SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search

Otto, Christian; Rokicki, Markus; Pardi, Georg; Gritz, Wolfgang; Hienert, Daniel; Yu, Ran; Hoyer, Johannes von; Hoppe, Anett; Dietze, Stefan; Holtz, Peter; Kammerer, Yvonne; Ewerth, Ralph

doi:10.1145/3498366.3505835

Cited by 3 publications

(1 citation statement)

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“…Future work might include identifying a method to learn the user's target knowledge during a search session, providing the opportunity to evaluate 𝑅𝑈 𝐿𝐾 framework against other models across diverse datasets that encompass different types of knowledge assessment tests, such as [24].…”

Section: Discussionmentioning

confidence: 99%

RULKKG: Estimating User’s Knowledge Gain in Search-as-Learning Using Knowledge Graphs

Nasser,

El Zein,

da Costa Pereira

et al. 2024

Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval

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In the context of search as learning, users engage in search sessions to fill their information gaps and achieve their learning goals.Tracking the user's state of knowledge is therefore essential for estimating how close they are to achieve these learning goals. In this respect, we extend a recently proposed approach that uses the recognition of entities present in the text to track the user's knowledge.Our approach introduces a more complete representation by considering both the entities and their relations. More precisely, we represent both the user's knowledge and the user's learning goals (or target knowledge) as knowledge graphs.We show that the proposed representation captures a complementary aspect of knowledge, thus helping to improve the user knowledge gain estimation when used in combination with other representations.

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Section: Discussionmentioning

confidence: 99%