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Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights

Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more spe...

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Autores principales: Papakostas, Christos, Troussas, Christos, Krouska, Akrivi, Sgouropoulou, Cleo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502520/
https://www.ncbi.nlm.nih.gov/pubmed/36146410
http://dx.doi.org/10.3390/s22187059
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author Papakostas, Christos
Troussas, Christos
Krouska, Akrivi
Sgouropoulou, Cleo
author_facet Papakostas, Christos
Troussas, Christos
Krouska, Akrivi
Sgouropoulou, Cleo
author_sort Papakostas, Christos
collection PubMed
description Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students’ cognitive skills. Students’ data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes.
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spelling pubmed-95025202022-09-24 Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights Papakostas, Christos Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Sensors (Basel) Article Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students’ cognitive skills. Students’ data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes. MDPI 2022-09-18 /pmc/articles/PMC9502520/ /pubmed/36146410 http://dx.doi.org/10.3390/s22187059 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Papakostas, Christos
Troussas, Christos
Krouska, Akrivi
Sgouropoulou, Cleo
Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title_full Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title_fullStr Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title_full_unstemmed Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title_short Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
title_sort personalization of the learning path within an augmented reality spatial ability training application based on fuzzy weights
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502520/
https://www.ncbi.nlm.nih.gov/pubmed/36146410
http://dx.doi.org/10.3390/s22187059
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