Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles

Abstract

E-learning systems present to students learning material carefully prepared and organized by teachers to meet some course goals. However, further relevant information that can help students to complete their learning process about different subjects can also be found on the Web in the form of Web pages, articles, encyclopedias, dictionaries, etc. In this chapter, we present a personalized recommendation approach to suggest relevant Web pages to students according to the context of the activities they are carrying out and their content-based profiles. Learning of user profiles is based on a clustering analysis of learning experiences captured through observation. Afterwards, the learned profiles as well as the more recently accessed documents in a Web-based learning system are used to recommend pages gathered by searching the Web.