Retrieval Techniques for Contextual Learning

Following constructivist models of contextual learning, knowledge acquisition goes beyond mere absorption of isolated facts, and, instead is enabled, stimulated and supported by related existing knowledge and experiences. In this paper, we discuss a range of query expansion and result list reranking techniques aiming to preserve contextual dependencies among retrieved documents and, thereby, enhancing the performance of learning-centric search engines. Our empirical evaluation is based on a snapshot of Wikipedia and suggests significantly increased usability during an interactive user study.