A web content-based recommender system to promote automatic discovery of learning content for high school students

Abstract

World Wide Web has becomes a sea of information with billions of published documents and millions of changes happening on a daily basis. For web users, getting lost in this sea of information hyperspace has become a common thing in addition to facing information overload when browsing the web due to the enormous amount of information they get from the web and for high school students using the World Wide Web as a source of learning content, this might pose a great challenge in their effort to retrieve relevant information from the internet. This study has employed web content based approach to develop a prototype that learns what High School Students are expected to learn in school and personalizes the way they surf the web in search of relevant learning materials from the World Wide Web. The prototype was evaluated by online learners through an inbuilt review module in the prototype, in addition to conducting a survey on learners who used the prototype. The feedback received from the surveys was positive and showed that the prototype had a positive impact on the learners who used it as a way of personalized online learning