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Full Project – Design and implementation of students academic performance evaluation system with fuzzy logic

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CHAPTER ONE

INTRODUCTION

1.0 BACKGROUND OF THE STUDY

Fuzzy logic was propounded by Professor Lofti A. Zadeh at the University of California at Berkeley as result of the principal aim of database technology which provides common data for all users to share while avoiding extra effort of keeping duplicated files. The database however deals mostly with descriptive aspect of data. Consideration of these aspects would lead to production of human-like injection computing system lead to embed more human-like Intelligent system into the computing system lead to research work called “FUZZY LOGIC” The rule of logic used in computer query language are all based on exact Ideas of truth and falsehood which implies that all objects in a database fit the same patterns or paradigm.

Conventional logic sometimes leads to unsolvable paradoxes, in natural language, we have a full freedom to add, modify, divide up the middle ground between potentially overlapping classes and any difficult situation can be described locally.

Naturally, language deals with fuzzy classes by definition and class

splitting ( in fuzzy sets, the grade of membership is expressed in terms of scale that can vary in contribution between 0 and l).

Fuzzy logic is a type of expression characterizing classes that for various reasons cannot have or do not have sharply defined boundaries. These inappropriate definitions of class boundary are called “fuzzy sets?

Fuzzy can be viewed as a particular form of vagueness that provides sufficient information for deciding fuzzy proposition or that explained fuzzy propositions.

Fuzziness is not a probabilistic attribute in which the degree of membership of a set is linked to a given statistically defined portability function, rather, it is an admission of possibility that an individual is a member of a set or that a given statement is true. But the assessment of the possibility can be based on subjective, intuitive knowledge or preferences but it could also be related to clearly defined uncertainties that have a basis in probability theory. For example, uncertainty in class allocation could be linked to the possibility of measurement of errors of a certain magnitude.

Hence, fuzzy logic is applicable where there arc ambiguities. Vague

Concepts conflicts. It can resolve conflicts by collaboration, propagation and aggregation but cannot be applied where there are no human expert who can dissolve rules about data relationship ( it cannot do without expert system ) that originate from artificial intelligent and it is rightly abstract and heuristics.

1.1 STATEMENT OF PROBLEM

This analysis of the problem beginning with a description of the students performance in remedial science examinations by considering the

fuzziness students score database.

The maximum score for any student taking a remedial science examination is 500 ( five hundred) marks, hence, by applying the system of the students score database with respect to fuzzy logic, there is redefinition and class splitting such that the grade of membership or a boundary of students with certain range or marks is expressed in terms of a scale that can vary continuously between 0 and 1 where 0 represent the lowest marks or point obtained and 1 represent the highest marks or point which is 500.

The splitting of the marks and above will be rated as “EXCELLENT”, student having 300 marks and above will be rated as “VERY GOOD”, students having 200 marks and above will be rated as “GOOD” students having 100 marks and above will be rated as “FAIRLY GOOD”, and students having marks less than 100 will be rated as “VERRY POOR”etc as it ranges between 0 and 1 in that order.

1.2 AIM AND OBJECTIVE

AIM: to use fuzziness student’s scores database in computing students’ records in examination and evaluating their performance by applying the design of the fuzzy logic system.

OBJECTIVE: l. To implement a fuzzy logic system which can report the rate of failure or reversal which shows the weakness of students in certain courses when a Number of sets of remedial science students examination performance are compared. Examination and students performance results report how excellently a particular has performed when compared with some other sets. Hence, revealing the variability of students’ performance in Remedial examination for several couples of years compared together, thereby showing the trend of improvement in students performance or Otherwise as the case may be for different set of years considered.

To assist the remedial board management in addressing the issue of mass failure experienced by remedial students probably as a result of lack of Competent lecturers in certain courses being taken by these students, so that efficient administrative planning towards improving the students performance can be made probably by replacement of incompetent Lecturers and provision of study logic.

3 To make recommendations based on the finding of the study.

I.3 SIGNIFICANCE OF THE STUDY

The application of fuzzy logic system to evaluate the performance of the Remedial student is very important. This enhances the efforts of the lecturers of the Remedial students which will in turn one, two-three years set of students performances are compared features that are common to all the sets will be considered and this will affect a new administrative decision depending on the result of evaluation made. For example, if fuzzy logic system reveals that for the past 3 years, record of student’s performance in chem001 has the highest grade point of 70% then the administrative oofficers will be able to realize the cause and reason for mass failure of the students by evaluating the lecturers in charge of chem001 course

Therefore, it is very useful for the administrative officers to choose the

best cost-effective capacity planning which will reduce the alarming rate of failure of students in remedial science and make provision for a better Standard. It is real-time system which can reveal trend of performance of remedial students whose result allow the administrative officers to make a lasting and effective decisions or plan towards optimizing the performance of the students in various courses in the remedial science programme. The environment in which leaning takes place also contributes to student failure. This is because a situation whereby the administration of the school could not be able to provide enough practical equipments for students to use during the practical class will affect majority of students in that practical course which may lead to their failure. Hence, the administrative officers should carry out their own task to prevent students from failing as a result of lack of the practical equipment.

Students on their own contributes to their failures, some of the students

felt reluctant to their studies since they are newly introduced to the school

Environment hoping that their being in the school alone will make them to pass and this lead some of them to be attending parties and other unnecessary gathering thereby forgetting their main purpose of being in the school which is studying. The students are therefore informed to be with their books always to enable them pass all their courses.

I.4 LIMITATION OF THE STUDY

It is necessary to inform the readers that this project is not extensive, in that,

we are limiting it to remedial science programme in Kogi State University,

Anyigba.

1.5 METHODOLOGY

Record of past remedial students through the remedial board secretary
Questioning techniques
Using the design tools (flow-chart) to model the system
The use of object-oriented programming language (C#).

1.6 Definition of Terms

The following technical and operational terms will be encountered within this project and brief description of each of them is done at this point.

Fuzzy: It is having a frizzy texture or appearance that is very difficult

to perceive; indistinct or vague.

LOGIC: It is a system or set of principles underlying the

arrangement of elements in a computer or electronic device so as to perform a specific task. It is also the science of hoe to evaluate arguments and reasoning.

PERFORMANCE: It is the accomplishment of a given task

measured against preset known standards of accuracy, completeness, cost, and speed. It also a completion of task with application of knowledge, skills and abilities.

EVALUATION: It is a systematic determination of a subject’s merit,

worth and significance, using criteria governed by a set of standards. It is also the process of determining to what extent the educational objectives are actually being realized.

NEURAL NETWORK: It is a series of algorithms that endeavors to

recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

ARTIFICIAL INTELLIGENCE (AI): it is an area of computer

science that emphasizes the creation of intelligence machines that work and react like humans.

DEFUZZIFICATION: Is the process of producing a quantifiable

result in crisp logic, giving fuzzy sets and corresponding membership degrees.

1.7 ORGANIZATION OF THE PROJECT

Chapter one of the project consists of the introduction of fuzzy logic

System, statement of the problem, aim and objectives, significance of the

Study, scope of the study, limitation of the study, methodology and Organization of the project.

Chapter two consists of the fuzzy logic, a detailed explanation of the

applications and the remedial students’ performance variability constraints or causes.

Chapter three consists of the design and implementation of the fuzzy

logic system on students’ performance in remedial examination while comparing 2-3 different past set records of students Performance.

Chapter four consists of system implementation and documentation

And a detailed explanation on how the students will be admitted into the

Remedial programme, to supply the student result, evaluation of the

Performance of the students for different session and the choice of the

Software applied.

Chapter five consists of conclusion and recommendation

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