Retrieval Algorithms Optimized for Human Learning

While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one involving unnecessarily easy or difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. We address this problem by introducing a novel theoretical framework, algorithms, and empirical analysis of an information retrieval model that is optimized for learning outcomes instead of generic relevance. We do this by formulating an optimization problem that incorporates a cognitive learning model into a retrieval objective, and then give an algorithm for an efficient approximate solution to find the search results that represent the best 'training set' for a human learner. Our model can personalize results for an individual user's learning goals, as well as account for the effort required to achieve those goals for a given set of retrieval results. We investigate the effectiveness and efficiency of our retrieval framework relative to a commercial search engine baseline ('Google') through a crowdsourced user study involving a vocabulary learning task, and demonstrate the effectiveness of personalized results from our model on word learning outcomes.