Optimized Web Mining Technique for Adaptive E-learning Site: A Case Study
Renuka Mahajan, J S Sodhi
Journal of Information Organization, Vol. 5, No. 3, 2015.
DOI: 
Link: http://www.dline.info/jio/fulltext/v5n3/v5n3_1.pdf
Abstract:
An important application of web usage mining is mining web log data, where the sequences of web pages
accessed by various web users, over a period of time, are recorded on the web server. We propose a new optimized
technique in realm of an e-learning site that pre-processes the web log data to recommend the best links for a learner to
visit next. We propose a novel methodology, by partitioning the database, on the basis of the learnerâ€™s knowledge level,
to create a specialized suffix tree(s) from the existing sequences of previous â€˜nâ€™ learnersâ€™ path. Further to reduce the
overhead of re-mining the web patterns from the whole web data, we propose that a web traversal pattern should be
regarded significant, only if it qualifies the minimum threshold of length and frequency in the database. These significant
patterns are added to generalize suffixes. These are then mined, using the most efficient mining algorithm after a
comparative analysis of various algorithms, to find the most frequent navigation paths for recommendation to new
learner. We conducted experiments in web log mining on a real case study of an Indian e-learning site. The proposed
methodology is verified by experiments with promising results on computational time. This speed up obtained, in Web
Pattern Mining, is a meaningful approach for building recommender based e-learning system, to predict the future
learning paths.