Data Mining Application for Determining Students’ Academic Performance.
ABSTRACT
The project mainly focused on developing an application for information 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 (dot 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.
TABLE OF CONTENTS
Title page
Certification
Dedication
Acknowledgment
Abstract
Table of Contents
CHAPTER ONE: GENERAL INTRODUCTION
1.1 Introduction
1.2 Statement of the problem
1.3 Aims and objectives
1.4 Significance of the study
1.5 Scope and limitations
1.6 Organisation of report
1.7 Definition of terms/acronyms
CHAPTER TWO: LITERATURE REVIEW
2.4 Data mining in higher education
2.0 Review of general text
2.1 Research and evolution of data mining
2.2 Data Mining process
2.3 Academic analytics
2.4 Data Mining in higher education
CHAPTER THREE: PROJECT METHODOLOGY
3.1 Methods of data collection
3.2 Description of the existing system
3.3 Problems of the existing system
3.4 Description of the proposed system
3.5 Advantages of the proposed system
3.6 Design and implementation methodologies
CHAPTER FOUR: DESIGN, IMPLEMENTATION AND DOCUMENTATION OF THE SYSTEM
4.1 Design of the system
4.1.1 Output design
4.1.2 Input Design
4.1.3 Database design
4.1.4 Procedure Design
4.2 Implementation of the system
4.2.1 Hardware Support
4.2.2 Software support
4.3 Documentation of the system
4.3.1 Operating the system
4.3.2 Maintaining the system
CHAPTER FIVE: SUMMARY AND CONCLUSION
5.1 Summary
5.2 Conclusions
5.3 Recommendation
REFERENCES
APPENDICES
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]
REFERENCES
Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena (2010). PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreateSpace.
Arnold K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33.
Black E. W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. Internet and Higher Education.
Campbell J. P. (2007). Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. Unpublished doctoral dissertation, Purdue University.
Campbell J. P., DeBlois P. B., & Oblinger D. G. (2007). Academic analytics: A new tool for a new era. Educause Review.
Castro F., Vellido A., Nebot A., & Mugica F. (2007). Applying data mining techniques to e-learning problems. Studies in Computational Intelligence.
Cook C. E., Wright M. and O’Neal C. (2007). Action research for instructional improvement: Using data to enhance student learning at your institution. To Improve the Academy.
Clifton Christopher (2010). “Encyclopedia Britannica: Definition of Data Mining”. http://www.britannica.com/EBchecked/topic/1056150/data-mining.
Council N. (2001). “Knowing What Student Knows. The Science and Design of Educational Assessment”. National Academic Press. Washington, D.C. 2001
Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA.
Emmanuel N. Ogor, 1991, “Leaving Early: Undergraduate Non-completion in Higher Education”. Philadelphia: Palmer Press. 1991
Fayyad Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). “From Data Mining to Knowledge Discovery in Databases”. http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf.
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