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Journal of Interactive Media

Editor-in-Chief: Ziegler, Jürgen

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Volume 16, Issue 1


Orientation and Navigation Support in Resource Spaces Using Hierarchical Visualizations

Sebastian Gross / Marcel Kliemannel / Niels Pinkwart
Published Online: 2017-04-05 | DOI: https://doi.org/10.1515/icom-2016-0043


In this article we investigate how orientation and navigation in (extensive) spaces consisting of digital resources can be supported by using hierarchical visualizations. Such spaces can consist of heterogeneous sets of digital resources as for instance articles from Wikipedia, textbooks, and videos. Due to easier access to digital resources in the Internet age, a manual exploration of these spaces might lead to information overload. As a result, techniques need to be developed in order to automatically analyze and structure sets of resources. We introduce a prototypical implementation of a visualization pipeline that extracts information dimensions from resources in order to group them into semantically similar clusters, and visualizes these clusters using two different visualizations: a treemap visualizing clusters and nested subclusters, and a rooted tree visualizing groups of semantically similar resources as subtrees. In a lab study we evaluated the two visualizations and compared them to two control groups. The results may hint to users’ better understanding of the resources’ underlying knowledge as compared to using typical approaches (e.g. web search results as list) when using hierarchical visualizations.

Keywords: Visualization; Resource Space


  • [1]

    J. C. Nesbit and O. O. Adesope. Learning with concept and knowledge maps: A meta-analysis. Review of educational research, 76(3):413–448, 2006.CrossrefGoogle Scholar

  • [2]

    K. M. Markham, J. J. Mintzes, and M. G. Jones. The concept map as a research and evaluation tool: Further evidence of validity. Journal of research in science teaching, 31(1):91–101, 1994.CrossrefGoogle Scholar

  • [3]

    J. D. Novak and A. J. Cañas. The theory underlying concept maps and how to construct them. Florida Institute for Human and Machine Cognition, 1:2006–2001, 2006.Google Scholar

  • [4]

    A. J. Cañas and J. D. Novak. Concept mapping using CmapTools to enhance meaningful learning. In Knowledge Cartography: Software Tools and Mapping Techniques, pages 25–46. Springer London, London, 2008.Google Scholar

  • [5]

    S. R. Alpert. Comprehensive mapping of knowledge and information resources: The case of Webster. In Knowledge and Information Visualization: Searching for Synergies, pages 220–237. Springer Berlin Heidelberg, Berlin, Heidelberg, 2005.Google Scholar

  • [6]

    R. Zeiliger and L. Esnault. The constructivist mapping of Internet information at work with Nestor. In Knowledge cartography, pages 89–111. Springer, 2014.Google Scholar

  • [7]

    N.-S. Chen, C.-W. Wei, and H.-J. Chen. Mining e-learning domain concept map from academic articles. Computers & Education, 50(3):1009–1021, 2008.CrossrefWeb of ScienceGoogle Scholar

  • [8]

    T.-C. Hsieh and T. I. Wang. A mining-based approach on discovering courses pattern for constructing suitable learning path. Expert systems with applications, 37(6):4156–4167, 2010.CrossrefWeb of ScienceGoogle Scholar

  • [9]

    C. C. Aggarwal and C. X. Zhai. A survey of text clustering algorithms. In Mining text data, pages 77–128. Springer, 2012.Google Scholar

  • [10]

    S. K. Card, J. D. Mackinlay, and B. Shneiderman. Readings in information visualization: using vision to think. Morgan Kaufmann, 1999.Google Scholar

  • [11]

    C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Information Retrieval, 13(2):192–195, 2010.Google Scholar

  • [12]

    J. Leskovec, A. Rajaraman, and J. David Ullman. Mining of massive datasets. Cambridge University Press, 2014.Google Scholar

  • [13]

    A. Huang. Similarity measures for text document clustering. In Proceedings of the sixth New Zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand, pages 49–56, 2008.Google Scholar

  • [14]

    D. Holten. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on visualization and computer graphics, 12(5):741–748, 2006.CrossrefGoogle Scholar

  • [15]

    J. Heer, M. Bostock, and V. Ogievetsky. A tour through the visualization zoo. Communications of the ACM, 53(6):59–67, June 2010.Web of ScienceCrossrefGoogle Scholar

  • [16]

    R. Spence. Information visualization, volume 1. Springer, 2001.Google Scholar

  • [17]

    S. Gross and N. Pinkwart. Towards an integrative learning environment for Java programming. In D. G. Sampson, R. Huang, G.-J. Hwang, T.-Z. Liu, N.-S. Chen, Kinshuk, and C.-C. Tsai, editors, IEEE 15th International Conference on Advanced Learning Technologies (ICALT), pages 24–28, Los Alamitos, CA, July 2015. IEEE Computer Society Press, 2015.Google Scholar

  • [18]

    B. Mokbel, S. Gross, B. Paassen, N. Pinkwart, and B. Hammer. Domain-independent proximity measures in intelligent tutoring systems. In S. K. D’Mello, R. A. Calvo, and A. Olney, editors, Proceedings of the 6th International Conference on Educational Data Mining (EDM), pages 334–335, Memphis, TN, 2013.Google Scholar

  • [19]

    S. Gross, B. Mokbel, B. Paassen, B. Hammer, and N. Pinkwart. Example-based feedback provision using structured solution spaces. International Journal of Learning Technology, 9(3):248–280, 2014.CrossrefGoogle Scholar

About the article

Sebastian Gross

Sebastian Gross received his Diploma degree in Business Information Systems from Clausthal University of Technology. He joined the research group ‘Human-Centered Information Systems’ headed by Prof. Pinkwart in October 2011. In July 2013, he followed Prof. Pinkwart to Humboldt-Universität zu Berlin where he works in the research project ‘Learning dynamic feedback in intelligent tutoring systems’ (DynaFIT).

Marcel Kliemannel

Marcel Kliemannel studied computer science at Humboldt-Universität zu Berlin, and graduated in 2016. In his Master’s thesis he investigated techniques for visualizing knowledge spaces and strategies for supporting navigation in multi-dimensional visualizations.

Niels Pinkwart

Niels Pinkwart studied Computer Science and Mathematics at the University of Duisburg-Essen, where he also completed his PhD in 2005. Since 2013, he is Professor at Humboldt-Universität zu Berlin where he heads a research group which investigates Computer Science in Education and Society.

Published Online: 2017-04-05

Published in Print: 2017-04-01

Funding Source: Deutsche Forschungsgemeinschaft

Award identifier / Grant number: PI 764/6-2

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under the grant “DynaFIT – Learning Dynamic Feedback in Intelligent Tutoring Systems” (PI 764/6-2).

Citation Information: i-com, Volume 16, Issue 1, Pages 35–44, ISSN (Online) 2196-6826, ISSN (Print) 1618-162X, DOI: https://doi.org/10.1515/icom-2016-0043.

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