Rough Set Rules Extraction for Student Programming Skills

Programming is a critical subject to computer science or information technology students. It is one of the fundamental skills they need to acquire during study. The aim of the study is to generate a compact set of rules using real data to predict student's performance. Not all variables as usua...

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Main Author:	Kerwad, Mokhtar Massoud
Format:	Thesis
Language:	eng eng
Published:	2006
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/1848/1/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf https://etd.uum.edu.my/1848/2/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf
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id	my-uum-etd.1848
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Kerwad, Mokhtar Massoud Rough Set Rules Extraction for Student Programming Skills
description	Programming is a critical subject to computer science or information technology students. It is one of the fundamental skills they need to acquire during study. The aim of the study is to generate a compact set of rules using real data to predict student's performance. Not all variables as usual if good results are to be obtained. Data mining refers to one of the phases or step within the knowledge discovery in databases (KDD) processes for extracting used rough set technique. The extracted rules will be a measurement of the students' performance in programming and give the insight to educators on what should be help the students to master programming skills.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Kerwad, Mokhtar Massoud
author_facet	Kerwad, Mokhtar Massoud
author_sort	Kerwad, Mokhtar Massoud
title	Rough Set Rules Extraction for Student Programming Skills
title_short	Rough Set Rules Extraction for Student Programming Skills
title_full	Rough Set Rules Extraction for Student Programming Skills
title_fullStr	Rough Set Rules Extraction for Student Programming Skills
title_full_unstemmed	Rough Set Rules Extraction for Student Programming Skills
title_sort	rough set rules extraction for student programming skills
granting_institution	Universiti Utara Malaysia
granting_department	Faculty of Information Technology
publishDate	2006
url	https://etd.uum.edu.my/1848/1/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf https://etd.uum.edu.my/1848/2/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf
_version_	1747827217402953728
spelling	my-uum-etd.18482013-07-24T12:13:23Z Rough Set Rules Extraction for Student Programming Skills 2006 Kerwad, Mokhtar Massoud Faculty of Information Technology Faculty of Information Technology QA76 Computer software Programming is a critical subject to computer science or information technology students. It is one of the fundamental skills they need to acquire during study. The aim of the study is to generate a compact set of rules using real data to predict student's performance. Not all variables as usual if good results are to be obtained. Data mining refers to one of the phases or step within the knowledge discovery in databases (KDD) processes for extracting used rough set technique. The extracted rules will be a measurement of the students' performance in programming and give the insight to educators on what should be help the students to master programming skills. 2006 Thesis https://etd.uum.edu.my/1848/ https://etd.uum.edu.my/1848/1/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf application/pdf eng validuser https://etd.uum.edu.my/1848/2/Mokhtar_Massoud_Kerwad_-_Rough_set_extraction_for_student_programming_skills.pdf application/pdf eng public masters masters Universiti Utara Malaysia Beynon, M., Curry, B., and Morgan, P., (2000). Classification and rule induction using Rough set theory. Expert system 17(3)136-148. Byrne, P., and Lyons, G., (2001). The Effect of Student Attributes on Success in Programming. Department of Information Technology, ACM ISBN. Bergin, S., and Reilly, R., (2005). Programming: Factors that Influence Success, ACM SIGCSE, pp. 411-415. Bilski, P., Walcza., Z. and Wojeiechowski, J., (2005). 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A Report of three NSF Workshops on Mining Large, Massive, and Distributed Data. http://www.ncdm.uic.edu/m3d2.htm. Hostetler, T., R., (1983). Predicting Student Success in an Introduction Programming Course. Proceedings of NECC5. IEEE Press, Silver Spring, Md. Hu, X., and Cercone, N., (1994). Discovery of Decision Rules in Relational Databases: A Rough set Approach, ACM Department of Computer Science. Han, J. and Kamber, M., (2001). Data Mining Concepts and Techniques. Morgan Kaufinann. San Francisco. Hu, X., Lin, T., Y., and Han, J., (2004). A New Rough sets Model Based on Database Systems, Fundamental Informatics XX (2004)1-18. Honghai, F., Guoshun,, C., Yufeng, W., Bingru, Y., and Yurnei, C., (2005). Rough set Based Classification rules generation for SARS Patients. ZEEE. Jiang, Y., Xu, C., Gou., J and Li, Z., (2004). Research on Rough set Theory Extension and Rough Reasoning. ZEEE, International Conference on Systems. Kusiak, A., (2001). Rough set Theory: A Data Mining Tool for Semiconductor Manufacturing. Department of Industrial Engineering, Intelligent Systems Laboratory. Lil, J., and Cercone, N., (2005). A Rough set Based Model to Rank the Importance of Association Rules Vo1.3642, pp. 109-118. Ma, Y., Liu, B., Wong, C. K., Yu, P.s., and Lee, S. M., (2000). Targeting the Right Students Using Data Mining, ACM, pp. 457-463. Miller, M. T., Jerebko, A. K., Malley, J. D., and Summers, R. M., (2003). Feature Selection for Computer-Aided Polyp Detection using Genetic Algorithms. Proceedings of SPIE Vol5031. Norita, M., N, Hibadullah, C., F., and Osman, J., (2005). Factors Affecting Performance in Introductory Programming. Faculty of Information Technology, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia. Pawlak, Z., (1996). Rough sets and Data Analysis. Institute of Theoretical and Applied Informatics Polish Academy of Sciences, IEEE Pillay, N., Vikash R., and Jugoo., (2005). An Investigation into Student Characteristics Affecting Novice Programming Performance. Department of Computer Science. Volume 37, Number 4. Peng, Y., Liu, G., Lin, T and Geng, H., (2005). Application of Rough set Theory in Network Fault Diagnosis. Proceedings of the Third International Conference on Information Technology and Applications (ICITAY05) IEEE. Raymer, M. L., Punch,W. F., Goodman, E. D., Kuhn, L. A., and Jain, A. k., (2000).Dimensionality Reduction Using Genetic Algorithms. IEEE Transactions on Evolutionary Computation, vol. 4, no. 2. Ronald D., and McFarland., (2003), Teaching Students to Learn in the Computer Science and Information Systems Curriculum: Creating a Distinction Between Content and Methods. JCSC 19,l (October 2003). Revett, K., Gorunescu, F., Gorunescu, M., Darzi, E., and Ene, M., (2005). A Breast Cancer Diagnosis System: A Combined Approach Using Rough sets and Probabilistic Neural Networks. IEEE Schultz, M. G., Eskin, E., and Stolfo, S. J., (2000), Data Mining Methods for Detection of New Malicious Executable, Department of Computer Science, Colombia University and Stat University of New york. Tan, P., Steinback, M. and Kurnar, V., (2006). Introduction to Data Mining. Addison Wesley. Pearson Education. Vaishnavi, V. and Kuechler, W., (2005). Design Research in Information Systems. Retrieved Fabruary 20, 2004, last updated June 5, 2005, pkom http://www.isworld.org/Researchdesigddris/Sworld.htm. Werth, L. H., (1986), Predicting Student Performance in a Beginning Computer Science Class. Department of Computer Sciences, ACM, Wei, J., Huang, D., Wang, S., and Ma, Z., (2002), Rough set Based Decision tree. Intelligent Control and Automation. 2002. Proceedings of the 4th World Congress on. Volume 1, Page(s):426-431 vol. 1 Digital Object Identifier. Yusof, A. M., and Abdullah, R., (2005), The Evolution of Programming Courses: Course curriculum, students, and their performance. Computer Science and Information Technology. Volume 37, Number 4.

Rough Set Rules Extraction for Student Programming Skills

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