  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.
