Determining Significant Classification Factors for Senior Learning: A Case Study of Thai Seniors and Social Media Skill Learning

Boontasri, Kanchana; Temdee, Punnarumol

doi:10.1007/s11277-020-07216-2

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…[ 8 ]: Digital 3D technologies for humanities research and education → [ 13 ]: VR education support System; [ 22 ]: Mobile learning as the key to higher education innovation; [ 23 ]: Determining significant classification factors for senior learning; [ 41 ]: 3D reconstruction, digital twinning, and virtual reality: Architectural heritage applications → [ 8 , 13 , 22 , 23 , 41 ];…”

Section: Results On Dt Types Emerging Technologies and Sensor Classesmentioning

confidence: 99%

“…Based on a reference corpus resulting from a search for scientific publications in books, journals and conference papers on the keywords virtual museums , digital twins , emerging technologies and sensor in leading science databases [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], we classify DTs according to their application areas, relevant technologies and concepts depending on type, focus and features, sensor technology used and adaptation or innovation power throughout their life cycle. Design, realization, operation and evolution are guided by experts, engineers, customers, curators, software engineers and visitors, all of whom define adequate quality criteria (QC) and metrics (QM) for DTs and ViMs V&VA.…”

Section: Introductionmentioning

confidence: 99%

“…In this review paper, we examine prominent science databases for occurrences of the terms virtual, museum, digital, twin, emerging, technologies, and sensor in journals, book chapters, conference papers and ePublications. Recent searches in Scopus, IEEEXplore, and Google Scholar with the search string “virtual AND museum AND digital AND twin AND emerging AND technologies AND sensor” yield 43 relevant publications [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ] from the last 13 years, 34 of which were in Scopus, 3 of which were excluded because the keywords were used in other meanings, such as “Siamese twins” (cf. Figure 1 and Figure A1 in the Appendix A ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview

Luther

Baloian

Biella

et al. 2023

Sensors

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This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT’s life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT—content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies—mostly still under development—and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers’/visitors’ engagement. It advocates for stakeholders’ collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT’s entire life cycle.

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Section: Results On Dt Types Emerging Technologies and Sensor Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview

Luther

Baloian

Biella

et al. 2023

Sensors

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Simulation-based digital twins monitoring: an approach focused on models’ accreditation

Santos

Campos

Montevechi

et al. 2022

Int J Adv Manuf Technol

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Digital Twin simulation models: a validation method based on machine learning and control charts

Santos

Campos

Montevechi

et al. 2023

International Journal of Production Research

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This code allows monitoring digital twin accuracies using k-NN classifiers. It carries out the following steps:1. Import real and digital twin datasets from excel files. 2. Scale data using Robust Scaler. 3. Classify real and digital twin data using k-NN and cross-validation (and defines the optimal number of neighbors, i.e., 'k') 4. Calculate p-chart control limits for k-NN accuracies and check for out-of-control points. 5. Plot p-chart for k-NN accuracies. Rationale:We fit k-NN classifiers to separate real and digital twin observations. The more accurate the digital twin, the more difficult it will be for the k-NN to separate both groups of observations. If the null hypothesis (H0: real and digital twin data come from the same distribution) is true, the best decision k-NN can take is to classify each observation at random. In this case, k-NN accuracies will remain around 50% (for balanced real/digital twin datasets). However, if real and digital twin data come from totally different distributions, the k-NN classification accuracy may reach up to 100%. So it is important to keep in mind that a k-NN accuracy of 50% indicates a very accurate digital twin, while a k-NN accuracy of 100% indicates a very innacurate digital twin.

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Determining Significant Classification Factors for Senior Learning: A Case Study of Thai Seniors and Social Media Skill Learning

Cited by 6 publications

References 44 publications

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview

Simulation-based digital twins monitoring: an approach focused on models’ accreditation

Digital Twin simulation models: a validation method based on machine learning and control charts

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