Cover Image

Analyzing Academic Achievement of CAS's Students Using Data Mining

Massive information can be collected from students' data in order to produce knowledge. The educational institutions collect students' data such as academic information, demographic, and personal traits. The data collected based on these variables used to predict the students' academ...

Full description

Saved in:
Bibliographic Details
Main Author: Nor Asiah, Abdul Rahman
Format: Thesis
Language:English
English
Published: 2009
Subjects:
QA71-90 Instruments and machines
Online Access:http://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf
http://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf
http://etd.uum.edu.my/1737/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.etd.1737
record_format eprints
spelling my.uum.etd.17372013-07-24T12:12:58Z http://etd.uum.edu.my/1737/ Analyzing Academic Achievement of CAS's Students Using Data Mining Nor Asiah, Abdul Rahman QA71-90 Instruments and machines Massive information can be collected from students' data in order to produce knowledge. The educational institutions collect students' data such as academic information, demographic, and personal traits. The data collected based on these variables used to predict the students' academic achievement. On this study, the respondents are students who have graduated within the period of six months in the year 2006, 2007 and 2008. Two data mining techniques for analyzing and building the classification model for students' achievement in College of Arts and Sciences (CAS), Universiti Utara Malaysia (UUM) are presented. Initially, the relationship and correlation between students' cumulative grade point average (CGPA) with academic background, demographic, entry qualification, sponsorship and interpersonal skills, students' achievement are analyzed. For model building purposes, final CGPA has been used as a target. The analysis conducted using Multinomial Logistic Regression and Neural Network found that, gender, entry qualification, language qualification (Bahasa Malaysia and English), family income, sponsorship, analytical and analysis skill as well as teamwork are all the best predictors contributed to students' performance. The result obtained through this study can be used to help the management of CAS to make certain decisions and to predict the outcome of current and future students. 2009-05 Thesis NonPeerReviewed application/pdf en http://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf application/pdf en http://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf Nor Asiah, Abdul Rahman (2009) Analyzing Academic Achievement of CAS's Students Using Data Mining. Masters thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
Nor Asiah, Abdul Rahman
Analyzing Academic Achievement of CAS's Students Using Data Mining
description Massive information can be collected from students' data in order to produce knowledge. The educational institutions collect students' data such as academic information, demographic, and personal traits. The data collected based on these variables used to predict the students' academic achievement. On this study, the respondents are students who have graduated within the period of six months in the year 2006, 2007 and 2008. Two data mining techniques for analyzing and building the classification model for students' achievement in College of Arts and Sciences (CAS), Universiti Utara Malaysia (UUM) are presented. Initially, the relationship and correlation between students' cumulative grade point average (CGPA) with academic background, demographic, entry qualification, sponsorship and interpersonal skills, students' achievement are analyzed. For model building purposes, final CGPA has been used as a target. The analysis conducted using Multinomial Logistic Regression and Neural Network found that, gender, entry qualification, language qualification (Bahasa Malaysia and English), family income, sponsorship, analytical and analysis skill as well as teamwork are all the best predictors contributed to students' performance. The result obtained through this study can be used to help the management of CAS to make certain decisions and to predict the outcome of current and future students.
format Thesis
author Nor Asiah, Abdul Rahman
author_facet Nor Asiah, Abdul Rahman
author_sort Nor Asiah, Abdul Rahman
title Analyzing Academic Achievement of CAS's Students Using Data Mining
title_short Analyzing Academic Achievement of CAS's Students Using Data Mining
title_full Analyzing Academic Achievement of CAS's Students Using Data Mining
title_fullStr Analyzing Academic Achievement of CAS's Students Using Data Mining
title_full_unstemmed Analyzing Academic Achievement of CAS's Students Using Data Mining
title_sort analyzing academic achievement of cas's students using data mining
publishDate 2009
url http://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf
http://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf
http://etd.uum.edu.my/1737/
_version_ 1644276508281274368
score 13.211869

Similar Items