Abstract-This paper focuses on how students access web pages in a task specific information retrieval. An investigation on how students search the web for their current needs was carried out and students' behavioural characteristics as they surf the internet to answer some given online multiple choice questions was collected. Twenty three students participated in the study and a number of behavioural characteristics were captured. Camtasia studio 7 was used to record their searching activity. The result shows that 328 web pages were visited by the students, and among the parameters captured, the time spent on the search task has a stronger correlation with the students' performance than any other captured parameter. The time spent on a document can be used as a good implicit indicator to infer learner's interest in a context based recommender system.
Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak & Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.
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