Association analysis for an online education system

An important goal of data mining is to discover the unobvious relationships among the objects in a data set. Web-based educational systems collect vast amounts of data on user patterns, and data mining methods can be applied to these databases to discover interesting associations between student attributes, problem attributes, and solution strategies. In this paper, we propose a framework for the discovery of interesting association rules within a Web-based educational system. A hybrid measure of subjective and objective measure for rule interestingness is proposed which is called contrasting rules. Contrasting association rule is one in which a conjunction of attributes is compared for complementary subsections of a data set. We provide a new algorithm for mining contrasting rules that can improve these systems for both teachers and students - allowing for greater learner improvement and more effective evaluation of the learning process. A larger advantage of developing this approach is its wide application in any other data mining application.

[1]  Stephen D. Bay,et al.  Detecting Group Differences: Mining Contrast Sets , 2001, Data Mining and Knowledge Discovery.

[2]  William F. Punch,et al.  Mining interesting contrast rules for a web-based educational system , 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..

[3]  Stephen D. Bay.  Multivariate Discretization for Set Mining , 2001, Knowledge and Information Systems.

[4]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[5]  Abraham Silberschatz,et al.  On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.

[6]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[7]  Stephen D. Bay,et al.  Detecting change in categorical data: mining contrast sets , 1999, KDD '99.

[8]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  Cheri Speier,et al.  The LearningOnline Network with CAPA Initiative , 2001, 31st Annual Frontiers in Education Conference. Impact on Engineering and Science Education. Conference Proceedings (Cat. No.01CH37193).

[11]  William F. Punch,et al.  Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System , 2003, GECCO.

[12]  Jinyan Li,et al.  Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness , 1998, PAKDD.

[13]  Stephen D. Bay,et al.  Discovering and Describing Category Differences: What makes a discovered difference insightful? , 2000 .

[14]  Wynne Hsu,et al.  Post-Analysis of Learned Rules , 1996, AAAI/IAAI, Vol. 1.

[15]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[16]  Jiawei Han,et al.  Meta-Rule-Guided Mining of Association Rules in Relational Databases , 1995, KDOOD/TDOOD.

[17]  W. F. Punch,et al.  Predicting student performance: an application of data mining methods with an educational Web-based system , 2003, 33rd Annual Frontiers in Education, 2003. FIE 2003..