📚 Departmental Project and Seminar Proposal Topics with Materials
Agricultural Economics and Extension
Banking and Finance (BF)
Building Technology (BT)
Business Administration and Management (BAM)
Business Education
Cooperative Economics and Management (CEM)
Curriculum Studies
Economics
Education
Electrical Electronics Engineering (EEE)
English Education
English Language
Estate Management (EM)
Food Science and Technology (FT)
Guidance and Counselling
Integrated Science Education
Linguistics and Communication
Maritime and Transport
Marketing (MKT)
Mechanical Engineering
Midwifery
Pharmaceutical Technology / Science
📚 Departmental Project and Seminar Proposal Topics with Materials
Physical and Health Education
Political Science
Public Administration (PA)
Purchasing and Supply (PS)
Science Education
Tourism and Hospitality Management
Urban and Regional Planning (URP)
Vocational Education
Entrepreneurial Skills
👗 Ankara Craft
📿 Bead Making
🎂 Cake Making
📹 CCTV Installation
🧀 Chin-Chin Making
🛫 China Goods Importation
🍩 Doughnut Making
🎋 Hair Braiding Tutorial
🍪 How to Make Eggrolls
🥜 How to Make Peanuts
🎀 How to tie Gele
👄 Make-Up Guide
🥠 Meat-Pie Making
🎨 Paint Making
🕸 Pom-Pom Rug Making
🍵 Soap Making
💼 More Entrepreneurial Skill


R Unknown
Chat
Compose Post Website URL Search Ad. Post Advert
Anonymous
Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network
S

Software Implementation of a System for Predicting Student Performance Using Artificial Neural Network

C.S. Project Software
Reference ID: SD-4943-CS

Software Implementation under Computer Science (CS)

Software Implementation of a System for Predicting Student Performance Using Artificial Neural Network can be acquired by Contacting or Whatsapping Sparklyn Services Software Programmer with the number displayed below 👇

DEDICATION

This research work titled "Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network" is dedicated to God for his enabling grace and to all computer enthusiasts who help to make life a pleasant experience.



i

ACKNOWLEDGEMENT

I owe my indebtedness to my Supervisor (Name of your Supervisor), the Head of Department (Name of your HOD), the Lecturers in the department of Computer Science (CS), Book Authors and Profound Scholars of existing/related research work for your moral support that facilitated the successful completion of my (Tertiary Institution level). I am grateful to God Almighty and my parent for their financial support in my career. I really appreciate you all for everything, Thank you very much.



ii


Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network

TABLE OF CONTENTS

Preliminary Pages

CHAPTER ONE

INTRODUCTION

  • 1.1 Introduction
  • 1.2 Background of Study
  • 1.3 Statement of Problem
  • 1.4 Aim and Objectives of the Study
  • 1.5 Significance of Study
  • 1.6 Scope of Study
  • 1.7 Limitations of the Study
  • 1.8 Definition of Terms

CHAPTER TWO

LITERATURE REVIEW

  • 2.1 Introduction
  • 2.2 Conceptual Review
  • 2.3 Theoretical Framework
  • 2.3.1 Overview of Artificial Neural Network
  • 2.3.2 ANN Database Query system
  • 2.3.3 Concept of ANN System Student Registration
  • 2.3.3.1 Online ANN System Student Registration
  • 2.3.3.2 Usage of Online Registration Forms
  • 2.4 Empirical Review of Related Literature

CHAPTER THREE

SYSTEM ANALYSIS AND DESIGN

  • 3.1 Methodology Adopted
  • 3.1.1 Problem Identification Using SSADM
  • 3.2 Analysis of the Existing System
  • 3.2.1 Dataflow of the Existing System
  • 3.2.2 Disadvantages Of The Existing System
  • 3.2.3 Weakness of the existing System
  • 3.3 Feasibility Study
  • 3.3.1 Economic Feasibility
  • 3.3.2 Technical Feasibility
  • 3.3.3 Operational Feasibility
  • 3.4 Analysis of the Proposed System
  • 3.4.1 Data Flow Diagram of the Proposed System
  • 3.4.2 Advantages of the Proposed System
  • 3.4.3 Justification of the Proposed System
  • 3.5 Functional Requirements
  • 3.5.1 Use Case Diagram Of The Admin / User Privileges
  • 3.6 Data Requirements
  • 3.7 High Level Model of the Proposed System

