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Identifier |
000434161 |
Title |
An analytics algorithm for performance assessment in VR training |
Alternative Title |
Ένας αλγόριθμος αναλυτικών στοιχείων για αξιολόγηση της απόδοσης εκμάθησης στην εικονική πραγματικότητα |
Author
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Κέντρος, Μιχαήλ Γ.
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Thesis advisor
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Παπαγιαννάκης, Γεώργιος
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Reviewer
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Τζίτζικας, Ιωάννης
Ρούσσος, Αναστάσιος
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Abstract |
Virtual Reality (VR) hardware and software solutions are rapidly evolving, providing developers
with innovative technologies and tools to build applications, in the form of educational virtual experiences.
In addition, the popularity of learning and training through such realistic and low-cost VR simulations is
growing. Correspondingly, computer graphics and developers utilize the existing VR tools in combination
with 3D game engines to create VR immersive experiences tailored around education. However, the current
platforms for 3D interactive environments focus on producing mostly embedded gamification tools that
serve the entertaining capabilities of VR technology.
Task performance assessment is a vital part of the learning process, and by providing valuable
feedback it guides the learner towards improvement. In this thesis we addressed this issue, by developing a
platform and algorithms that enable developers to accurately, rapidly and systematically author the
automatic task performance assessment process of VR training scenarios. We introduce three generalized
components, non-dependent to the context of the VR simulation, a) the VR analytics assessment framework,
b) a Machine Learning (ML) algorithm capable of VR assessment and c) the VR Session Logger. In more
detail, our analytics assessment framework utilizes user analytics for computing the user’s score through
an authoring tool for defining performance evaluation rules, whereas by employing supervised ML, our
agent is capable of learning these rules directly from a subject matter expert’s (SME) VR data. Furthermore,
we present our novel algorithm for logging accurately VR sessions by recording the user’s movement and
tracking the resulting effects.
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Language |
English |
Subject |
Logging |
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Machine learning |
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Task assessment |
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Virtual reality |
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Αναλυτικά στοιχεία |
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Καταγραφή δεδομένων |
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Μηχανική μάθηση |
Issue date |
2020-11-27 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/7/7/7/metadata-dlib-1606204178-945473-16958.tkl
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Views |
581 |