Knowledge Discovery from Students’ Result Repository:Association Rule Mining Approach

Over the years, several statistical tools have been used to analyze students’ performance from different points of view. This paper presents data mining in education environment that identifies students’ failure patterns using association rule mining technique. The identified patterns are analysed to offer a helpful and constructive recommendations to the academic planners in higher institutions of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve students’ academic performance and trim down failure rate. The software for mining student failed courses was developed and the analytical process was described.

[1]  Senol Zafer Erdogan,et al.  A DATA MINING APPLICATION IN A STUDENT DATABASE , 2005 .

[2]  Chih-Yang Lin,et al.  A Two-Phase Fuzzy Mining and Learning Algorithm for Adaptive Learning Environment , 2001, International Conference on Computational Science.

[3]  K. Robert Lai,et al.  The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance , 2003, Expert Syst. Appl..

[4]  George Angelos Papadopoulos,et al.  Information Systems Development: Towards a Service Provision Society , 2009 .

[5]  Chun-Jung Chen,et al.  Towards error-free and personalized Web-based courses , 2003, 17th International Conference on Advanced Information Networking and Applications, 2003. AINA 2003..

[6]  Robertas Damasevicius,et al.  Analysis of Academic Results for Informatics Course Improvement Using Association Rule Mining , 2008, ISD.

[7]  John F. Roddick,et al.  Association mining , 2006, CSUR.

[8]  Sylvia B. Encheva,et al.  Application of Association Rules for Efficient Learning Work-Flow , 2006, Intelligent Information Processing.

[9]  Gwo-Jen Hwang A knowledge-based system as an intelligent learning advisor on computer networks , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[10]  George D. Magoulas,et al.  Neural network-based fuzzy modeling of the student in intelligent tutoring systems , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[11]  Tao Xiong,et al.  A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[12]  William F. Punch,et al.  PREDICTING STUDENT PERFORMANCE: AN APPLICATION OF DATA MINING METHODS WITH THE EDUCATIONAL WEB-BASED SYSTEM LON-CAPA , 2003 .

[13]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[14]  Àngela Nebot,et al.  Applying Data Mining Techniques to e-Learning Problems , 2007 .

[15]  Buket Dogan,et al.  Association Rule Mining from an Intelligent Tutor , 2008 .