Mining Association Rules from Empirical Data in the Domain of Education
AbstractThe data mining techniques and their applications are widely recognized as powerful tools in various domains. In the domain of education there is a variety of data of various types that are collected. The important question is: Is it possible to process collected data with the data mining technique and what are main advantages of data mining and e-learning interaction? If an e-learning system accumulates a huge volume of data, then it is possible to deploy techniques and tools from the domain of data mining in order to gain valuable information. The research presented in this paper is conducted on real-life data that originates from the Balkan region. The software system Weka is used to generate association rules. The main result of this research is the assessment of the parameters that are associated with the opinion that computer skills will be helpful in the future, from the students point of view. This result is very important because it gives the exact insight to computer technology usage in the Balkans schools. Furthermore, some advantages of the usage of data mining techniques in the domain of education are determined.
 C. Romero, S. Ventura, E. Garcia, Data mining in course management system: Moodle case study and tutorial, An Int. J. of Computers and Education 51, pp. 368-384, 2008.
 E. Brtka, D. Radosav, V. Brtka, The Data Mining module as a part of the e-learning system, (In Serbian), In Proc. of InfoTech Conference, Vrnjacka Banja, Serbia, 2009.
 E. Brtka, The data mining analysis approach in pedagogical research, Master Thesis, (In Serbian), Technical Faculty "Mihajlo Pupin", Zrenjanin, Serbia, 2009.
 E. Gaudioso, L. Talavera, Data mining to suport tutoring in virtual learning communities: Experiences and challenges. In C. Romero and S. Ventura (Eds.), Data mining in e-learning, Southampton UK, Wit Press, pp. 207-226, 2006.
 K. Diego, Data Mining and Statistics: What is the Connection?, The Data Administration Newsletter, LLC - www.TDAN.com
 D. Hand, H. Mannila, P. Smyth, Principles of Data Mining, MIT Press, Cambridge, MA. ISBN 0-262-08290-X. OCLC 226126187, 2001.
 DBMiner (2007), http://www.dbminer.com
 Clementine (2007), http://www.spss.com/clementine/
 Miner (2007), http://www-306.ibm.com/software/data/iminer/
 Weka (2007), http://www.cs.waikato.ac.nz/ml/weka/
 Keel (2007), http://www.keel.es/
 O. Zaiane, J. Luo, Web usage mining for a better web-based learning environment, In Proc. of conf. on advanced technology for education, Banff, Alberta, pp. 60-64, 2001.
 D. Silva, M. Vieira, Using data warehouse and data mining resources for ongoing assessment in distance learning, In IEEE int. conf. on advanced learning technologies, Kazan, Russia, pp. 40-45, 2002.
 J. Tane, C. Schmitz, G. Stumme, Semantic resource management for the web: An elearning application, In Proc. of the WWW conference, New York, USA, pp. 1-10, 2004.
 E. Garcia, C. Romero, S. Ventura, C. Castro, Using rules discovery for the continuous improvement of e-learning courses, In Int. conf. intelligent data engineering and automated learning, Burgos, Spain, pp. 887-895, 2006.
 Z. Pawlak, A. Skowron, Rudiments of rough sets, An Int. J. of Information Sciences 177:3- 27, 2007.
 A. Ohrn, Discernibility and Rough Sets in Medicine: Tools and Applications, PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway, 1999.
 I. H. Witten, E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufman, San Francisco, 2005.
 R. O. Duda, P. E. Hart, D. G. Stork, Pattern classification, Wiley Interscience, 2000.
 G. Chen, C. Liu, K. Ou, B. Liu, Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology, Journal of Educational Computing Research 23(3):305-332, 2000.
 B. Minaei-Bidgoli, W. Punch, Using genetic algorithms for data mining optimization in an educational web-based system, In Genetic and evolutionary computation conference, Chicago, USA, pp. 2252-2263, 2003.
 R. Baker, A. Corbett, K. Koedinger, Detecting student misuse of intelligent tutoring systems, In Intelligent tutoring systems, Alagoas, Brazil, pp. 531-540, 2004.
 S. B. Kotsiantis, C. J. Pierrakeas, P. E. Pintelas, Predicting students performance in distance learning using machine learning techniques", Applied Artificial Intelligence 18(5):411-426, 2004.
