  The emerging research field Search as Learning investigates how the Web
facilitates learning through modern information retrieval systems. SAL research
requires significant amounts of data that capture both search behavior of users
and their acquired knowledge in order to obtain conclusive insights or train
supervised machine learning models. However, the creation of such datasets is
costly and requires interdisciplinary efforts in order to design studies and
capture a wide range of features. In this paper, we address this issue and
introduce an extensive dataset based on a user study, in which $114$
participants were asked to learn about the formation of lightning and thunder.
Participants' knowledge states were measured before and after Web search
through multiple-choice questionnaires and essay-based free recall tasks. To
enable future research in SAL-related tasks we recorded a plethora of features
and person-related attributes. Besides the screen recordings, visited Web
pages, and detailed browsing histories, a large number of behavioral features
and resource features were monitored. We underline the usefulness of the
dataset by describing three, already published, use cases.
