On the Influence of Reading Sequences on Knowledge Gain During Web Search

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Wolfgang Gritz
  • Anett Hoppe
  • Ralph Ewerth

Research Organisations

External Research Organisations

  • German National Library of Science and Technology (TIB)
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Details

Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication46th European Conference on Information Retrieval, ECIR 2024
EditorsNazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, Iadh Ounis
PublisherSpringer Science and Business Media Deutschland GmbH
Pages364-373
Number of pages10
ISBN (electronic)978-3-031-56063-7
ISBN (print)9783031560620
Publication statusPublished - 23 Mar 2024
Event46th European Conference on Information Retrieval, ECIR 2024 - Glasgow, United Kingdom (UK)
Duration: 24 Mar 202428 Mar 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14610 LNCS
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

Nowadays, learning increasingly involves the usage of search engines and web resources. The related interdisciplinary research field search as learning aims to understand how people learn on the web. Previous work has investigated several feature classes to predict, for instance, the expected knowledge gain during web search. Therein, eye-tracking features have not been extensively studied so far. In this paper, we extend a previously used line-based reading model to one that can detect reading sequences across multiple lines. We use publicly available study data from a web-based learning task to examine the relationship between our feature set and the participants’ test scores. Our findings demonstrate that learners with higher knowledge gain spent significantly more time reading, and processing more words in total. We also find evidence that faster reading at the expense of more backward regressions, i.e., re-reading previous portions of text, may be an indicator of better web-based learning. We make our code publicly available at https://github.com/TIBHannover/reading_web_search.

Keywords

    Eye-Tracking, Knowledge Gain, Reading, Search as Learning, Web Search

ASJC Scopus subject areas

Cite this

On the Influence of Reading Sequences on Knowledge Gain During Web Search. / Gritz, Wolfgang; Hoppe, Anett; Ewerth, Ralph.
Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. ed. / Nazli Goharian; Nicola Tonellotto; Yulan He; Aldo Lipani; Graham McDonald; Craig Macdonald; Iadh Ounis. Springer Science and Business Media Deutschland GmbH, 2024. p. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14610 LNCS).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Gritz, W, Hoppe, A & Ewerth, R 2024, On the Influence of Reading Sequences on Knowledge Gain During Web Search. in N Goharian, N Tonellotto, Y He, A Lipani, G McDonald, C Macdonald & I Ounis (eds), Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14610 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 364-373, 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, United Kingdom (UK), 24 Mar 2024. https://doi.org/10.48550/arXiv.2401.05148, https://doi.org/10.1007/978-3-031-56063-7_28
Gritz, W., Hoppe, A., & Ewerth, R. (2024). On the Influence of Reading Sequences on Knowledge Gain During Web Search. In N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, & I. Ounis (Eds.), Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024 (pp. 364-373). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14610 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.48550/arXiv.2401.05148, https://doi.org/10.1007/978-3-031-56063-7_28
Gritz W, Hoppe A, Ewerth R. On the Influence of Reading Sequences on Knowledge Gain During Web Search. In Goharian N, Tonellotto N, He Y, Lipani A, McDonald G, Macdonald C, Ounis I, editors, Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 364-373. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.48550/arXiv.2401.05148, 10.1007/978-3-031-56063-7_28
Gritz, Wolfgang ; Hoppe, Anett ; Ewerth, Ralph. / On the Influence of Reading Sequences on Knowledge Gain During Web Search. Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. editor / Nazli Goharian ; Nicola Tonellotto ; Yulan He ; Aldo Lipani ; Graham McDonald ; Craig Macdonald ; Iadh Ounis. Springer Science and Business Media Deutschland GmbH, 2024. pp. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Download
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abstract = "Nowadays, learning increasingly involves the usage of search engines and web resources. The related interdisciplinary research field search as learning aims to understand how people learn on the web. Previous work has investigated several feature classes to predict, for instance, the expected knowledge gain during web search. Therein, eye-tracking features have not been extensively studied so far. In this paper, we extend a previously used line-based reading model to one that can detect reading sequences across multiple lines. We use publicly available study data from a web-based learning task to examine the relationship between our feature set and the participants{\textquoteright} test scores. Our findings demonstrate that learners with higher knowledge gain spent significantly more time reading, and processing more words in total. We also find evidence that faster reading at the expense of more backward regressions, i.e., re-reading previous portions of text, may be an indicator of better web-based learning. We make our code publicly available at https://github.com/TIBHannover/reading_web_search.",
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AU - Hoppe, Anett

AU - Ewerth, Ralph

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N2 - Nowadays, learning increasingly involves the usage of search engines and web resources. The related interdisciplinary research field search as learning aims to understand how people learn on the web. Previous work has investigated several feature classes to predict, for instance, the expected knowledge gain during web search. Therein, eye-tracking features have not been extensively studied so far. In this paper, we extend a previously used line-based reading model to one that can detect reading sequences across multiple lines. We use publicly available study data from a web-based learning task to examine the relationship between our feature set and the participants’ test scores. Our findings demonstrate that learners with higher knowledge gain spent significantly more time reading, and processing more words in total. We also find evidence that faster reading at the expense of more backward regressions, i.e., re-reading previous portions of text, may be an indicator of better web-based learning. We make our code publicly available at https://github.com/TIBHannover/reading_web_search.

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