Title: Comparative study of mining algorithms for adaptive e-learning environment

Authors: Renuka Mahajan; J.S. Sodhi; Vishal Mahajan; Richa Misra

Addresses: AIIT, Amity, Noida, UP, India ' AKC Data Systems, Delhi, India ' HCL Technologies, Noida, UP, India ' JIM, Noida, UP, India

Abstract: Many algorithms have been introduced in the area of sequential pattern mining over the past few years. In this paper, we try to investigate some of these algorithms and make a performance comparison. The main objective of this paper is a survey based on the recently published research papers to perform the comparison of various frequent pattern mining algorithms in the realm of adaptive e-learning. This study gives the comparative advantages and drawbacks of these algorithms. For this, we use a real case study of an Indian e-learning site and select the most suitable algorithm for generating frequent usage patterns (of topics referred by various learners). This would be useful in recommending the next navigation path to the new learner(s) in an adaptive e-learning domain.

Keywords: educational web mining; relationship mining; association rules mining; sequential pattern mining; recommender-based e-learning; adaptive e-learning; frequent pattern mining; FP-tree; generalised sequential patterns; GSP; WAP mine; PL WAP algorithm; electronic learning; online learning.

DOI: 10.1504/IJLEG.2014.068271

International Journal of Logistics Economics and Globalisation, 2014 Vol.6 No.2, pp.102 - 111

Received: 27 Mar 2014
Accepted: 03 Aug 2014

Published online: 23 Mar 2015 *

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