Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 46th European Conference on Information Retrieval, ECIR 2024 |
| Editors | Nazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, Iadh Ounis |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 364-373 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-031-56063-7 |
| ISBN (print) | 9783031560620 |
| Publication status | Published - 23 Mar 2024 |
| Event | 46th European Conference on Information Retrieval, ECIR 2024 - Glasgow, United Kingdom (UK) Duration: 24 Mar 2024 → 28 Mar 2024 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14610 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
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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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 proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On the Influence of Reading Sequences on Knowledge Gain During Web Search
AU - Gritz, Wolfgang
AU - Hoppe, Anett
AU - Ewerth, Ralph
N1 - Funding Information: Part of this work was financially supported by the Leibniz Association, Germany (Leibniz Competition 2023, funding line "Collaborative Excellence", project VideoSRS [K441/2022]).
PY - 2024/3/23
Y1 - 2024/3/23
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.
AB - 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.
KW - Eye-Tracking
KW - Knowledge Gain
KW - Reading
KW - Search as Learning
KW - Web Search
UR - http://www.scopus.com/inward/record.url?scp=85189362194&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2401.05148
DO - 10.48550/arXiv.2401.05148
M3 - Conference contribution
AN - SCOPUS:85189362194
SN - 9783031560620
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 364
EP - 373
BT - Advances in Information Retrieval
A2 - Goharian, Nazli
A2 - Tonellotto, Nicola
A2 - He, Yulan
A2 - Lipani, Aldo
A2 - McDonald, Graham
A2 - Macdonald, Craig
A2 - Ounis, Iadh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 46th European Conference on Information Retrieval, ECIR 2024
Y2 - 24 March 2024 through 28 March 2024
ER -