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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Online Access: | 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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QA76 Computer software Chong, Sze Wei Analyzing Primary Student Data Using Data Mining |
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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. |
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Master's degree |
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Chong, Sze Wei |
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Chong, Sze Wei |
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Chong, Sze Wei |
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Analyzing Primary Student Data Using Data Mining |
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Analyzing Primary Student Data Using Data Mining |
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Analyzing Primary Student Data Using Data Mining |
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Analyzing Primary Student Data Using Data Mining |
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Analyzing Primary Student Data Using Data Mining |
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analyzing primary student data using data mining |
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Universiti Utara Malaysia |
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College of Arts and Sciences (CAS) |
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2009 |
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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. University of Pittsburgh, pp. 1-8. |