A Machine Learning Approach for One-Stop Learning

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called one-stop learning (OSL). In this chapter, the contributions made by Lim and Ko (2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: IGI PUBLISHING This paper appears in the publication, Data Mining and Knowledge Discovery Technologies edited by D. Taniar © 2008, IGI Global 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-gl bal.com ITB15231