How Does Team Composition Affect Knowledge Gain of Users in Collaborative Web Search?

Luyan Xu, Xuan Zhou, Ujwal Gadiraju

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

15 Citations (Scopus)
73 Downloads (Pure)


Studies in searching as learning (SAL) have revealed that user knowledge gain not only manifests over a long-term learning period, but also occurs in single short-term web search sessions. Though prior works have shown that the knowledge gain of collaborators can be influenced by user demographics and searching strategies in long-term collaborative learning, little is known about the effect of these factors on user knowledge gain in short-term collaborative web search. In this paper, we present a study addressing the knowledge gain of user pairs in single collaborative web search sessions. Using crowdsourcing we recruited 454 unique users (227 random pairs), who then collaboratively worked on informational search tasks spanning 10 different topics and information needs. We investigated how users’ demographics and traits, and the interaction between these factors could influence their knowledge gain. We found that in contrast to offline collaboration cases, user demographics such as gender, age, etc. do not significantly effect users’ knowledge gain in collaborative web search sessions. Instead, our results highlight the presence of labor division of queries and particular interaction patterns in communication that facilitate knowledge gain in user pairs. Based on these findings, we propose a multiple linear regression model to predict the knowledge gain of users in collaborative web search sessions from the perspective of team composition.
Original languageEnglish
Title of host publicationHT'20: Proceedings of the 31st ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)978-1-4503-7098-1
Publication statusPublished - 2020
EventHT'20: 31st ACM Conference on Hypertext and Social Media - Online event, United States
Duration: 13 Jul 202015 Jul 2020
Conference number: 31


ConferenceHT'20: 31st ACM Conference on Hypertext and Social Media
Abbreviated titleHT'20
Country/TerritoryUnited States
CityOnline event
OtherVirtual/online event due to COVID-19

Bibliographical note

Virtual/online event due to COVID-19
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Collaborative web search
  • Knowledge gain
  • Team composition
  • Search as learning
  • Crowdsourcing
  • User study


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