CHAPTER FOUR

SYSTEM DESIGN AND IMPLEMENTATION

  • 4.1 Objectives of the Design
  • 4.2 Cohesion and Decomposition High level Model
  • 4.3 Control Center / Overall Dataflow Diagram
  • 4.3.1 Proposed System Operation Flowchart
  • 4.4 System Specification and Design
  • 4.4.1 Input and Output Specification
  • 4.4.2 Database Specification and Design
  • 4.4.3 Data Dictionary
  • 4.5 Choice and Justification of Programming Language
  • 4.6 Program Documentation
  • 4.7 Implementation Techniques
  • 4.8 Programming Module Specification
  • 4.8.1 Installation
  • 4.8.2 Security Design Specification
  • 4.8.3 System Architecture
  • 4.9 Computer Hardware Minimum Requirement
  • 4.10 Software Requirement
  • 4.11 Personnel / User Training

CHAPTER FIVE

SUMMARY, CONCLUSION, AND RECOMMENDATION

  • 5.1 Introduction
  • 5.2 Summary
  • 5.3 Conclusion
  • 5.4 Recommendation

REFERENCES

APPENDIX A - “SOURCE CODE”

APPENDIX B - “OBJECT PROGRAM”




iii

ABSTRACT

An artificial neural network imitates the human brain in problem solving, is a more general approach that can handle this type of problem. The aim of the study is to design and implement a system for predicting student performance using artificial neural network. In achieving this aim, the following specific objectives were laid out as follows to develop a artificial neural network system software that will create easy and friendly user interface ANNs which will allow fast operation and analysis of student output, determine some suitable factors that affect a student’s performance and model an artificial neural network that can be used to predict a candidate’s performance based on given pre requirement data given to it. The motivation that led to the implementation of the proposed system is as a result of poor academic performance of some Nigerian students (tertiary and secondary). In recent times has been partly traced to inadequacies of the National University Admission Examination System and it has become obvious that the present process is not adequate for selecting potentially good students. The methodology adopted in this study is the structured system analysis and design methodology (SSADM) which is a technical approach for analyzing and designing an application or system by applying object throughout the software development process. The programming language used is HTML, CSS, JAVASCRIPT, PHP, SQL and JQUERY. The reason why web programming languages was used is because, it is platform independent and it is a web based application. This research work will be of benefit to lecturers, academic students and tertiary institutions. The expected result is a computerized artificial neural network system that will predict student performance and enhance the admission process of institution in terms of admitting the right student into the institution.





iv


Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network

CHAPTER ONE

1.1 Introduction

An artificial neural network imitates the human brain in problem solving, is a more general approach that can handle this type of problem. Hence, our attempt to build an adaptive system such as the Artificial Neural Network to predict the performance of a candidate based on the effect of these factors. The results of this prediction can also be used by instructors to specify the most suitable teaching actions for each group of students, and provide them with further assistance tailored to their needs. In addition, the prediction results may help students develop a good understanding of how well or how poorly they would perform, and then develop a suitable learning strategy. Accurate prediction of student achievement is one way to enhance the quality of education and provide better educational services (Romero and Ventura, 2007). Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques.

As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitation of the study and Definition of technical terms.

1.2 Background of Study

In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected “neurons” which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network’s designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

The artificial neural network (ANN), a soft computing technique, has been successfully applied in different fields of science, such as pattern recognition, fault diagnosis, forecasting and prediction. However, as far as we are aware, not much research on predicting student academic performance takes advantage of artificial neural network. Kanakana and Olanrewaju (2001) utilized a multilayer perception neural network to predict student performance. They used the average point scores of grade 12 students as inputs and the first year college results as output. The research showed that an artificial neural network based model is able to predict student performance in the first semester with high accuracy. A multiple feed-forward neural network was proposed to predict the students’ final achievement and to classify them into two groups. In their work, a student achievement prediction method was applied to a 10-week course. The results showed that accurate prediction is possible at an early stage, and more specifically at the third week of the 10-week course.

