Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives. In this context, informational search missions with an intention to obtain knowledge pertaining to a topic are prominent. The importance of learning as an outcome of web search has been recognized. Yet, there is a lack of understanding of the impact of web search on a user's knowledge state. Predicting the knowledge gain of users can be an important step forward if web search engines that are currently optimized for relevance can be molded to serve learning outcomes. In this paper, we introduce a supervised model to predict a user's knowledge state and knowledge gain from features captured during the search sessions. To measure and predict the knowledge gain of users in informational search sessions, we recruited 468 distinct users using crowdsourcing and orchestrated real-world search sessions spanning 11 different topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Our supervised models utilise and derive a comprehensive set of features from the current state of the art and compare performance of a range of feature sets and feature selection strategies. Through our results, we demonstrate the ability to predict and classify the knowledge state and gain using features obtained during search sessions, exhibiting superior performance to an existing baseline in the knowledge state prediction task.

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Significant parts of cultural heritage are produced on the web during the last decades. While easy accessibility to the current web is a good baseline, optimal access to the past web faces several challenges. This includes dealing with large-scale web archive collections and lacking of usage logs that contain implicit human feedback most relevant for today's web search. In this paper, we propose an entity-oriented search system to support retrieval and analytics on the Internet Archive. We use Bing to retrieve a ranked list of results from the current web. In addition, we link retrieved results to the WayBack Machine; thus allowing keyword search on the Internet Archive without processing and indexing its raw archived content. Our search system complements existing web archive search tools through a user-friendly interface, which comes close to the functionalities of modern web search engines (e.g., keyword search, query auto-completion and related query suggestion), and provides a great benefit of taking user feedback on the current web into account also for web archive search. Through extensive experiments, we conduct quantitative and qualitative analyses in order to provide insights that enable further research on and practical applications of web archives

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