Towards Intelligent Search Assistance For Inquiry-Based Learning

In Online Inquiry-Based Learning (OIBL) learners search for information to answer driving questions. While learners conduct sequential related searches, the search engines interpret each query in isolation, and thus are unable to utilize task context. Consequently, learners usually get less relevant search results. We are developing a NLP-based search agent to bridge the gap between learners and search engines. Our algorithms utilize contextual features to provide user with search term suggestions and results re-ranking. Our pilot study indicates that our method can effectively enhance the quality of OIBL.