XML based pre-processing and analysis of log data in adaptive e-learning system: An algorithmic approach

E-learning has become the most popular way of delivering education and learning. Adaptive E-learning systems are the systems that adapt according to the requirements of the user. These systems should be capable of capturing the user preferences in terms of their learning styles and adapt the user interface accordingly. Web log analysis of the usage data can provide useful information regarding the learning styles. This analysis is extremely useful to develop an adaptive environment for the learner and at the same time for instructors to see how often their course contents are being used. In this paper a modified literature based approach is proposed where the learner’s behavior is tracked by capturing the interactions with e-learning portal. The captured behavior will be stored in the form of sessions which will be grouped together to generate the sequence files in the XML formats. The learning styles have been identified by an algorithmic approach based on the frequency and time that the learners spend on various learning components on the portal. The approach is useful to provide an adaptive user interface which includes adaptive contents and recommendations in learning environment to improve the efficiency of e-learning. The learning style model used is Felder-Silverman Learning Style Model (FSLSM) to fit the learning styles into an adaptive environment.