Web Usages Mining in Automatic Detection of Learning Style in Personalized e-Learning System

The e-learning system generates huge amount of data which contain hidden and valuable information and they are required to be explored for useful knowledge for decision making. Learner’s activity related data and all behavioral vis-a-vis navigational data are stored in the log files. Extracting knowledgeable information from these data by using Web Usage Mining technique is a very challenging and difficult task. Basically, there are three steps of Web Usage Mining i.e. preprocessing, pattern discovery and pattern analysis. This paper proposes a Dynamic Dependency Adaptive Model (DDAM) based on Bayesian Network. This model mines learner’s navigational accesses data and finds learner’s behavioral patterns which individualize each learner and provide personalized learning path to them according to their learning styles in the learning process. Result shows that learners effectively and efficiently access relevant information according to their learning style which is useful in enhancing their learning process. This model is learner centric but it also discovers patterns for decision making process for academicians and people at top management.

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