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A Survey on E-Learning Personalization Techniques Using Data Mining

M. JaiKumar, V. Uma Maheswari


Web based education System (E-learning) has the tremendous growth in current learning scenario. Such systems utilize several data mining techniques and tools to evaluate the knowledge level of every user. This survey explores the impact of data mining techniques in adaptive e-learning environment. Implementation adaptive E-learning environment with personalized knowledge evaluation is a challenging task. In this paper, we explore different techniques and methods, which used in such environment and modern e-learning environment. Finally, we list the comparisons of these schemes by some criteria for Web based education system. By applying the most appropriate Data mining techniques on personalized e-learning environment will bring better solution, so based on the comparison, our system gives optimum way to achieve high accuracy in e-learning recommendation.


E-Learning System, Data Mining and Survey, Fuzzy Student Model, Personalization, Programming.

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J. Lo, Y. Chan, and S. Yen, “Designing an adaptive web-based learning system based on students’ cognitive styles identified online” Comput Edu., vol. 58, no. 1, pp. 209–222, Jan. 2012.

Brown, I. (1998). The effect of WWW document structure on students’ information retrieval. Journal of Interactive Media in Education, 98(12), 1–18

Graf, S., & Kinshuk, T.-C. L. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116–131

Paramythis, Alexandros, and Susanne Loidl-Reisinger. "Adaptive learning environments and e-learning standards." Second european conference on e-learning. Vol. 1. 2003.

Sterbini, Andrea, and Marco Temperini. "Adaptive construction and delivery of web-based learning paths." Frontiers in Education Conference, 2009. FIE'09. 39th IEEE. IEEE, 2009.

Ngai, Eric WT, J. K. L. Poon, and Y. H. C. Chan. "Empirical examination of the adoption of WebCT using TAM." Computers & education 48.2 (2007): 250-267.

Romero, Cristóbal, Sebastián Ventura, and Enrique García. "Data mining in course management systems: Moodle case study and tutorial." Computers & Education 51.1 (2008): 368-384.

Chou, Shih‐Wei, and Chien‐Hung Liu. "Learning effectiveness in a Web‐based virtual learning environment: a learner control perspective." Journal of computer assisted learning 21.1 (2005): 65-76.

http://www. Efrontlearning. net

SalisuSani et al, “Proposal for Ontology Based Approach to Fuzzy Student Model Design”.

Tzone-I Wang et al, “A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning”.

Lili Zhang, Shaocheng Tong, and Yongming Li, “Adaptive Fuzzy Output-Feedback Control with Prescribed Performance for Uncertain Nonlinear Systems”.

L Jegatha Deborah et al, “Fuzzy-logic based learning style prediction in e-learning using web interface information”.

YongmingLi, “Adaptive fuzzy control of uncertain stochastic nonlinear systems with unknown dead zone using small-gain approach

J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Englewood Cliffs, NJ, USA: Prentice-Hall, 2001.

Chen, Chih-Ming, Ling-Jiun Duh, and Chao-Yu Liu. "A personalized courseware recommendation system based on fuzzy item response theory." E-Technology, e-Commerce and e-Service, 2004. EEE'04. 2004 IEEE International Conference on. IEEE, 2004.

Steven Gray, “Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs”.

Jang, Jyh-Shing Roger, Chuen-Tsai Sun, and EijiMizutani. "Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence." (1997).

Manouselis, Nikos, RiinaVuorikari, and Frans Van Assche. "Collaborative recommendation of e‐learning resources: an experimental investigation."Journal of Computer Assisted Learning 26.4 (2010): 227-242.

Gao, Fengrong, et al. "Personalized service system based on hybrid filtering for digital library." Tsinghua Science & Technology 12.1 (2007): 1-8.

Yu, Zhiwen, et al. "TV program recommendation for multiple viewers based on user profile merging." User modeling and user-adapted interaction 16.1 (2006): 63-82.

Tung-Cheng Hsieh et al, “Designing and implementing a personalized remedial learning system for enhancing the programming learning”.

Baylari, Ahmad, and Gh A. Montazer. "Design a personalized e-learning system based on item response theory and artificial neural network approach." Expert Systems with Applications 36.4 (2009): 8013-8021.

Ghauth, Khairil Imran Bin, and NorAniza Abdullah. "Building an e-learning recommender system using vector space model and good learners average rating." Advanced Learning Technologies, 2009. ICALT 2009. Ninth IEEE International Conference on. IEEE, 2009.


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