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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Main Author:	Chong, Sze Wei
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA76 Computer software
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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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). A new analysis model for data mining processes in higher educational systems. Proceeding of 5th international conference ITHET. MMU, Cyberjaya, Malaysia. pp. 5-8. Beal, C. R. & Cohen, P. R. (2006). Temporal Data Mining for Educational Applications.Bravo, J., Vialardi, C. & Ortigosa, A. (2007). A problem-oriented method for supporting AEH authors through data mining. Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07). Madrid, Spain. pp. 53-62.Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T.,Shearer, C. & Wirth, R.(1999). CRISP-DM 1.0. Proceeding of Step-by-step Data Mining Guide. pp. 9-10.Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T.,Shearer, C. & Wirth, R.(1999). CRISP-DM 1.0. Proceeding of Step-by-step Data Mining Guide. pp. 9-10.Chen, M.-S., Jan, J. & Yu, P.S. (1996). Data mining: An overview from a database perspective. Proceeding of IEEE Transaction on Knowledge and Data Engineering,8. pp. 866-883.Garcia, E., Romero, C., Ventura, S. & Calder, T. (2007). Drawbacks and solutions of applying association rule mining in learning management systems. Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07). pp. 13-22.Gargano, M. L. & Raggad, B. G. (1999). Data mining-a powerful information creating tool. Proceeding of OCLC Systems & Services. 15(2), pp. 81-90.Hamalainen, W., Laine, T. H. & Suitinen, E. (2004). Data Mining in Personalizing Distance Education Courses. University of Joensuu, Finland. Lloyd, N. M., Heffernan, N. T. & Ruiz, C. (2007).Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. Proceeding of Educational Data Mining Workshop. Marina del Rey, CA. USA. pp. 40-49. Luan, J. (2001). Data mining as driven by knowledge management in higher education:Persistence clustering and prediction. Proceeding of Keynote for SPSS Public Cpnference, UCSF. pp. 1-16.Ma, Y., Liu, B., Wong, C. K., Yu, P. S. & Lee, S. M. (2000). Targeting the Right Students Using Data Mining.Merceron, A. & Yacef, K. (2007). Revisiting interestingness of strong symmetric association rules in education data. Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07). pp. 3-12.Minaei-Bidgoli, B., Kortemeyer, G. & Punch, W. F. (2003). Optimizing classification ensembles via a genetic algorithm for a web-based educational system. East Lansing,USA. pp.1-9.Minaei-Bidgoli, B., Tan, P. N. & Punch, W. F. (2004). Mining interesting contrast rules for a web-based educational system. East Lansing, USA. pp. 1-8.Perera, D., Kay, J., Yacef, K. & Koprinska I. (2007). Mining learners’ traces from an online collaboration tool. Proceeding of Educational Data Mining Workshop.University of Sydney, Australia. pp. 60-69.Ravi, S., Kim, J. & Shaw, E. (2007). 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.

Analyzing Primary Student Data Using Data Mining

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