Detecting, Understanding and Supporting Everyday Learning in Web Search

TL;DR

This paper explores how to detect and support learning during web search by analyzing user behavior, understanding their knowledge state, and integrating learning needs into search systems, highlighting research challenges and recent advances.

Contribution

It surveys existing work on detecting learning needs and understanding users in web search, and presents the authors' research contributions and agenda in this area.

Findings

Identification of key challenges in detecting learning needs

Summary of methods for understanding user knowledge state

Discussion of integrating learning support into search systems

Abstract

Web search is among the most ubiquitous online activities, commonly used to acquire new knowledge and to satisfy learning-related objectives through informational search sessions. The importance of learning as an outcome of web search has been recognized widely, leading to a variety of research at the intersection of information retrieval, human computer interaction and learning-oriented sciences. Given the lack of explicit information, understanding of users and their learning needs has to be derived from their search behavior and resource interactions. In this paper, we introduce the involved research challenges and survey related work on the detection of learning needs, understanding of users, e.g. with respect to their knowledge state, learning tasks and learning progress throughout a search session as well as the actual consideration of learning needs throughout the retrieval and ranking process. In addition, we summarise our own research contributing to the aforementioned tasks and describe our research agenda in this context.