Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(2.14MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Automated Data Integration, Cleaning and Analysis Using Data Mining and SPSS Tool For Technical School in Malaysia
Tajul Rosli Razak, Abdul Hapes Mohammed, Noorfaizalfarid Hj Mohd Noor, Muhamad Arif Hashim
Pages - 211 - 225     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Data Integration, Data Cleaning, Data Analysis, Decision Support
ABSTRACT
Students’ performance plays major role in determining the quality of our education system. Sijil Pelajaran Malaysia (SPM) is a public examination compulsory to be taken by Form 5 students in Malaysia. The performance gap is not only a school and classroom issue but also a national issue that must be addressed properly. This study aims to integrate, clean and analysis through automated data mining techniques. Using data mining techniques is one of the processes of transferring raw data from current educational system to meaningful information that can be used to help the school community to make a right decision to achieve much better results. This proved DM provides means to assist both educators and students, and improve the quality of education. The result and findings in the study show that automated system will give the same result compare with manual system of integration and analysis and also could be used by the management to make faster and more efficient decision in order to map or plan efficient teaching approach for students in the future.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 Terry E, Spradlin, Kirk R, Walcott C, Kloosterman P, Zaman K, McNabb S, Zapf J & associates, “Is The Achievement Gap in Indiana Narrowing”, Education Resources Information Center Journal, September 2005.
2 Cripps A, “Using Artificial Neural Nets to Predict Academic Performance,” American Psychological Association Journal, pp. 33 – 37, Feb.1996.
3 Beal, C. R. & Cohen, P. R. (2006). Temporal Data Mining for Educational Applications. Chapman, A. D. 2005. Principles and Methods of Data Cleaning – Primary Species and Species-Occurrence Data, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen.
4 Hayek, John C, Kuh, George D, “College Activities and Environmental Factors Associated with The Development of Life Long Learning Competencies of College Seniors” Education Resources Information Center Journal, November 1999.
5 Henchey, Norman, “Schools That Make A Difference : Final Report. Twelve Canadian Secondary Schools in Low Income Settings” Education Resources Information Center Journal, November 2001.
6 Gibson, Margaret A, “Improving Graduation Outcomes for Migrant Students”, Education Resources Information Center Journal, July 2003.
7 Ma, Y., Liu, B., Wong, C. K., Yu, P. S. & Lee, S. M. (2000). Targeting the Right Students Using Data Mining.
8 Shmueli, G., Patel, N. R., & Bruce, P. C. (2007). Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner. Hoboken, NJ: John Wiley & Sons.
Mr. Tajul Rosli Razak
UNIVERSITI TEKNOLOGI MARA (PERLIS) - Malaysia
tajulrosli@perlis.uitm.edu.my
Dr. Abdul Hapes Mohammed
- Malaysia
Dr. Noorfaizalfarid Hj Mohd Noor
- Malaysia
Dr. Muhamad Arif Hashim
- Malaysia