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Mixed Reality Learning Visualizations Using Knowledge Graphs

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Advances in Web-Based Learning – ICWL 2023 (ICWL 2023)

Abstract

Learners have limited methods such as mind maps to gain an overview of the structure and key facts in their learning material. Knowledge Graphs (KGs) are a more powerful way of representing information on a learning topic since computers can also work with encoded knowledge and can help populate the graph with facts. However, visualizing the content of a KG to make it understandable and applicable for learning is a challenging task. We investigated the effect that different node representations of 3D Mixed Reality (MR) visualizations generated from KG data have on the learning behavior, memorability and user acceptance of such novel educational applications. For this, we developed an open-source software artifact that converts KGs about learning materials into three-dimensional networks which students can explore in a MR environment. The results of the study show that users prefer node visualizations in the graph that convey a visual identity in the form of images as opposed to merely displaying nodes as spheres. This preference is supported by the measured memorization, usability and technology acceptance values during the study. These insights help find an effective visualization of KGs for learning and understanding systematic information with their semantic interconnections in MR.

The research leading to these results has received funding from the German Federal Ministry of Education and Research (BMBF) through the project “Personalisierte Kompetenzentwicklung und hybrides KI-Mentoring” (tech4compKI) (grant no. 16DHB2213).

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Notes

  1. 1.

    https://github.com/rwth-acis/MR-KGV.

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Correspondence to Benedikt Hensen .

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Hensen, B., Rechtmann, A., Neumann, A.T. (2023). Mixed Reality Learning Visualizations Using Knowledge Graphs. In: Xie, H., Lai, CL., Chen, W., Xu, G., Popescu, E. (eds) Advances in Web-Based Learning – ICWL 2023. ICWL 2023. Lecture Notes in Computer Science, vol 14409. Springer, Singapore. https://doi.org/10.1007/978-981-99-8385-8_12

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  • DOI: https://doi.org/10.1007/978-981-99-8385-8_12

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  • Print ISBN: 978-981-99-8384-1

  • Online ISBN: 978-981-99-8385-8

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