 M. V. Yudelson, O. Medvedeva, E. Legowski, M. Castine, D. Jukic, C. Rebecca, Mining student learning data to develop high level pedagogic strategy in a medical ITS, In Proceedings of AAAI workshop on educational data mining, Boston, pp. 1-8, 2006.
 M. Cocea, S. Weibelzahl, Can log files analysis estimate learners level of motivation? In Proceedings of the workshop week Lernen - Wissensentdeckung - Adaptivitat, Hildesheim, pp. 32-35, 2006.
 W. Hamalainen, M. Vinni, Comparison of machine learning methods for intelligent tutoring systems, In Proceedings of the eighth international conference in intelligent tutoring systems, Taiwan, pp. 525-534, 2006.
 A. K. Jain, M. N. Murty, P. J. Flynn, Data clustering: A review, ACM Computing Surveys 31(3):264-323, 1999.
 T. Tang, G. McCalla, Smart recommendation for an evolving e-learning system, International Journal on E-Learning 4(1):105-129, 2005.
 E. Mor, J. Minguillon, E-learning personalization based on itineraries and long-term navigational behavior, In Proceedings of the 13th international world wide web conference, pp. 264-265, 2004.
 W. Hamalainen, J. Suhonen, E. Sutinen, H. Toivonen, "Data mining in personalizing distance education courses", In World conference on open learning and distance education, Hong Kong, pp. 1-11, 2004.
 J. Spacco, T. Winters, T. Payne, T. Inferring use cases from unit testing, In AAAI workshop on educational data mining, New York, pp. 1-7, 2006.
 R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, In Proc.of the ACM SIGMOD international conference on management of data, Washington DC, USA, pp. 1-22, 1993.
 O. Zaiane, Building a recommender agent for e-learning systems, In Proc.of the int. conference in education, Auckland, New Zealand, pp. 55-59, 2002.
 G. J. Hwang, C. L. Hsiao, C. R. Tseng, A computer-assisted approach to diagnosing student learning problems in science courses, Journal of Information Science and Engineering 19: 229-248, 2003.
 J. Lu, Personalized e-learning material recommender system, In International conference on information technology for application, Utah, USA, pp. 374-379, 2004.
 P. Markellou, I. Mousourouli, S. Spiros, A. Tsakalidis, Using semantic web mining technologies for personalized e-learning experiences, In Proc. of the web-based education, Grindelwald, Switzerland, pp. 461-826, 2005.
 B. Minaei-Bidgoli, P. Tan, W. Punch, Mining interesting contrast rules for a web-based educational system, In Int. conf.on machine learning applications, Los Angeles, California, pp. 1-8, 2004.
 C. Romero, S. Ventura, P.D. Bra, Knowledge discovery with genetic programming for providing feedback to courseware author, User Modeling and User-Adapted Interaction: The Journal of Personalization Research 14(5):425-464, 2004.
 P. Yu, C. Own, L. Lin, On learning behavior analysis of web based interactive environment, In Proc. of the implementing curricular change in engineering education, Oslo, Norway, pp. 1-10, 2001.
 A. Merceron, K. Yacef,Mining student data captured from a web-based tutoring tool: Initial exploration and results, Journal of Interactive Learning Research 15(4):319-346, 2004.
 J. Freyberger, N. Heffernan, C. Ruiz, Using association rules to guide a search for best fitting transfer models of student learning, In Workshop on analyzing studenttutor interactions logs to improve educational outcomes at ITS conference, Alagoas, Brazil, pp. 1-10, 2004.
 A. A. Ramli, Web usage mining using apriori algorithm: UUM learning care portal case, In Int. conf. on knowledge management, Malaysia, pp. 1-19, 2005.
 R. Spence, Information visualization, Addison-Wesley, 2001.
 R. Andonie, Extreme Data Mining: Inference from Small Datasets, INT J COMPUT COMMUN, ISSN 1841-9836, Vol. 5(3):280-291, 2010.
 R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo (eds.), Proc. of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487-499, 1994.
 G. Luger, W. Stubblefield, Artificial Intelligence - structures and strategies for complex problem solving, University of New Mexico, Albuquerque, The Benjamin/Cummings Publishing Company Inc, 1993.
 M. Pater, D.E. Popescu, Multi-Level Database Mining Using AFOPT Data Structure and Adaptive Support Constrains, INT J COMPUT COMMUN, ISSN 1841-9836, 3(S):437-441, 2008.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.