engaging in IT consultancies for public and private sector organizations in Sri Lanka. His research interest includes Multimedia Information Management, Multimedia Databases, Intelligent Human-Web Interaction, Web Information Management and Retrieval, and Web Search Optimization. Also his research interest include Audio Music Monitoring for Radio Broadcasting.



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