Reference Hub1
Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

Renuka Mahajan
Copyright: © 2017 |Pages: 23
ISBN13: 9781522504894|ISBN10: 1522504893|EISBN13: 9781522504900
DOI: 10.4018/978-1-5225-0489-4.ch001
Cite Chapter Cite Chapter

MLA

Mahajan, Renuka. "Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System." Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, IGI Global, 2017, pp. 1-23. https://doi.org/10.4018/978-1-5225-0489-4.ch001

APA

Mahajan, R. (2017). Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System. In V. Bhatnagar (Ed.), Collaborative Filtering Using Data Mining and Analysis (pp. 1-23). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch001

Chicago

Mahajan, Renuka. "Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System." In Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, 1-23. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0489-4.ch001

Export Reference

Mendeley
Favorite

Abstract

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.