Data Mining Application For Determining Student’s Academic Performance

Project and Seminar Material for Computer Science and Computer Engineering

Data Mining Application For Determining Student’s Academic Performance


The project mainly focused on developing an application for information extract or retrieval from pool of data (i.e. a large database) to form basis for decision making. Information extracted from the database in the course of data mining process can be presented in graphical format in form of graphs patterns, histogram, etc. and also in text format. The reason for suggesting the project is the need for employing computer software medium for sanitizing academic standard through computer based decision making. Data mining package can present clear reasons and factor that affects students’ performance and hence allow administrators to derive strategic means of tackling such issues. The package will be developed in a .net integrated development environment (.net IDE). The package IDE is chosen following the fact that extracted information needs to be presented in an enhanced pictorial/graphical format and easy communication with the database for program flexibility in windows platform.

Chapter One

General Introduction

1.1 Introduction

Data mining is a branch of computer science which deals with the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. (Clifton, 2010)

The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations. (Clifton, 2010)

Performance monitoring involves assessments which serve a vital role in providing information that is geared towards helping students, teachers, administrators, and policy makers to take decisions.(Counsil, 2001) The changing factors in contemporary education has led to the quest to effectively and efficiently monitor students’ performance in educational institutions, which is now moving away from the traditional measurement and evaluation techniques to the use of Data Mining Techniques which employ various intrusive data penetration and investigation methods to isolate vital implicit or hidden information. Due to the fact that several new technologies have contributed and generated huge explicit knowledge, causing implicit knowledge to be unobserved and stacked away within huge amounts of data. The main attribute of data mining is that it subsumes Knowledge Discovery which according to Frawley (1991) is a nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data processes, thereby contributing to predicting trends of outcomes by profiling performance attributes that supports effective decisions making. This project deploys theory and practice of data mining as it relates to students’ performance and monitoring program in Kwara State Polytechnic, Ilorin.

Technological developments and new programming techniques have improved understanding and use of Artificial Intelligence (AI). The isolation of hidden data and exposed relationships embedded within it, without a prior knowledge of the nature of any inherent relationship leading [Rubenking 2001] to assert that data mining is a logical evolution of database technology with the development of enhanced query tools such as SQL, database managers are capable querying data more flexibly. Rules derived from various algorithms during the implementation of Data Mining Tools in researches, support this opinion.

Recently educational institutions target activities within its organizations with computer-based tools to handle and store huge data available in educational processes for hidden patterns. The face value assessment of students at the point of entry can only be confirmed or dispelled by the dynamic follow-up monitoring of students’ performance during the course of study leading to serve as an indicator of the suitability and unsuitability of students before admission and during their course of study.

Fuzzy Set Theory is used in applications involving educational assessment and performance as it is regarded as efficient and effective in uncertain situations involving performance assessment. It is known that Expert Fuzzy scoring systems noted [Nolan 1998]; help teachers make assessment in less time and with a level of accuracy that compares favorably to the best teacher examiner. The package will be developed using dot net frame work(c#) crumple with mysql database. Graphics will be use in this project work to give a quick view of the level of performance of student fetching record from the database.

1.2 Statements of the Problem

The ideal goal of higher education is to continually maintain sustainable increasing graduation rates and growth with the most efficient procedures that allows for the accounting of input resources. The degree of quality students’ involves the pertinent issue of how to enhance and evaluate it through overt and covert processes. Hence, Data Mining processes for knowledge is the data which while dependent on quality, characteristics and preparation, supports and facilitates the thorough examination of the data’s different aspects for knowledge discovery in tertiary processes. The result helps Kwara State Polytechnic, Ilorin to predict the degree of likelihood of a student’s persistence, learning outcomes in terms of performance and by using computer-based evaluation tools, meaningful learning outcome topologies are created using charts and graphical representations. Other studies have shown that some techniques are particularly beneficial for the various sub process.

1.3 Aim and Objectives of the Study

The aim of this project is to design a computer-based application that summarizes all the qualities of assessment and performance monitoring of students’ which when expanded holds key information that answers questions on students’ academic performances. The objectives are as follow:

  1. To observe and compare individual, segmented and well aggregated students’ performance variables by analyzing the whole student base activities and then building one predictive model.
  2. To provide a continuous “Just-In-Time” student performance assessment model for predicting performance with reasonable degree of accuracy, thereby enhancing monitoring of student academic pursuance and any other stakeholder’ interests, at any point, for any student during the student’s tenure at the educational institution.
  3. To develop computer-based modeling process that will be effective and integrate all the data objects and rules needed for performance prediction allowing for quality control in the institution, using .netime.

1.4 Significance of the Study

Data mining is a system of searching through large amounts of data. It is a relatively new concept which is directly related to computer science. Despite this, it can be used with a number of older computer techniques such as statistics.

There are a number of software products that have been designed for those who wish to use data mining techniques. Once you are able to search through large amounts of information, you will be able to analyze it in a large number of different ways. Once you’ve analyzed the information, you can make conclusions and decisions which are based on logic. While the term data mining is a new concept, the concept of searching through data for patterns is not. Many large institutions have powerful computers that allow them to search through information to analyze reports over a given period of time.

What sets data mining apart from these older research methods is that data mining is a result of the advancement of computer processing power. In addition to this, the storage capabilities of contemporary computers have allowed data mining to be much more accurate than techniques that were used in the past. Because most data mining tools come in the form of software, the costs involved with searching and analyzing information have greatly dropped.