Advising students on their class performance and motivating them in order to improve on their performance is an integral part of every instruction. The mechanisms to achieve the above aim required a technique capable of accurately predicting student achievement as early as possible and cluster them for better academic assistance. According to Lykourentzou et al, (2009), student-achievement prediction can help identify the weak learners and properly assist them to cope with their academic pursuit. Several methods and systems have been developed for the above task, most of which are artificial intelligence-based.

For instance, Lykourentzou et al., (2009) estimated the final grades of students in e-learning courses with multiple feed-forward neural networks using multiple-choice test data of students of National Technical University of Athens, Greece as input. The results obtained shows that ANN is 91.2% efficient. Junemann, Lagos, and Arriagada (2007) used neural networks to predict future student schooling performance based on students’ family, social, and wealth characteristics. The aforementioned work focused on predicting the achievement of 15-year-old secondary students on reading, mathematics and science subjects in Berlin.

In the Nigeria context, Oladokun, Adebanjo & Charles-Owaba (2008) applied multilayer perception neural network for predicting the likely performance of candidates being considered for admission into Engineering Course of the University of Ibadan using various influencing factors such as ordinary level subjects’ scores, matriculation exam scores, age on admission, parental background etc., as input variables. The results showed that ANN model is able to correctly predict the performance of more than 70% of prospective students.

However, Abass et al., (2011) applied another technique of Artificial Intelligence (AI) i.e., case-base reasoning (CBR) to predict student academic performance based on the previous datasets using 20 students in the Department of Computer Science, TASUED as the study domain. The high correlation coefficient observed between the actual graduating CGPA and the CBR predicted ones also justify the usefulness and effectiveness of AI techniques in this type of task.

In this research work, Artificial Neural Network is used to estimate students’ final grade in the university with a prediction level of 92%.

Intuitively one expects the performance of a student to be a function of some number of factors (parameters) relating to the background and intelligence of said student. It is however obvious that it will be quite difficult finding an analytical (or a mathematical) model that may acceptably model this performance/factors relationship. However one practical approach for predicting the performance of a student may be by ‘extrapolating’ from historical data of past students’ background and their associated performances.

The drawback here is the difficulty of selecting an appropriate function capable of capturing all forms of data relationships as well as automatically modifying output in case of additional information, because the performance of a candidate is influenced by a number of factors, and this influence/relationship is not likely going to be any simple known regression model.

1.3 Statement of Problem

Investigation revealed that poor academic performance of some Nigerian students (tertiary and secondary) in recent times has been partly traced to inadequacies of the National University Admission Examination System. It has become obvious that the present process is not adequate for selecting potentially good students. Hence there is the need to improve on the sophistication of the entire system in order to preserve the high integrity and quality. It should be noted that this feeling of uneasiness of stakeholders about the traditional admission system, which is not peculiar to Nigeria, has been an age long and global problem.

Looking into the institution this days, you will discover that 48% of the student are actually performing very low on their academic level, whom if asked to defend his admission status cannot (i.e. sitting for the attitude test), when proper investigation is carried out, findings shows that most of them have their way into the school through bribe or the so called upper hand. Also another issue or problem for this research work is that some of the applied candidates, some are actually sound and capable of performing well when admitted, but because of some factors at the moment or surrounding the student, prevent the student from obtaining or securing his admission into the school.

Hence this study takes an scientific approach to tackling the problem of admissions by seeking ways to make the process more effective and efficient. Specifically the study seeks to explore the possibility of using an Artificial Neural Network model to predict the performance of a student before admitting the student.

1.4 Aim and Objectives of the Study

The aim of the study is to design and implement a system for predicting student performance using artificial neural network. In achieving this aim, the following specific objectives were laid out as follows to develop a artificial neural network system software that will:

  1. Enhance the admission process of this institution in terms of admitting the right student into the institution.
  2. Create easy and friendly user interface ANNs which will allow fast operation and analysis of student output.
  3. Be capable of predicting student performance.
  4. Determine some suitable factors that affect a student’s performance.
  5. Model an artificial neural network that can be used to predict a candidate’s performance based on given pre requirement data given to it.

1.5 Significance of Study

This study will be of immense benefit to researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.

1.6 Scope of Study

The scope of the research is focused on the design and implementation of a system for predicting student performance using artificial neural network.

