dc.identifier.uri |
http://dx.doi.org/10.15488/16877 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/17004 |
|
dc.contributor.author |
Gritz, Wolfgang
|
|
dc.contributor.author |
Hoppe, Anett
|
|
dc.contributor.author |
Ewerth, Ralph
|
|
dc.contributor.editor |
Cong, Gao
|
|
dc.contributor.editor |
Ramanath, Maya
|
|
dc.date.accessioned |
2024-04-04T08:54:05Z |
|
dc.date.available |
2024-04-04T08:54:05Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Gritz, W.; Hoppe, A.; Ewerth, R.: On the Impact of Features and Classifiers for Measuring Knowledge Gain during Web Search - A Case Study. In: Cong, Gao; Ramanath, Maya (Eds.): CIKMW2021, CIKM 2021 workshops : proceedings of the CIKM 2021 workshops, co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). Aachen, Germany : RWTH Aachen, 2021 (CEUR Workshop Proceedings ; 3052), 6. |
|
dc.description.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. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Aachen, Germany : RWTH Aachen |
|
dc.relation.ispartof |
CIKMW2021, CIKM 2021 workshops : proceedings of the CIKM 2021 workshops, co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) |
|
dc.relation.ispartofseries |
CEUR Workshop Proceedings ; 3052 |
|
dc.relation.uri |
https://ceur-ws.org/Vol-3052/paper6.pdf |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Textual Complexity |
eng |
dc.subject |
Knowledge Gain |
eng |
dc.subject |
Search as Learning |
eng |
dc.subject |
Learning Resources |
eng |
dc.subject |
Web-based Learning |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
004 | Informatik
|
|
dc.subject.ddc |
020 | Bibliotheks- und Informationswissenschaft
|
|
dc.title |
On the Impact of Features and Classifiers for Measuring Knowledge Gain during Web Search - A Case Study |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
1613-0073 |
|
dc.bibliographicCitation.volume |
3052 |
|
dc.bibliographicCitation.firstPage |
6 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|