Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
| Seiten | 75-84 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9781450356572 |
| Publikationsstatus | Veröffentlicht - 27 Juni 2018 |
| Veranstaltung | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, USA / Vereinigte Staaten Dauer: 8 Juli 2018 → 12 Juli 2018 |
Publikationsreihe
| Name | 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
|---|
Abstract
Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives. In this context, informational search missions with an intention to obtain knowledge pertaining to a topic are prominent. The importance of learning as an outcome of web search has been recognized. Yet, there is a lack of understanding of the impact of web search on a user's knowledge state. Predicting the knowledge gain of users can be an important step forward if web search engines that are currently optimized for relevance can be molded to serve learning outcomes. In this paper, we introduce a supervised model to predict a user's knowledge state and knowledge gain from features captured during the search sessions. To measure and predict the knowledge gain of users in informational search sessions, we recruited 468 distinct users using crowdsourcing and orchestrated real-world search sessions spanning 11 different topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Our supervised models utilise and derive a comprehensive set of features from the current state of the art and compare performance of a range of feature sets and feature selection strategies. Through our results, we demonstrate the ability to predict and classify the knowledge state and gain using features obtained during search sessions, exhibiting superior performance to an existing baseline in the knowledge state prediction task.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Information systems
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- BibTex
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41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018. 2018. S. 75-84 (41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Predicting user knowledge gain in informational search sessions
AU - Yu, Ran
AU - Gadiraju, Ujwal
AU - Holtz, Peter
AU - Rokicki, Markus
AU - Kemkes, Philipp
AU - Dietze, Stefan
N1 - Publisher Copyright: © 2018 ACM.
PY - 2018/6/27
Y1 - 2018/6/27
N2 - Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives. In this context, informational search missions with an intention to obtain knowledge pertaining to a topic are prominent. The importance of learning as an outcome of web search has been recognized. Yet, there is a lack of understanding of the impact of web search on a user's knowledge state. Predicting the knowledge gain of users can be an important step forward if web search engines that are currently optimized for relevance can be molded to serve learning outcomes. In this paper, we introduce a supervised model to predict a user's knowledge state and knowledge gain from features captured during the search sessions. To measure and predict the knowledge gain of users in informational search sessions, we recruited 468 distinct users using crowdsourcing and orchestrated real-world search sessions spanning 11 different topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Our supervised models utilise and derive a comprehensive set of features from the current state of the art and compare performance of a range of feature sets and feature selection strategies. Through our results, we demonstrate the ability to predict and classify the knowledge state and gain using features obtained during search sessions, exhibiting superior performance to an existing baseline in the knowledge state prediction task.
AB - Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives. In this context, informational search missions with an intention to obtain knowledge pertaining to a topic are prominent. The importance of learning as an outcome of web search has been recognized. Yet, there is a lack of understanding of the impact of web search on a user's knowledge state. Predicting the knowledge gain of users can be an important step forward if web search engines that are currently optimized for relevance can be molded to serve learning outcomes. In this paper, we introduce a supervised model to predict a user's knowledge state and knowledge gain from features captured during the search sessions. To measure and predict the knowledge gain of users in informational search sessions, we recruited 468 distinct users using crowdsourcing and orchestrated real-world search sessions spanning 11 different topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Our supervised models utilise and derive a comprehensive set of features from the current state of the art and compare performance of a range of feature sets and feature selection strategies. Through our results, we demonstrate the ability to predict and classify the knowledge state and gain using features obtained during search sessions, exhibiting superior performance to an existing baseline in the knowledge state prediction task.
KW - Knowledge gain
KW - Search as learning
KW - User modeling
KW - Web search
UR - http://www.scopus.com/inward/record.url?scp=85051476814&partnerID=8YFLogxK
U2 - 10.1145/3209978.3210064
DO - 10.1145/3209978.3210064
M3 - Conference contribution
AN - SCOPUS:85051476814
T3 - 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
SP - 75
EP - 84
BT - 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
T2 - 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
Y2 - 8 July 2018 through 12 July 2018
ER -