Optimising web usage mining for building adaptive e-learning site: a case study

Abstract

An important application of web usage mining is mining web log data. We propose a new optimised technique for web mining, in the realm of an e-learning site to recommend the best links for a learner to visit the next. It optimises web mining, by partitioning the database, on the basis of the learner's knowledge level, to create a suffix tree(s) from the existing sequences of previous 'n' learners' path. To further reduce the overhead of re-mining the web patterns, we propose that a web traversal pattern should be regarded as significant, only if it qualifies the minimum threshold of length and frequency in the database. These significant patterns are added to suffixes. They 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 n + 1th new learner. We conducted experiments on a real case study of an Indian e-learning site. This 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.