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SALIENT: Search as Learning – Investigating, Enhancing and Predicting Learning during Multimodal Web Search

WorkgroupMultimodal Interaction
Knowledge Construction
Duration05/2018 - 10/2021
FundingLeibniz Association, funding line "Cooperative Excellence" of the 2018 Leibniz Competition
Project description

The Internet has become indispensable when it comes to searching for information. Such an information search can be understood as a self-regulated learning process: Users of the Internet are expected to construct knowledge from what they find in the seemingly endless sea of data. The SALIENT project contributed to a better understanding of search as learning and developed methods to support the acquisition of knowledge through the Internet using ranking and retrieval algorithms.

While previous research on information retrieval has focused on information seekers' information needs, aspects such as learners' prior knowledge, and their learning intentions have received rather little attention (Hoppe et al., 2018). The SALIENT project contributed to closing this gap and developed a theoretical framework model to describe information search on the Internet as knowledge search (search as learning). In cooperation with the German National Library of Science and Technology (TIB) and the research center L3S, methods were developed to predict learning intentions and existing knowledge from user behavior during an Internet search (e.g., Yu et al., 2018; Shi et al, 2020). These methods were used to support Internet users in their knowledge acquisition. Particular focus was placed on learning with multimodal resources (Pardi et al., 2020) and on the possible emergence of "false security" during such self-regulated learning processes (von Hoyer et al., 2019).

  • Leibniz Information Centre for Science and Technology and University Library (TIB)

  • L3S Research Center

  • Leibniz-Institut für Sozialwissenschaft GESIS


Project Website: SALIENT

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Pardi, G., von Hoyer, J., Holtz, P., & Kammerer, Y. (2020). The role of cognitive abilities and time spent on texts and videos in a multimodal Searching as Learning task. In H. O’Brien, L. Freund, I. Arapakis, O. Hoeber, & I. Lopatovska (Eds.), Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (pp. 378-382). New York, NY: ACM.

Shi, J., Otto, C., Hoppe, A., Holtz, P., & Ewerth, R. (2019). Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality. Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information (pp. 11-19). ACM.

von Hoyer, J., Pardi, G., Kammerer, Y., & Holtz, P. (2019). Metacognitive judgments in Searching as Learning (SAL) tasks insights on (mis-) calibration, multimedia usage, and confidence. Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information (pp. 3-10). ACM.

Hoppe, A., Holtz, P., Kammerer, Y., Yu, R., Dietze, S., & Ewerth, R. (2018). Current Challenges for Studying Search as Learning Processes. In S. Dietze, M. D’Aquin, D. Gasevic, E. Herder, & J. Kimmerle (Eds.), Proceedings of the 7th Workshop on Learning & Education with Web Data (LILE2018) in conjunction with ACM Web Science 2018 (WebSci18) (pp. 19-22). Amsterdam: VU.

Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., & Dietze, S. (2018). Predicting User Knowledge Gain in Informational Search Sessions. In K. Collins-Thompson & Q. Mei (Eds.), Proceedings of SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 75-84). New York: ACM.