SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search

TL;DR

The SaL-Lightning Dataset provides comprehensive data on user search behavior, eye gaze, resource interactions, and knowledge gain during web search, supporting research in Search as Learning with detailed behavioral and resource features.

Contribution

This paper introduces a large, multi-faceted dataset capturing search behavior, eye gaze, resource interactions, and knowledge change, designed to facilitate SAL research and machine learning applications.

Findings

Dataset includes 114 participants' search sessions on lightning formation.

Knowledge was measured before and after search via questionnaires and recall tasks.

The dataset supports multiple research use cases in Search as Learning.

Abstract

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.