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.
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. | |
License of this version: | CC BY 4.0 Unported |
Document Type: | BookPart |
Publishing status: | publishedVersion |
Issue Date: | 2021 |
Appears in Collections: | Zentrale Einrichtungen Forschungszentren |
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2 | Germany | 2 | 25.00% | |
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4 | France | 1 | 12.50% | |
5 | Switzerland | 1 | 12.50% |
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