Untitled Document
   
You are from : ( )  
     
Untitled Document
Untitled Document
 

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

In the recent past, every domain has benefited from the growth of Information and Communication Technologies (ICT). Education is one such sector with the rapid deployments of e-learning systems. Teaching and learning in e-learning domain is a serious business and there is a need for information processing in order to enrich the learning experience as well as to tap the potential of the growing e-learning business. In this context, the article aims to investigate the need for data mining, identify the problems within e-learning that data mining can solve as well as present the existing approaches, currently existing open research challenges and the future directions in this area.

Keywords :

: Not available ...

References :

  1. Arruabarrena, R., Perez, T.A.,Lopez-Cuadrado, J., and Vadillo, J.G.J.(2002). On evaluating adaptive systems for education. Adaptive Hypermedia(pp. 363-367).
  2. Baker. R. J. D. F., & Yacef. K.,(2009). The state of educational data mining in 2009: A review and future visions,  J. Educational Data Mining, vol. 1, no. 1, pp. 3–17.
  3. Baker,R.J.D.F.,( 2010). Data Mining, In: Editors-in-Chief:  Penelope Peterson, Eva Baker and Barry McGaw, Editor(s)-in-Chief, International Encyclopaedia of Education, 3rd ed, Elsevier, Oxford, pp 112-118.
  4. Barahate. S. R., (2012).  Article: Educational Data Mining as a Trend of Data Mining in Educational System. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET 2012) icwet(9):11-16, March 2012.
  5. Beck, J.E. and Mostow, J.( 2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 353-362.
  6. Bedi, K., Milic, M., Stedul, I.,(2012). Information society and e-learning. MIPRO, 2012 Proceedings of the 35th International Convention , pp.1249-1253, 21-25 May 2012
  7. Bienkowski, M.,Feng, M.,Means, B.,(2012), Enhancing Teaching and LearningThrough Educational Data Mining and Learning Analytics: An Issue Brief, Office of Educational Technology, US Department of Education.
  8. Bowles, M.,(2004). What Is Electronic Learning? [online]. In: Bowles, Marc. Relearning to E-learn: Strategies for Electronic Learning and Knowledge. Carlton, Vic.: Melbourne University Press, 2004: 3-19. Availability:<http://search.informit.com.au/documentSummary; dn=825091693221992;res=IELHSS>
  9. Clark, R. C. & Mayer, R. E., (2011). e-learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, 3rd ed. Pfeiffer, San Francisco
  10. Calders,T., Pechenizkiy,M.,(2012). Introduction to The Special Section on Educational Data Mining, ACM SIGKDD Explorations Newsletter.
  11. Castro, F., Vellido, A., Nebot, A. Mugica, F. (2007). Applying Data Mining Techniques to e-Learning Problems.  In: Jain, L.C., Tedman, R. and Tedman, D. (eds.) Evolution of Teaching  and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, 62, Springer-Verlag, 183-221.
  12. GONG, Y., RAI, D., BECK, J. and HEFFERNAN, N. (2009). Does Self-Discipline Impact Students’ Knowledge and Learning? In Proceedings of the 2nd International Conference on Educational Data Mining, 61-70.
  13. Greenhow, C., Robelia, B., & Hughes, J. (2009). Learning, teaching, and scholarship in a digital age: Web 2.0 and classroom research: What path should we take now? Educational Researcher, 38, 246–259.
  14. Grobelnik M., Mladenic D., & Jermol M.,(2002). “Exploiting text mining in publishing and education”, In Proceedings of the ICML workshop on data mining lessons learned, Sydney, Australia, pp. 34–39, 2002.
  15. Hanna, M. (2004). Data mining in the e-learning domain. In Campus-Wide Information Systems, Volume 21, Number 1, 29-34.
  16. Hodge V., & Austin J., (2004).A survey of outlier detection methodologies, Artificial Intelligence Review, 22(2), pp. 85–126, 2004.
  17. Johnson, L., A. Levine, R. Smith, and S. Stone. (2010). The 2010 Horizon Report. Austin, TX: The New Media Consortium. [Available] http://wp.nmc.org/horizon2010/
  18. Macfayden, L. P., & Dawson, S. (2010). Mining LMS data to develop an ‘‘early warning’’ system for educators: A proof of concept. Computers and Education, 54(2), 588–599
  19. Osimo, D., (2008). Web 2.0 in Government: Why and How? JRC Scientific and Technical Reports, EUR 23358 EN
  20. Rallo R., Gisbert M., & Salinas J.,(2005). “Using data mining and social networks to analyze the structure and content of educative online communities”, In International conference on multimedia and ICTs in education, Caceres, Spain, pp. 1–10, 2005.
  21. Romero, C., Ventura, S., & Bra, P. D. (2004). 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.
  22. Romero, C., Ventura, S., Pechenizkiy, M., and Baker, R. S. J. D., eds, (2010), Handbook of Educational Data Mining, CRC Press
  23. Romero, C., & Ventura, S., (2007). Educational Data Mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), pp 135–146.
  24. Romero, C.; Ventura, S.,(2010). Educational Data Mining: A Review of the State of the Art, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on , vol.40, no.6,  pp.601-618, Nov. 2010
  25. Scheuer, O. & McLaren, B.M., (2011). Educational Data Mining. In the Encyclopedia of the Sciences of Learning, Springer
  26. Tang, T.Y., McCalla, G.(2005). Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. International Journal on e-Learning 4(1) (2005) 105-129.
  27. U.S. Department of Education.,(2010),Use of Education Data at the Local Level: From Accountability to Instructional Improvement. Washington, DC: U.S. Department of Education
  28. Witten, I.H. and Frank, E. (1999). Data mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Fransisco, CA.
  29. ZAÏANE, O. (2002). Building a recommender agent for e-learning systems. In Proceedings of the International Conference on Computers in Education, 55–59.
  30. [online], IEDM, (2012). International Educational Data Mining Society, Available at http://www.educational datamining.org/ > [Accessed 3 October 2012]
  31. [Online], Society for Learning Analytics Research,(2012). http://www.solaresearch.org/  [Accessed on 5th October 2012]
  32. Beck, J., & Woolf, B. (2000). High-level student modeling with machine learning. Proceedings of the 5th International Conference on Intelligent Tutoring Systems,584–593.
  33. R. Rabbany, M. Takaffoli and O. Zaïane. Analyzing participation of students in online courses using social network analysis techniques. Proceedings of Educational Data Mining, 21-30, 2011.
  34. Rojas, I.G.; Garcia, R.M.C.(2012) , "Towards Efficient Provision of Feedback Supported by Learning Analytics," Advanced Learning Technologies (ICALT), 2012 IEEE 12th International Conference on , vol., no., pp.599-603.
  35. S.L.Tanimoto.(2007), “Improving the Prospects for Educational Data Mining” http://www.educationaldatamining.org/UM2007/Tanimoto.pdf

Untitled Document
     
Untitled Document
   
  Copyright © 2013 IJITCS.  All rights reserved. IISRC® is a registered trademark of IJITCS Properties.