E-Iearning is a new topic in education environments and gradually has found its proper place in the recent training methods. But due to the fact that, there is no face to face contact between the teachers and students in e-learning systems, neither the teachers nor the students in the course are aware of each other`s behavior, so in these types of systems, the need of feedback between the students and the professors is felt , this will help improve the teaching and learning process. Although most of these systems can offer a reporting tool" the teachers, in general, cannot provide a clear view about the status of their students. In this paper we investigate efficient query search, as well as global issues, with the aim of solving this problem with a new approach in the design of decision support systems, a system which would enable teachers to answer questions like these in order to understand students` academic achievement using data mining techniques based on the data in the database management system for educational content. Finally, the paper concludes and suggests that teachers of these courses do not require the learning and data mining techniques, but only a model or models are needed to interpret the results of teachers and other educational activities that are essential to help.

Data Mining; Web Mining; Data ware house; ELearning; Distance Education

[I] Ahmadian, Kaveh, computer-assisted education standards and the advantages and disadvantages of this approach on a case study and experience Mytny Executive, 30/2/81. http://www.amoozgar.com.
[2] Akhavan, Peyman, E., M. Rice, presents a proposed framework for the development of e-Iearning and e-Iearning in the country, Tomorrow Management Journal, Fall Winter 84.
[3] Jalali, AA, Abbasi, MA. Learning: the changing face of education in the world, 8/10 1 81 http :/ / www.amoozgar.com
[4] Agrawal, R. and Srikant, R. : Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.
[5] Alvarez, E. Zorrilla, M. E. 2008. Orientaciones en el disefioy evaluaci6n de uncurso virtual para la ensefianzadeaplicacionesinformaticas. RevistaIberoamericana de TecnologiasdelAprendizaje (IEEE-RITA), 3(2), 61-70.http://webs.uvigo.es/cesei/RITAl2008111
[6] Conrad, D. L. 2002. Engagement, excitement, anxiety and fear: Learners` experiences of starting an online course. American Journal of Distance Education, 16(4), pp. 205-226.
[7] Dougherty, J. ,Kohavi, M. , and Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In Int. Conf. Machine Learning Tahoe City, CA,pp. 194-202.
[8] Douglas,I.,2008.MeasuringParticipation in Internet Supported Courses. International Conference on Computer Science and Software Engineering, 5, pp. 714-717.
[9] Graf, S. ,Kindhuk and Liu, T. Identifying Learning Styles in Learning Management Systems by Using Indications from Students` Behaviour. Proc. of the 8th IEEE International Conference on Advanced Learning Technologies.July, Santander, Spain. 2008.
[10] Han, J. Data mining: Concepts and Techniques. Morgan Kaufmann. 2006.
[II] Hung, J. , and Zhang, K. 2008. Revealing Online Learning Behaviors and Activity Patterns and MakingPredictionswithData Mining Techniques in Online Teaching.Journal of Dnline Learning and Teaching 8(4), pp. 426-436.
[12] Krichen, 1. 2007. Investigating Learning Styles in the Online Educational Environment. Proceedings of the 8th ACMSIG information Conference on Information Technology Education, 127- 134, Destin, Florida, USA, 18 - 20 de October 2007.
[13] Merceron, A. and Yacef, K. . 2008. Interestingness Measuresfor Association Rules in EducationalData. 1st International Conference on Educational Data Mining (EDM08). Montreal, Canada.
[14] Millan, S, Zorri II a, M. E. ,Menasalvas, 2005. E. Intelligent elearningplatforms infrastructure. XXXI Latin American lnformatics Conference (CLEI`2005). Cali, Colombia. 29, Shiraz UniversityPiatetsky-Shapiro. 2009. Data Mining Tools Used Poll. KDNuggets.Com.
[16] Romero, C. and Ventura, S. Educational Data Mining: ASurvey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146, 2007.
[17] Romero, C. , Ventura, S. , Espejo, P. G. , and Hervas, C. DataMining Algorithms to Classify Students. International Conference on Educational Data Mining, Canada, 2008.
[18] SCORM 2004 3rd Edition. The Sharable Content Object Reference Model, ADL. 2009.
[19] Steen, H. L. 2008. Effective eLearning Design.Journal of Online Learning and Teaching, 4 (4). http://jolt. merlot. org/vol4n04/steen 1208. Htm.
[20] Witten, I. H., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition).Morgan Kaufmann. ISBN 0-12-088407-0.
[21] Zarane, O. 2002. Building a recommender agent for elearning systems.Computers in Education, 2002. Proceedings of the International Conference on Computers in Education, pp. 55-59.
[22] Zorrilla, M. , and Alvarez, E. 2008. MA TEP: Monitoring and Analysis Tool for e-Learning Platforms. Proceedings of the8th IEEE International Conference on Advanced Learning Technologies. Santander, Spain.