Analyzing Primary Student Data Using Data Mining

Nowadays, academic achievement has become the most important evidence for establishing the value of Malaysia’s education boundary. In this study, the primary students’ examination data is collected on the previous examination mark yet sake to be analyzed for their future study plan. The selection of...

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主要作者:	Chong, Sze Wei
格式:	Thesis
语言:	eng eng
出版:	2009
主题:	QA76 Computer software
在线阅读:	https://etd.uum.edu.my/1574/1/Chong_Sze_Wei_801162_%282009%29.pdf https://etd.uum.edu.my/1574/2/1.Chong_Sze_Wei_801162_%282009%29.pdf
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id	my-uum-etd.1574
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Chong, Sze Wei Analyzing Primary Student Data Using Data Mining
description	Nowadays, academic achievement has become the most important evidence for establishing the value of Malaysia’s education boundary. In this study, the primary students’ examination data is collected on the previous examination mark yet sake to be analyzed for their future study plan. The selection of using data mining approaches was based on the capability of data mining as a grateful tool for academic analysis purposes. Focused on educational boundary, data mining approaches can be used for the process of uncovering hidden information and patterns that can help school community forecast the students’ academic achievement. Therefore, the other relevant data such as student performance information and family income also engaged in this study. The overall relevant raw datasets is used for preprocessed and analyzed using statistical method. In addition, the result from the statistical manner analysis point out the considerable contribution of these attributes to the academic achievement plan.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Chong, Sze Wei
author_facet	Chong, Sze Wei
author_sort	Chong, Sze Wei
title	Analyzing Primary Student Data Using Data Mining
title_short	Analyzing Primary Student Data Using Data Mining
title_full	Analyzing Primary Student Data Using Data Mining
title_fullStr	Analyzing Primary Student Data Using Data Mining
title_full_unstemmed	Analyzing Primary Student Data Using Data Mining
title_sort	analyzing primary student data using data mining
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1574/1/Chong_Sze_Wei_801162_%282009%29.pdf https://etd.uum.edu.my/1574/2/1.Chong_Sze_Wei_801162_%282009%29.pdf
_version_	1747827169336229888
spelling	my-uum-etd.15742013-07-24T12:12:23Z Analyzing Primary Student Data Using Data Mining 2009 Chong, Sze Wei College of Arts and Sciences (CAS) College of Art and Science QA76 Computer software Nowadays, academic achievement has become the most important evidence for establishing the value of Malaysia’s education boundary. In this study, the primary students’ examination data is collected on the previous examination mark yet sake to be analyzed for their future study plan. The selection of using data mining approaches was based on the capability of data mining as a grateful tool for academic analysis purposes. Focused on educational boundary, data mining approaches can be used for the process of uncovering hidden information and patterns that can help school community forecast the students’ academic achievement. Therefore, the other relevant data such as student performance information and family income also engaged in this study. The overall relevant raw datasets is used for preprocessed and analyzed using statistical method. In addition, the result from the statistical manner analysis point out the considerable contribution of these attributes to the academic achievement plan. 2009 Thesis https://etd.uum.edu.my/1574/ https://etd.uum.edu.my/1574/1/Chong_Sze_Wei_801162_%282009%29.pdf application/pdf eng validuser https://etd.uum.edu.my/1574/2/1.Chong_Sze_Wei_801162_%282009%29.pdf application/pdf eng public masters masters Universiti Utara Malaysia Anjewierden, A., Koll”offel, B. & Hulshof, C. (2007). Using data mining methods for automated chat analysis to understand and support inquiry learning processes. Proceeding of Towards Educational Data Mining. Enschede, The Netherlands. pp.27-36.Arnold, A., Beck, J. & Scheines, R. (2004). An Inductive Approach. Proceeding of Feature Discovery in the Context of Educational Data Mining. Pittsburgh, PA 15213, USA.Beikzadeh, M. R. & Delavari, N. (2004). 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Mining on-line discussions: Assessing technical quality for student scaffolding and classifying messages for participation profiling.Proceeding of Educational Data Mining Workshop. Marina del rey, CA. USA. pp.70-79. Raghavan, V. & Hafez, A. (2000). Dynamic Data Mining. University of Louisiana, USA.pp. 1-10.Reza, B. & Naeimeh, D. (2004). A New Analysis Model for Data Mining Processes in Higher Educational Systems. MMU, Cyberjaya. Malaysia. pp. 5-8.Seifert, J. W. (2004). Data mining: An overview. Proceeding of CRS Report for Congress. pp. 1-16.Tanimoto, S. T. (2007). Improving the prospects for educational data mining. Pp. 1-6.Tsantis, L. & Castellani, J. (2001). Enhancing learning environments through solutionbased knowledge discovery tools: Forecasting for self-perpetuating systemic reform.pp. 1-35.Yudelson, M. V., Medvedeva, O., Legowski, E., Castine, M., Jukic, D. & Crowley, R. S.(2006). Mining student learning data to develop high level pedagogic strategy in a medical ITS. 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Analyzing Primary Student Data Using Data Mining

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