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PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system

Christos Papakostas¹, Christos Troussas¹, Akrivi Krouska¹ and Cleo Sgouropoulou¹

1. Department of Informatics and Computer Engineering
   University of West Attica, Greece
   {cpapakostas, ctrouss, akrouska, csgouro}@uniwa.gr

Abstract

Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students’ spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students' motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students’ individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students' knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.

Key words

Fuzzy logic, augmented reality, spatial ability, adaptive training, personalized system

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230130043P

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Papakostas, C., Troussas, C., Krouska, A., Sgouropoulou, C.: PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS230130043P