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International Conference on Web-Based Learning

ICWL 2019: Advances in Web-Based Learning – ICWL 2019 pp 229–240Cite as

Visualizing Search History in Web Learning

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11841))

Abstract

Search history visualization provides a medium to organize and quickly re-find information in searching. Scientific studies show that a good visualization of a user search history should not only present the explicit activities represented by search queries and answers but also depict the latent information exploration process in the searcher’s mind. In this paper, we propose the LogCanvasTag platform for search history visualization. In comparison to existing work, we focus more on helping searchers to re-construct the semantic relationship among their search activities. We segment a user’s search history into different sessions and use a knowledge graph to represent the searching process in each of the sessions. The knowledge graph consists of all queries and important related concepts as well as their relationships and the topics extracted from the search results of each query. Especially to help searchers not get lost in complicated history graph, we provide a function wherein sub-graphs can be extracted for each topic from the session graph for deeper insights. We also provide a collaborative perspective to support a group of users in sharing search activities and experience. Our experimental results indicate that searching experience of both independent users and collaborative searching groups benefit from this search history visualization. We present novel insights into the factors of graph-based search history visualization that help in quick information re-finding.

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Notes

  1. 1.

    https://learnweb.l3s.uni-hannover.de/.

  2. 2.

    https://onlinelibrary.wiley.com/doi/full/10.1002/pra2.2016.14505301122.

  3. 3.

    https://github.com/felipemoraes/searchx.

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Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz University of Hannover, Hannover, Germany

    Tetiana Tolmachova, Ivana Marenzi & Ujwal Gadiraju

  2. Renmin University of China, Beijing, China

    Luyan Xu

Authors
  1. Tetiana Tolmachova
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  2. Luyan Xu
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  3. Ivana Marenzi
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  4. Ujwal Gadiraju
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Corresponding author

Correspondence to Tetiana Tolmachova .

Editor information

Editors and Affiliations

  1. Magdeburg-Stendal University of Applied Sciences, Magdeburg, Germany

    Michael A. Herzog

  2. Comenius University in Bratislava, Bratislava, Slovakia

    Zuzana Kubincová

  3. Chongqing Academy of Science and Technology, Chongqing, China

    Peng Han

  4. Sapienza University, Rome, Italy

    Marco Temperini

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Tolmachova, T., Xu, L., Marenzi, I., Gadiraju, U. (2019). Visualizing Search History in Web Learning. In: Herzog, M., Kubincová, Z., Han, P., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2019. ICWL 2019. Lecture Notes in Computer Science(), vol 11841. Springer, Cham. https://doi.org/10.1007/978-3-030-35758-0_21

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  • DOI: https://doi.org/10.1007/978-3-030-35758-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35757-3

  • Online ISBN: 978-3-030-35758-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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