Grade prediction improved by regular and maximal association rules

Yıl 2015, Cilt: 3 Sayı: 2, 57 - 61, 01.04.2015

Anca Udristoiu , Stefan Udristoiu

https://doi.org/10.18201/ijisae.17210

Cited By: 1

Öz

In this paper we propose a method of predicting student scholar performance using the power of regular and maximal association rules. Due to the large number of generated rules, traditional data mining algorithms can become difficult and inappropriate to educational systems. Thus, we use some methods to overcome this problem, discovering rules useful in educational process. These methods are applied to the e-learning system Moodle, for “Database” course.

Anahtar Kelimeler

Education data mining, Regular association rule, Maximal association rule, Learning management system

Kaynakça

• B. Liu, W. Hsu, Y. Ma(1998). Integrating classification and association rule mining. Proc. 4th International Conference on Knowledge Discovery and Data Mining. Pages. 80–86.
• A. Amir, Y. Aumann, R. Feldman, M. Fresco(2005). Maximal Association Rules: A Tool for Mining Associations in Text. Journal of Intelligent Information Systems. Vol. 25(3). Pages. 333–345.
• C. Romero, A. Zafra, J.M. Luna, S. Ventura(2013). Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data. Expert Systems. Vol. 30(2). Pages. 162-172.
• C. Romero and S. Ventura(2010). Educational data mining: a review of the state-of-the-art. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews. Vol. 40. Pages. 601–618.
• O.R. Zaïane(2002). Building a Recommender Agent for e-Learning Systems. Proc. The International Conference on Computers in Education. ICCE. Pages. 55–59.
• O.R Zaïane, J. Luo(2001). Towards Evaluating Learners’ Behavior in a Web-based Distance Learning Environment. Proc. IEEE International Conference on Advanced Learning Technologies, ICALT’01. Pages. 357–360.
• B. Minaei-Bidgoli, P.N. Tan, W.F. Punch(2004). Mining Interesting Contrast Rules for a Web-based Educational System. Proc. The 2004 International Conference on Machine Learning and Applications. ICMLA’04.
• G.J. Hwang, , C.L. Hsiao, C.R. Tseng(2003). A Computer-Assisted Approach to Diagnosing Student Learning Problems in Science Courses. Journal of Information Science and Engineering. Vol. 19. Pages. 229–248.
• A. Kumar(2005). Rule-Based Adaptive Problem Generation in Programming Tutors and its Evaluation. Proc. The 12th International Conference on Artificial Intelligence in Education. Pages. 36–44.
• T. Matsui, T. Okamoto(2003). Knowledge Discovery from Learning History Data and its Effective Use for Learning Process Assessment Under the e-Learning Environment. Proc.Society for Information Technology and Teacher Education International Conference. Pages. 3141–3144.
• S.D. Resende, V.M.T. Pires(2001). An Ongoing Assessment Model for Distance Learning. Proc. International Conference Internet and Multimedia Systems and Applications. Acta Press. Pages. 17–21.
• S.D. Resende, V.M.T. Pires(2002). Using Data Warehouse and Data Mining Resources for Ongoing Assessment of Distance Learning. Proc. IEEE International Conference on Advanced Learning Technologies. ICALT’02.
• M.F. Costabile, A. De Angeli, T. Roselli, R. Lanzilotti, P. Plantamura(2003). Evaluating the Educational Impact of a Tutoring Hypermedia for Children. Information Technology in Childhood Education Annual. Pages. 289–308.
• H.H. Hsu, C.H. Chen, W.P. Tai(2003). Towards Error-Free and Personalized Web-Based Courses. Proc. The 17th International Conference on Advanced Information Networking and Applications. AINA’03. Pages. 99–104.
• P. Markellou, I. Mousourouli, S. Spiros, A. Tsakalidis(2005). Using Semantic Web Mining Technologies for Personalized e-Learning Experiences. Proc. Conference on Web-based Education. WBE’05.
• M.L. Dos Santos, K. Becker(2003). Distance Education: a Web Usage Mining Case Study for the Evaluation of Learning Sites. Proc. The 3rd IEEE International Conference on Advanced Learning Technologies. ICALT’03. Pages. 360–361.
• R. Agrawal, T. Imielinski, and A. Swami(1993). Mining Association Rules between Sets of Items in Large Databases. Proc. SIGMOD International Conference on Management of Data. Pages. 207–216.
• M. Houtsma and A. Swami(1993). Set-Oriented Mining of Association Rules. Technical Report RJ 9567. IBM.
• R. Agrawal and R. Srikant(1994). Fast Algorithms for Mining Association Rules in Large Databases. Proc. International Conference on Very Large Data Bases. Pages. 487-499.
• J. Cole(2005). Using Moodle. O’Reilly.