A web usage mining based recommendation model for learning management systems

Web based learning systems provides huge volume of educational content to learners. However, a single learner might not be interested in learning all the contents delivered. To encourage learners of varying skill sets and to develop learning interests web recommendation system is needed for web based learning. This paper focuses on providing recommendations to learners as well as web masters to improve overall effectiveness of web based teaching and learning. This work deals with analysis of web log data and development of recommendation framework using web usage mining techniques like upper approximation based rough set clustering using k nearest neighbors, dynamic support pruned all k-th order Markov model and all k-th order association rule mining by dynamic frequent (k+1) item set generation using Apriori. The goal of this integrated approach is to make accurate recommendations for learning management systems with reduced state space complexity.