Foundations and Trends® in Information Retrieval > Vol 19 > Issue 4

Search as Learning

By Kelsey Urgo, University of San Francisco, USA, kurgo@usfca.edu | Jaime Arguello, University of North Carolina at Chapel Hill, USA, jarguello@unc.edu

 
Suggested Citation
Kelsey Urgo and Jaime Arguello (2025), "Search as Learning", Foundations and Trends® in Information Retrieval: Vol. 19: No. 4, pp 365-556. http://dx.doi.org/10.1561/1500000084

Publication Date: 11 Mar 2025
© 2025 K. Urgo and J. Arguello
 
Subjects
Design and evaluation,  Information visualization,  Perception and the user interface,  Specific user groups (children, elders, etc.),  Applications of IR,  Evaluation issues and test collections for IR,  Usability, interactivity, and visualization issues in IR,  User modelling and user studies for IR,  Web search,  Applications and case studies
 

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In this article:
1. Introduction
2. Characterizing Learning
3. Learning Assessment
4. The Effects of Task and Searcher Characteristics on Learning
5. Predicting Learning During Search
6. Tools to Support Learning During Search
7. Self-Regulated Learning (SRL)
8. Future Research Directions
References

Abstract

Search systems are often designed to support simple look-up tasks, such as fact-finding and navigation tasks. However, people increasingly use search engines to complete tasks that require deeper learning. In recent years, the search as learning (SAL) research community has argued that search systems should also be designed to support information-seeking tasks that involve complex learning as an important outcome. This monograph aims to provide a comprehensive review of prior research in search as learning and related areas.

Searching to learn can be characterized by specific learning objectives, strategies, and context. Therefore, we begin by reviewing research in education that has aimed at characterizing learning objectives, strategies, and context. Then, we review methods used in prior studies to measure learning during a search session. Here, we discuss two important recommendations for future work: (1) measuring learning retention and (2) measuring a learner's ability to transfer their new knowledge to a novel scenario. Following this, we discuss studies that have focused on understanding factors that influence learning during search and search behaviors that are predictive of learning. Next, we survey tools that have been developed to support learning during search. Searching for the purpose of learning is often a solitary activity. Research in self-regulated learning (SRL) aims to understand how people monitor and control their own learning. Therefore, we review existing models of SRL, methods to measure engagement with specific SRL processes, and tools to support effective SRL. We conclude by discussing potential areas for future research.

DOI:10.1561/1500000084
ISBN: 978-1-63828-536-6
208 pp. $99.00
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ISBN: 978-1-63828-537-3
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Table of contents:
1. Introduction
2. Characterizing Learning
3. Learning Assessment
4. The Effects of Task and Searcher Characteristics on Learning
5. Predicting Learning During Search
6. Tools to Support Learning During Search
7. Self-Regulated Learning (SRL)
8. Future Research Directions
References

Search as Learning

Search systems are often designed to support simple look-up tasks, such as fact-finding and navigation tasks. However, people increasingly use search engines to complete tasks that require deeper learning. In recent years, the search as learning (SAL) research community has argued that search systems should also be designed to support information seeking tasks that involve complex learning as an important outcome.

This monograph provides a comprehensive review of prior research in search as learning and related areas. Searching to learn can be characterized by specific learning objectives, strategies, and context. Therefore, the monograph begins with a review of research in education that has aimed at characterizing learning objectives, strategies, and context. Then, review methods used in prior studies to measure learning during a search session are covered. Two important recommendations for future work are studied: (1) measuring learning retention and (2) measuring a learner’s ability to transfer their new knowledge to a novel scenario. Following this, studies that have focused on understanding factors that influence learning during search and search behaviors that are predictive of earning are discussed. Also, tools that have been developed to support learning during search are surveyed.

Searching for the purpose of learning is often a solitary activity. Research in self-regulated learning (SRL) aims to understand how people monitor and control their own learning. Therefore, existing models of SRL are researched, as well as methods to measure engagement with specific SRL processes, and tools to support effective SRL. The monograph concludes with suggesting potential areas for future research.

 
INR-084