|
| Automated Data Integration, Cleaning and Analysis Using Data Mining and SPSS Tool For Technical School in Malaysia
|
|
Full
text: |
 PDF(2.14MB) |
|
|
Source |
International Journal of Computer Science and Security (IJCSS) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(0 Bytes) |
|
Volume: 6 Issue: 4 |
| |
Pages: |
|
Publication
Date: August 2012 |
|
ISSN
(Online): 1985-1553 |
|
|
|
|
|
Pages |
211 - 225 |
|
Author(s) |
|
|
|
Published
Date |
10-08-2012 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Data Integration, Data Cleaning, Data Analysis, Decision Support |
|
|
| |
|
|
| No
record found |
| |
|
| |
|
|
| 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 |
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.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Tajul Rosli Razak : Colleagues
|
|
| Abdul Hapes Mohammed : Colleagues
|
|
| Noorfaizalfarid Hj Mohd Noor : Colleagues
|
|
| Muhamad Arif Hashim : Colleagues
|
|