1.7 Limitations of the Study

During the course of this study, many things militated against its completion, some of which are:

  1. Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
  2. Research material: availability of research material is a major setback to the scope of the study.
  3. Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
  4. Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).

1.8 Definition of Terms

ANN: ANN is an acronym for Artificial Neural Network imitates the human brain in problem solving, is a more general approach that can handle this type of problem.

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

This chapter focuses on the review of related literature. A literature review includes the current knowledge as well as theoretical and methodological contributions to a particular topic. It documents the state of the art with respect to the topic you are writing. It surveys the literature in the topic selected. In this research work the literature review includes the …

Complete Material Chapters of Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network

Order Complete Material with Preferred Acquisition Method

Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network Complete Material can be acquired by placing an order for the material which will be sent in Microsoft Word (MS-Word) Format and the cost of acquisition is ₦3,000.

For Mobile Money (MoMo) and Researchers Outside Nigeria, Kindly Request Complete Material via WhatsApp.


METHOD #1

Request Complete Material

Complete Material Chapters of Design and Implementation of a System for Predicting Student Performance Using Artificial Neural NetworkClick here to request the Complete Material via WhatsApp including;
  • Preliminary Pages, Chapter 1-5, References, and Appendix.


METHOD #2

Account Details - For USSD / POS Transfer

Details


Account Name: Sparklyn Services
Account No: 1222599051
Account Type: Current
Bank Name: Zenith Bank PLC

After transaction, kindly inform Us with the contact details above.


METHOD #3

Sparklyn Services, duly registered with the Corporate Affairs Commission (CAC) under the Federal Law with RC: 2994849 operates on Secure Sockets Layer (SSL), therefore all transactions on this site is secured and safe!

Order Complete Material with Card
Full Name
Phone Number
Email Address
Research Topic

Secured by Paystack

Disclaimer for Computer Science (CS) Research Material

The displayed research work titled "Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network" is stated as a research guideline towards accomplishing your assigned project / seminar research work. All the research materials on this website are ONLY for research purposes and should be used as a guideline in developing your research work. For no reason should you copy word for word as Sparklyn Services (sparklyn.com.ng) will not be liable for any who copied the material.
By ordering the complete research guideline, it signifies that you've accepted our terms of service.

Proposal Writing Format for Computer Science (CS) Research Work

Final year research work is all about finding real life problem and proffering solution that will partially or totally eliminate the existing system bottlenecks. The following are the major and elective project proposal writing sections for "Design and implementation of a system for predicting student performance using artificial neural network" research work;

Major Sections
  • Motivation for Embarking on the Project

  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

Elective Sections
  • Methodology and Reason for Using It (such as; models, SSADM, or OOADM)

  • Tools (programming languages and software used)



Didn't find your Preferred Topic? Perform an Instant Topic Search

Your preferred topic wasn't listed? Click here to view more Computer Science proposal topics

Proposal Writing Format for Computer Science Research Work


Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network is a proposal topic for final year research work, which comprises the major and elective project proposal writing sections for Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network research work.

Major Sections
  • Motivation for Embarking on the Project

  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

Elective Sections
  • Methodology and Reason for Using It (such as; models, SSADM, or OOADM)

  • Tools (programming languages and software used)


Defense Procedure for Computer Science Researchers


Know your Project / Seminar Work (Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network): Here are the key point to study if your work is cumbersome or not.

  • Abstract

CHAPTER ONE

  • Motivation / Statement of Problems
  • Aims & Objective of Study
  • Scope of Study
  • Significance of Study

CHAPTER TWO

  • State two or more citation from your review of related literature.

CHAPTER THREE

  • Know the methodologies, tools and techniques used.

CHAPTER FOUR

  • Justification of your work and things to adhered to before using the system or research work.

CHAPTER FIVE

  • Conclusion and Recommendation

Dress Code: Your dress code should be cooperate wear for example; putting on suit and tie during project defense gives you an automatic mark without a word.

External Examiner / Supervisor Questioning & Student Answering: Questions will come from the research work, any difficult or unknown question, kindly say "Sorry Sir/Madam, the question is not within my scope of study".


Summary Headlines for Design and Implementation of a System for Predicting Student Performance Using Artificial Neural Network




    NEED HELP? CALL US 24/7:
    +234 803 051 1988