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...

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Main Author:	Nor Asiah, Abdul Rahman
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf https://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf
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id	my-uum-etd.1737
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
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
qualification_name	masters
qualification_level	Master's degree
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
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf https://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf
_version_	1747827196845621248
spelling	my-uum-etd.17372013-07-24T12:12:58Z Analyzing Academic Achievement of CAS's Students Using Data Mining 2009-05 Nor Asiah, Abdul Rahman College of Arts and Sciences (CAS) College of Arts and Sciences 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 https://etd.uum.edu.my/1737/ https://etd.uum.edu.my/1737/1/Nor_Asiah_Abdul_Rahman.pdf application/pdf eng validuser https://etd.uum.edu.my/1737/2/1.Nor_Asiah_Abdul_Rahman.pdf application/pdf eng public masters masters Universiti Utara Malaysia Ainin, S., & Suhana, M. (2006).Student success factors: identifying key predictors. Journal of Education for Business, 81 (6), p328-333. Amral, N., Ozveren, C.S. & King, D. (2007). Short term load forecasting using Multiple Linear Regression. In 42nd International Universities Power Engineering Conference (UPEC 2007), (pp. 1192 - 1198). Akey, T. M. (2006). School context, student attitudes and behaviour, and academic achievement: an exploratory analysis. New York: MDRC Publications. Apte, C., Liu, B., Pednault, E. P. D. & Smyth, P. (2002). 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Analyzing Academic Achievement of CAS's Students Using Data Mining

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