Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CIKM 2021 Workshops |
| Untertitel | Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021 - Gold Coast, Australien Dauer: 1 Nov. 2021 → 5 Nov. 2021 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 3052 |
| ISSN (Print) | 1613-0073 |
Abstract
Search engines are normally not designed to support human learning intents and processes. The field of Search as Learning (SAL) aims to investigate the characteristics of a successful Web search with a learning purpose. In this paper, we analyze the impact of text complexity of Web pages on predicting knowledge gain during a search session. For this purpose, we conduct an experimental case study and investigate the influence of several text-based features and classifiers on the prediction task. We build upon data from a study of related work, where 104 participants were given the task to learn about the formation of lightning and thunder through Web search. We perform an extensive evaluation based on a state-of-the-art approach and extend it with additional features related to textual complexity of Web pages. In contrast to prior work, we perform a systematic search for optimal hyperparameters and show the possible influence of feature selection strategies on the knowledge gain prediction. When using the new set of features, state-of-the-art results are noticeably improved. The results indicate that text complexity of Web pages could be an important feature resource for knowledge gain prediction.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- RIS
CIKM 2021 Workshops: Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). 2021. (CEUR Workshop Proceedings; Band 3052).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On the Impact of Features and Classifiers for Measuring Knowledge Gain during Web Search
T2 - 2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021
AU - Gritz, Wolfgang
AU - Hoppe, Anett
AU - Ewerth, Ralph
N1 - Funding Information: Part of this work is financially supported by the Leibniz Association, Germany (Leibniz Competition 2018, funding line "Collaborative Excellence", project SALIENT [K68/2017]).
PY - 2021
Y1 - 2021
N2 - Search engines are normally not designed to support human learning intents and processes. The field of Search as Learning (SAL) aims to investigate the characteristics of a successful Web search with a learning purpose. In this paper, we analyze the impact of text complexity of Web pages on predicting knowledge gain during a search session. For this purpose, we conduct an experimental case study and investigate the influence of several text-based features and classifiers on the prediction task. We build upon data from a study of related work, where 104 participants were given the task to learn about the formation of lightning and thunder through Web search. We perform an extensive evaluation based on a state-of-the-art approach and extend it with additional features related to textual complexity of Web pages. In contrast to prior work, we perform a systematic search for optimal hyperparameters and show the possible influence of feature selection strategies on the knowledge gain prediction. When using the new set of features, state-of-the-art results are noticeably improved. The results indicate that text complexity of Web pages could be an important feature resource for knowledge gain prediction.
AB - Search engines are normally not designed to support human learning intents and processes. The field of Search as Learning (SAL) aims to investigate the characteristics of a successful Web search with a learning purpose. In this paper, we analyze the impact of text complexity of Web pages on predicting knowledge gain during a search session. For this purpose, we conduct an experimental case study and investigate the influence of several text-based features and classifiers on the prediction task. We build upon data from a study of related work, where 104 participants were given the task to learn about the formation of lightning and thunder through Web search. We perform an extensive evaluation based on a state-of-the-art approach and extend it with additional features related to textual complexity of Web pages. In contrast to prior work, we perform a systematic search for optimal hyperparameters and show the possible influence of feature selection strategies on the knowledge gain prediction. When using the new set of features, state-of-the-art results are noticeably improved. The results indicate that text complexity of Web pages could be an important feature resource for knowledge gain prediction.
KW - Knowledge gain
KW - Learning resources
KW - Search as learning
KW - Textual complexity
KW - Web-based learning
UR - http://www.scopus.com/inward/record.url?scp=85122883259&partnerID=8YFLogxK
M3 - Conference contribution
T3 - CEUR Workshop Proceedings
BT - CIKM 2021 Workshops
Y2 - 1 November 2021 through 5 November 2021
ER -