Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CHIIR '22 |
| Untertitel | Proceedings of the 2022 Conference on Human Information Interaction and Retrieval |
| Seiten | 347-352 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781450391863 |
| Publikationsstatus | Veröffentlicht - 14 März 2022 |
| Veranstaltung | CHIIR 2022: ACM SIGIR Conference on Human Information Interaction and Retrieval - Virtual, Online, Deutschland Dauer: 14 März 2022 → 18 März 2022 |
Publikationsreihe
| Name | CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval |
|---|
Abstract
The emerging research field Search as Learning (SAL) investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired knowledge in order to obtain conclusive insights or train supervised machine learning models. However, the creation of such datasets is costly and requires interdisciplinary efforts in order to design studies and capture a wide range of features. In this paper, we address this issue and introduce an extensive dataset based on a user study, in which 114 participants were asked to learn about the formation of lightning and thunder. Participants' knowledge states were measured before and after Web search through multiple-choice questionnaires and essay-based free recall tasks. To enable future research in SAL-related tasks we recorded a plethora of features and person-related attributes. Besides the screen recordings, visited Web pages, and detailed browsing histories, a large number of behavioral features and resource features were monitored. We underline the usefulness of the dataset by describing three, already published, use cases.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Information systems
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- BibTex
- RIS
CHIIR '22: Proceedings of the 2022 Conference on Human Information Interaction and Retrieval. 2022. S. 347-352 (CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - SaL-Lightning Dataset
T2 - CHIIR 2022
AU - Otto, Christian
AU - Rokicki, Markus
AU - Pardi, Georg
AU - Gritz, Wolfgang
AU - Hienert, Daniel
AU - Yu, Ran
AU - Hoyer, Johannes von
AU - Hoppe, Anett
AU - Dietze, Stefan
AU - Holtz, Peter
AU - Kammerer, Yvonne
AU - Ewerth, Ralph
N1 - Funding Information: This work is financially supported by the Leibniz Association, Germany (Leibniz Competition 2018, funding line "Collaborative Excellence", project SALIENT [K68/2017]).
PY - 2022/3/14
Y1 - 2022/3/14
N2 - The emerging research field Search as Learning (SAL) investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired knowledge in order to obtain conclusive insights or train supervised machine learning models. However, the creation of such datasets is costly and requires interdisciplinary efforts in order to design studies and capture a wide range of features. In this paper, we address this issue and introduce an extensive dataset based on a user study, in which 114 participants were asked to learn about the formation of lightning and thunder. Participants' knowledge states were measured before and after Web search through multiple-choice questionnaires and essay-based free recall tasks. To enable future research in SAL-related tasks we recorded a plethora of features and person-related attributes. Besides the screen recordings, visited Web pages, and detailed browsing histories, a large number of behavioral features and resource features were monitored. We underline the usefulness of the dataset by describing three, already published, use cases.
AB - The emerging research field Search as Learning (SAL) investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired knowledge in order to obtain conclusive insights or train supervised machine learning models. However, the creation of such datasets is costly and requires interdisciplinary efforts in order to design studies and capture a wide range of features. In this paper, we address this issue and introduce an extensive dataset based on a user study, in which 114 participants were asked to learn about the formation of lightning and thunder. Participants' knowledge states were measured before and after Web search through multiple-choice questionnaires and essay-based free recall tasks. To enable future research in SAL-related tasks we recorded a plethora of features and person-related attributes. Besides the screen recordings, visited Web pages, and detailed browsing histories, a large number of behavioral features and resource features were monitored. We underline the usefulness of the dataset by describing three, already published, use cases.
KW - Knowledge Gain
KW - User Study
KW - Web Learning
UR - http://www.scopus.com/inward/record.url?scp=85127392073&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2201.02339
DO - 10.48550/arXiv.2201.02339
M3 - Conference contribution
T3 - CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
SP - 347
EP - 352
BT - CHIIR '22
Y2 - 14 March 2022 through 18 March 2022
ER -