A Personalized Group-Based Recommendation Approach for Web Search in E-Learning

The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students' dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual's learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This paper aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students' learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.