1.5 Scope and Limitations of the Study

Data mining in academics needs large volume of data which analytical conclusions can be drawn from. This project work focused on developing an application that will take live data of students’ GP per semester and store it in a database. Analysis of Students’ Academic Performance Monitoring and Evaluation can be drawn from the stored results. Reports of the performance evaluation can be extracted and categorized by users’ choice; per semester, session evaluation of students’ performance grouped by department, institute or the entire polytechnic as a whole.

The proposed system does not take account of evaluating any factor affecting students’ performance. The system as well is not target at computing students’ result or function as record keeping software for students in the institution. Due to difficulties foreseen in covering the entire polytechnic as a case study for the research, the research coverage is limited to Institute of Basic and Applied Sciences (IBAS).

1.6 Organisation of the Report

This research work provides efficient way of handling importation and exportation operation job and sheds more light on how to design software for it. The project consists of five chapters. The preliminaries contain the title page, table of contents and abstract.

Chapter one contains the introduction of the study, statement of the research problem, aims and objectives of the study, significance of the study, and the organization of the report.

Chapter two contains the literature review on data mining and its implementation in academic and students’ performance analysis. It also discusses issues related to data mining and it is used for academic performance in higher institutions.

Chapter three contain analysis of the existing and proposed system, which entails method employed in gathering facts, analysis and problems of the existing system, its contain the description of the current system, problems of the existing system, Description of the proposed system, Advantage of the proposed system , Disadvantage of the proposed system, implementation techniques and choice of programming language.

Chapter four is basically contains Design implement and Documentation of the system. It contain output design, input design, file design, procedure design, contain implementation technique, programming language, Hardware and Software, it contains document of the system.

Chapter five contains the summary, Experience, problem encountered, Recommendation and conclusion.

1.7 Definition of Terms


SQL, often referred to as Structured Query Language, is a database computer declarative language designed for managing data in relational database management systems (RDBMS), and originally based upon relational algebra and topple relational calculus. [http://en.wikipedia.or/wiki/SQL]

Data Mining:

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both.

Decision Trees:

The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes.

Co Linearity:

A set of points is collinear (also co-linear or colinear) if they lie on a single straight line or a projective line (for example, projective line over any field). [http://en.wikipedia.or/wiki/collinearitydata_modelling]

Data Modeling:

Data modeling is a method used to define and analyze data requirements needed to support the business processes of an organization. The data requirements are recorded as a conceptual data model with associated data definitions.

Chapter Five

Summary, Conclusion and Recommendations

5.1 Summary

Performance monitoring involves assessments which serve a vital role in providing information that is geared to help students, teachers, administrators, and policy makers to take decisions (Council, 2001) The changing factors in contemporary education has led to the quest to effectively and efficiently monitor student performance in educational institutions, which is now moving away from the traditional measurement and evaluation techniques to the use of data mining techniques which employs various intrusive data penetration and investigation methods to isolate vital implicit or hidden information.

Assessment as a dynamic process produces data that reasonable conclusions are derived by stakeholders for decision making that expectedly impact on students’ learning outcomes. The data mining methodology while extracting useful, valid patterns from higher education database environment contribute to proactively ensuring students maximize their academic output. This project has developed a methodology by the derivation of performance prediction indicators to deploying a simple student performance assessment and monitoring system within a teaching and learning environment by mainly focusing on performance monitoring of students’ examination scores in order to predict their final achievement status upon graduation. Based on various data mining techniques and the application of machine learning processes, rules are derived that enable the classification of students in their predicted classes. The deployment of the computer-based solution, integrates measuring, ‘recycling’ and reporting procedures in the new system to optimize prediction accuracy.

5.2 Experience Gained and Problems Encountered

The research work has helped to improve my knowledge in several ways. These include understanding the basic concept of data mining and how it is applicable in different fields of study such as academic, medicine, engineering, etc.

I gain more experience in how important Microsoft visual basic is in application development and this project make me believe that computer programming is just far beyond computing sum and average of numbers.

Surfing the internet for related literatures on data mining reveals many ideas that are (since these days) implicit to me. I realize how complicated data mining techniques are, its role in the academic settings and how it can be used to enhance administrative performance through effective decision making.

It is quite impossible for someone to go through a research work individually or collective without encountering some problems along side. The problems encountered ranges from limited resources to time constraint.

Data mining is a very wide aspect to cover within the stipulated time and gathering resources for the project initially looks difficult but thanks to WWW consortium that has made the internet a knowledge world bank for everybody where you can borrow without collaterals.

5.3 Recommendations for Future Work

The encouraging results obtained on application of knowledge discovery, begs for a comprehensive strategic implementation, an integration of the results of other research efforts in areas such as Instructor assessment and performance, curriculum, course relevance, student attitude, demographics, etc and its impact on the student learning process must be determined and integrated into any prototype. Learning process must be determined and integrated into any prototype performance, course relevance, student attitude, demographics, etc and its impact on the student learning process must be evaluated and integrated into any future performance monitoring prototype. Data Mining Tools has a potential in performance monitoring of High school and other levels education offering historical perspectives of students’ performances. The results may both complement and supplement tertiary education performance monitoring and assessment implementations.

5.4 Conclusion

The result of this project indicates that Data Mining Tools capabilities provided effective monitoring tools for student academic performance with overall 94% success rating and fine tuning derived variables improves rules quality producing improved performance.

The various reporting tools that this system offers serve mainly to compare changes over time in performances as may be affected by the different rules that are available plus other well chosen variables exposes systematic structures required to improve performance monitoring. Computer-based implementation with dynamic reporting capabilities and efficiency is perceived as better solution and recommended for very large student databases in Oracle or MS SQL Server database environment

Data Mining Application For Determining Student’s Academic Performance

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Data Mining Application For Determining Student’s Academic Performance


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