Searching to Learn with Instructional Scaffolding

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

3 Citations (Scopus)
7 Downloads (Pure)

Abstract

Web search engines are today considered to be the primary tool to assist and empower learners in finding information relevant to their learning goals- be it learning something new, improving their existing skills, or just fulfilling a curiosity. While several approaches for improving search engines for the learning scenario have been proposed (e.g. a specific ranking function), instructional scaffolding (or simply scaffolding)-a traditional learning support strategy-has not been studied in the context of search as learning, despite being shown to be effective for improving learning in both digital and traditional learning contexts. When scaffolding is employed, instructors provide learners with support throughout their autonomous learning process. We hypothesize that the usageof scaffolding techniques within a search system can be an effective way to help learners achieve their learning objectives whilst searching. As such, this paper investigates the incorporation of scaffolding into a search system employing three different strategies (as well as a control condition): (i) AQe, the automatic expansion of user queries with relevant subtopics; (ii) CURATEDsc, the presenting of a manually curated static list of relevant subtopics on the search engine result page; and (iii) FEEDBACKsc, which projects real-time feedback about a user's exploration of the topic space on top of the CURATEDsc visualization. To investigate the effectiveness of these approaches withrespect to human learning, we conduct a user study (N=126) where participants were tasked with searching and learning about topics such as genetically modified organisms. We find that (i) the introduction of the proposed scaffolding methods in the proposed topics does not significantly improve learning gains. However, (ii) it does significantly impact search behavior. Furthermore, (iii) immediate feedback of the participants' learning (FEEDBACKsc) leads to undesirable user behavior, with participants seemingly focusing on the feedback gauges instead of learning.

Original languageEnglish
Title of host publicationCHIIR 2021
Subtitle of host publicationProceedings of the 2021 Conference on Human Information Interaction and Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages209-218
Number of pages10
ISBN (Print)978-1-4503-8055-3
DOIs
Publication statusPublished - 2021
EventCHIIR 2021: 6th ACM SIGIR Conference on Human Information Interaction and Retrieval - Online event due to COVID-19 , Virtual, Australia
Duration: 14 Mar 202119 Mar 2021
Conference number: 6

Conference

ConferenceCHIIR 2021
Abbreviated titleCHIIR 2021
CountryAustralia
CityVirtual
Period14/03/2119/03/21

Keywords

  • query expansion
  • search as learning
  • search interface
  • task oriented search
  • user interface

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