Publikationen (FIS)

SaL-Lightning Dataset

Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search

authored by
Christian Otto, Markus Rokicki, Georg Pardi, Wolfgang Gritz, Daniel Hienert, Ran Yu, Johannes von Hoyer, Anett Hoppe, Stefan Dietze, Peter Holtz, Yvonne Kammerer, Ralph Ewerth

The emerging research field Search as Learning (SAL) 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.

L3S Research Centre
External Organisation(s)
Leibniz-Institut für Wissensmedien (IWM)
German National Library of Science and Technology (TIB)
GESIS - Leibniz Institute for the Social Sciences
University of Bonn
Heinrich-Heine-Universität Düsseldorf
Media University (HdM) Stuttgart
Conference contribution
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Human-Computer Interaction, Information Systems
Electronic version(s) (Access: Open) (Access: Closed)