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Nowadays, many educational institutions offering elearning courses. Moreover, these courses unable to cope up with the individuals difference among learners. The learning efficiency of students increased by providing personalized course material based on characteristics such as knowledge ability, learning style, motivation in e-learning system. In this paper, we propose a new literature based method to identify learning style, motivation and knowledge ability of students automatically and dynamically using simple rules and finally offering the personalized course material based on these three factors. We used recorded data of learners' behavior during their interaction with learning objects, forums and a mapping rule to infer motivation, knowledge ability and learning styles with respect to the Felder-Silverman Learning Style Model.

Query log is the pouch of valuable information that records user's search queries and related actions on the internet. By mining the recorded information, it is possible to exploit the user's underlying goals, preferences, interests, search behaviors and implicit feedback. The wealth of mined information can be used in many applications such as query log analysis, query recommendation, query reformulation, query intent identification and many more to improve performance of search engine by providing more relevant results. Over the past decade, there has been tremendous work done for improving search engine results to flourish the users for searching. This paper reviews and compares some of the available methods to give an insight into the area of query log processing for information retrieval. Our approach classifies web query intent based on knowledge extraction from query log analysis.

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