Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web search engines do a poor job of serving an important segment of the population: children. While there are many important problems in interface design, content filtering, and results presentation related to addressing children's search needs, perhaps the most fundamental challenge is simply that of providing relevant results at the right level of reading difficulty. At the opposite end of the proficiency spectrum, it may also be valuable for technical users to find more advanced material or to filter out material at lower levels of difficulty, such as tutorials and introductory texts.We show how reading level can provide a valuable new relevance signal for both general and personalized Web search. We describe models and algorithms to address the three key problems in improving relevance for search using reading difficulty: estimating user proficiency, estimating result difficulty, and re-ranking based on the difference between user and result reading level profiles. We evaluate our methods on a large volume of Web query traffic and provide a largescale log analysis that highlights the importance of finding results at an appropriate reading level for the user.
This paper describes the results of a study designed to validate the use of domain competency models to diagnose student scientific misconceptions and to generate personalized instruction plans using digital libraries. Digital library resources provided the content base for human experts to construct a domain competency model for earthquakes and plate tectonics encoded as a knowledge map. The experts then assessed student essays using comparisons against the constructed domain competency model and prepared personalized instruction plans using the competency model and digital library resources. The results from this study indicate that domain competency models generated from select digital library resources may provide the desired degree of content coverage to support both automated diagnosis and personalized instruction in the context of nationally-recognized science learning goals. These findings serve to inform the design of personalized instruction tools for digital libraries.
Abstract. This paper describes a personalization approach for using online resources in digital libraries to support intentional learning. Personalized resource recommendations are made based on what learners currently know and what they should know within a targeted domain to support their learning process. We use natural language processing and graph based algorithms to automatically select online resources to address students' specific conceptual learning needs. An evaluation of the graph based algorithm indicates that the majority of recommended resources are highly relevant or relevant for addressing students' individual knowledge gaps and prior conceptions.
Although searchers often click on more than one result following a query, little is known about how they interact with search results after their first click. Using large scale query log analysis, we characterize what people do when they return to a result page after having visited an initial result. We find that the initial click provides insight into the searcher's subsequent behavior, with short initial dwell times suggesting more future interaction and later clicks occurring close in rank to the first. Although users think of a search result list as static, when people return to a result list following a click there is the opportunity for the list to change, potentially providing additional relevant content. Such change, however, can be confusing, leading to increased abandonment and slower subsequent clicks. We explore the risks and opportunities of changing search results during use, observing, for example, that when results change above a user's initial click that user is less likely to find new content, whereas changes below correlate with increased subsequent interaction. Our results can be used to improve people's search experience during the course of a single query by seamlessly providing new, more relevant content as the user interacts with a search result page, helping them find what they are looking for without having to issue a new query.
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