A Context-Based Information Agent for Supporting Intelligent Distance Learning Environments

Abstract

The large amount of information now available on the Web can play a prominent role in building a cooperative intelligent distance learning environment. We propose a system to provide learners with useful information in a group discussion. Finding the right information at the right moment is quite a difficult task, especially when the learner’s interests are continually updated during the discussion. This paper presents a context-based information agent that can observe conversations among a community of learners on the Web, interpret the learners’ inputs, and then assess the current context of the session. The agent must be able to adapt its behavior autonomously to the changing context, build a new query to get updated information from the Web, and originate the search task. Then, it can filter the results, organizing, and presenting information useful to the learners in their current activities. We claim that specifying the context of a search better can significantly improve search results. An important task, therefore, is to assess the context. For this, we have developed dominant meaning space. That is a new set based measure to evaluate the closeness between queries and documents. Our experiments show that the proposed method greatly improves retrieval effectiveness, in terms of average overall accuracy as well as that in the top twenty documents. This work is the core component of a new pedagogical agent to help people learn tasks defined within greater Web-based tutoring systems.