A Study on Efficiency of Data Mining Approaches to Online -Learning Methods

Authors

  • Chinaguravaiah Makkena  Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, JNTU Hyderabad, Hyderabad, India
  • K Anuradha  Department of Computer Science and Engineering, KMIT, Affiliated Jntu Hyderabad, Hyderabad, Andhra Pradesh, India

Keywords:

Online-Learning, Adaptive, Data Mining, Learning System

Abstract

Now days, on-line learning systems increase student's ability to be told on their own. the use of information Mining in education system has become a significant analysis area, and it's accustomed collect info expeditiously from electronic learning systems. The academic systems face numerous issues like static delivery of the fabric identification of student desires and checking the standard of student interaction level. This paper surveys academic data processing approaches like pattern mining, clustering, classification, and AI. The goal of this paper is to get economical information from web-based learning systems. This work provides specific web-based courses, well-known adjective environment, and intelligent learning systems. The comparison of electronic learning systems and elaborated analysis modify students to enhance the training expertise. This paper presents the previously performed analysis connected studies, techniques that may be used to improve the scholar information and tutorial progress in an E-Learning system.

References

  1. F. Castro, A. Vellido, À. Nebot et al., "Applying data mining techniques to e-learning problems," Evolution of teaching and learning paradigms in intelligent environment, pp. 183-221: Springer, 2007.
  2. J.L. Hung, and K. Zhang, "Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching," MERLOT Journal of Online Learning and Teaching, 2008.
  3. G. Sakarkar, S. Deshpande, and V. Thakare, "Intelligent online e-learning systems: a comparative study," International Journal of Computer Applications, vol. 56, no. 4, 2012.
  4. M. A. Hogo, "Evaluation of e-learners behaviour using different fuzzy clustering models: a comparative study," arXiv preprint arXiv:1003.1499, 2010.
  5. K. E. Maull, M. G. Saldivar, and T. Sumner, "Observing the online behavior of teachers: From Internet usage to personalization for pedagogical practice."
  6. F. Mödritscher, M. Andergassen, and G. Neumann, "Dependencies between e-learning usage patterns and learning results." p. 24.
  7. D. Suresh, and S. Prakasam, "The Impact of E-learning system using Rank-based Clustering Algorithm (ESURBCA),"International Journal of Computer Applications, vol. 83, no. 7, 2013.
  8. A. Garrido, and L. Morales, "E-Learning and Intelligent Planning: Improving Content Personalization," Tecnologias del Aprendizaje, IEEE Revista Iberoamericana de, vol. 9, no. 1, pp. 1-7, 2014.
  9. B. Y. Babu, G. Sriramakrishnan, and G. Visvanathan, "Survey of E-Learning: Content Personalization."
  10. S. BAher, and L. LMR J, "A comparative study of association rule algorithms for course recommender system in e-learning," International Journal of Computer Applications, vol. 39, no. 1, pp. 48-52, 2012.
  11. M. Blagojević, and Ž. Micić, "A web-based intelligent report e-learning system using data mining techniques," Computers & Electrical Engineering, vol. 39, no. 2, pp. 465-474, 2013.
  12. S. A. E. A. Elaal, "E-Learning Using Data Mining," Chinese-Egyptian Research Journal Helwan University, 2013.
  13. R. S. Y. Baker, K (2009), Baker, R.S.; Yacef, K (2009). "The state of educational data mining in 2009: A review and future visions". JEDM-Journal of Educational Data Mining 1 (1): 2017.
  14. S. Kumar, A. K. Gankotiya, and K. Dutta, "A comparative study of moodle with other e-learning systems." pp. 414-418.
  15. Y. -h. Wang, and H.-C. Liao, "Data mining for adaptive learning in a TESL-based e-learning system," Expert Systems with Applications, vol. 38, no. 6, pp. 6480-6485, 2011.
  16. M. F. AlAjmi, S. Khan, and A. Sharma, "Studying Data Mining and Data Warehousing with Different E-Learning System," IJACSA) International Journal of Advanced Computer Science and Applications, vol. 4, no. 1, 2013.
  17. A. Iglesias, L. Moreno, P. Martínez et al., "Evaluating the accessibility of three open‐source learning content management systems: A comparative study," Computer Applications in Engineering Education, vol. 22, no. 2, pp. 320-328, 2014.
  18. S. B. Aher, and L. Lobo, "Applicability of data mining algorithms for recommendation system in e-learning." pp. 1034-1040.
  19. M. Prema, and S. Prakasam, "Effectiveness of Data Mining-based E-learning system (DMBELS)," International Journal of Computer Applications, vol. 66, no. 19, 2013.
  20. G.J. Hwang, T. C. Huang, and J. C. Tseng, "A group-decision approach for evaluating educational web sites," Computers & Education, vol. 42, no. 1, pp. 65-86, 2004.
  21. Y. Arora, A. Singhal, and A. Bansal, "PREDICTION & WARNING: a method to improve student's performance," ACM SIGSOFT Software Engineering Notes, vol. 39, no. 1, pp. 1-5, 2014.
  22. D. Kabakchieva, "Predicting student performance by using data mining methods for classification," Cybernetics and information technologies, vol. 13, no. 1, pp. 61-72, 2013.
  23. P. Meedech, N. Iam-On, and T. Boongoen, "Prediction of Student Dropout Using Personal Profile and Data Mining Approach," Intelligent and Evolutionary Systems, pp. 143-155: Springer, 2016.
  24. S. Sivakumar, S. Venkataraman, and C. Gombiro, "A User - Intelligent Adaptive Learning Model for Learning Management System Using Data Mining and Artificial Intelligence," International Journal for Innovative Research in Science and Technology, vol. 1, no. 10, pp. 78-81, 2015.
  25. K. Dolenc, and B. Aberšek, "TECH8 intelligent and adaptive e-learning system: Integration into Technology and Science classrooms in lower secondary schools," Computers & Education, vol. 82, pp. 354-365, 2015.
  26. P. Appalla, V. M. Kuthadi, and T. Marwala, "An efficient educational data mining approach to support e-learning," Information Systems Design and Intelligent Applications, pp. 63-75: Springer, 2016.
  27. R. Jindal, and M. D. Borah, "A Survey on Educational Data Mining and Research Trends," International Journal of Database Management Systems, vol. 5, no. 3, pp. 53, 2013.
  28. E. Wakelam, A. Jefferies, N. Davey et al., "The Potential for Using Artificial Intelligence Techniques to Improve e-Learning Systems." p. 762.
  29. N. A. Al Saiyd, and I. A. Al-Sayed, "A generic model of student-based adaptive intelligent web-based learning environment." pp. 781-786.
  30. L. Morales, A. Garrido, and I. Serina, "Planning and execution in a personalised e-learning setting." pp. 233-242.
  31. S. Prakasam, "An agent-based intelligent system to enhance e-learning through mining techniques."

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

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How to Cite

[1]
Chinaguravaiah Makkena, K Anuradha, " A Study on Efficiency of Data Mining Approaches to Online -Learning Methods , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.808-816, July-August-2017.