On the Impact of Features and Classifiers for Measuring Knowledge Gain during Web Search - A Case Study

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Date
2021
Volume
3052
Issue
Journal
Series Titel
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Publisher
Aachen, Germany : RWTH Aachen
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Abstract

Search engines are normally not designed to support human learning intents and processes. The ÿeld 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 in˝uence of several text-based features and classiÿers 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 in˝uence 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.

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Keywords
Textual Complexity, Knowledge Gain, Search as Learning, Learning Resources, Web-based Learning, Konferenzschrift
Citation
Gritz, W., Hoppe, A., & Ewerth, R. (2021). On the Impact of Features and Classifiers for Measuring Knowledge Gain during Web Search - A Case Study (G. Cong & M. Ramanath, eds.). Aachen, Germany : RWTH Aachen.
License
CC BY 4.0 Unported