Metadata domain-knowledge driven search engine in "HyperManyMedia" E-learning resources

Abstract

In this paper, we exploit the synergies between Information Retrieval and E-learning by describing the design of a system that uses "Information Retrieval" in the context of the Web and "E-learning". With the exponential growth of the web, we noticed that the "general-purpose" of web applications started to diminish and more domain-specific or personal aspects started to rise, e.g., the trend of personalized web pages, a user's history of browsing and purchasing, and topical/focused search engines. The huge explosion of the amount of information on the web makes it difficult for online students to find specific information with a specific media format unless a prior analysis has been made. In this paper, we present a metadata domain-driven search engine that indexes text, powerpoint, audio, video, podcast, and vodcast lectures. These lectures are stored in a prototype "HyperManyMedia" E-learning web-based platform. Each lecture in this platform has been tagged with metadata using the domain-knowledge of these resources.