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@PHDTHESIS{Hensen:1020761,
author = {Hensen, Benedikt},
othercontributors = {Decker, Stefan Josef and Klemke, Roland},
title = {{D}esigning effective mixed reality learning experiences:
structuring insights with knowledge graphs and large
language models},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-09217},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {Mixed Reality (MR), a technology that merges the digital
and real world, offers significant opportunities to enhance
learning experiences by providing immersive 3D environments,
visualizing complex workflow processes and enabling seamless
collaborative interactions. However, the literature mainly
contains isolated, context-specific case studies about a
certain topic and course, limiting the applicability of
insights about MR-enhanced learning for diverse educational
scenarios. Instead, granular considerations are helpful that
take into account factors about the learning setting like
the number of students, the characteristics of the course,
the theoretical or practical focus of the learning goals, a
remote or co-located setting, etc. Consequently, there
exists a need for educators and students to utilize a
structured knowledge base that guides them in integrating MR
into learning. While navigating around its limitations, they
can then benefit from the strength and opportunities of the
MR technology. This dissertation aims to address this gap by
systematically exploring approaches to integrate MR into
learning and documenting the findings in a knowledge graph.
By following the design science research methodology,
solutions are developed for educators which can be
incorporated into their existing practices. The process
consists of several steps that build up to the final
knowledge base. The methodology starts with a comprehensive
literature review that defines the solution space based on
the characteristics of MR learning applications. Then, an
infrastructure is established that enables efficient
development of MR artifacts. These MR learning artifacts are
constructed and evaluated in iterations. Finally, an MR
learning ontology is formulated to structure the gathered
information, and the insights from the studies are recorded
in a knowledge graph. The outcomes are achieved in a
scalable, innovative process using an Artificial
Intelligence (AI)-in-the loop process. A locally hosted
Large Language Model (LLM) on consumer-grade hardware
extracts the argumentations about design rationale from the
numerous own research studies and from examined publications
and adds them to an editable knowledge graph which can be
adjusted by a human author. By providing a knowledge base
that reflects the expertise, it enables effective
decision-making for educators and students about integrating
MR in learning activities. The knowledge base can be updated
with the same LLM-driven process to continuously reflect
state-of-the-art data. With the results of the dissertation,
educators and students can benefit from a flexible query
system on the knowledge base that provides the filtered
information according to their use case characteristics.},
cin = {124510},
ddc = {004},
cid = {$I:(DE-82)124510_20160614$},
pnm = {BMBF 16DHB2213 - Verbundprojekt: Personalisierte
Kompetenzentwicklung und hybrides KI-Mentoring -
tech4compKI; Teilvorhaben: Verteilte Datenanalyse zur
Bestimmung von Personmerkmalen (16DHB2213)},
pid = {G:(BMBF)16DHB2213},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-09217},
url = {https://publications.rwth-aachen.de/record/1020761},
}
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