Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology
Abstract
:1. Introduction
- What are the factors that measure the training effectiveness? How can we distinguish and quantify the influence of these factors on training effectiveness?
- What kind of maintenance tasks are training based on VR, AR, and traditional methods suitable for and how to choose training media to achieve better training effectiveness?
- For the same kind of maintenance tasks, how does the training platform affect the trainees’ learning processes, resulting in better or worse training effectiveness?
- Which method can help reduce the correction effect of individual differences on training effectiveness during training?
2. Experimental Platform
2.1. Setting up Training Environment
2.2. Training Methods for Maintenance of Three Kinds of Platforms
2.3. Analysis of the Advantages and Disadvantages of Three Types of Platform Training
3. Experiment
3.1. Experimental Process
3.2. Pre-Test Experiment
- Screening out individuals with high technical affinity through questionnaires;
- An experiment to test individual’s cognitive ability aims to quantify the result of the comprehensive effect of individual factors.
3.2.1. Technical Affinity Test
- Number of AR/VR interactions;
- Number of times to learn about the mechanical maintenance system;
- Number of times to use the HMD device;
- Number of times you experience AR/VR games;
- Knowledge of AR/VR technology.
3.2.2. Measure Individual Cognitive Ability
- The cognitive ability measurement experiment is based on a crane maintenance task. The operational element Q in the experimental task includes three basic elements: Qr1~Qr8 (relays), Qcb1~Qcb3 (circuit breakers), and Qrs1~Qrs5 (rocker switches).
- As shown in Figure 7, the trainee needs to first select the task level to perform, which will start at level 1. Then, watch the operation demonstration, each step has a time limit of 5s, and then complete the assessment task in the assessment mode; the assessment data will be recorded by the system. There are two attempts for each person before moving to the next level without errors.
- The operation elements of each level are randomly arranged and combined by three basic elements of Qr, Qcb, and Qrs, the number of operation elements in level 1 is X1 = 3, the number of operation elements in level n is Xn = Xn−1 + 1, n ≥ 2.
- The final grades were all within the range of Level 2 to Level 5, and the number of steps Q completed by the trainee was recorded. It can be seen that Q is a continuous variable. It was set as a covariate in the one-way covariance analysis to reduce the influence caused by individual cognitive ability differences.
3.3. Training and Assessment Tasks
3.4. Post-Test
3.5. Quantization Method Based on Improved CPSI Model
- Touch by mistake;
- Wrong location;
- Missing a step;
- Operation error;
- Decision error;
- Repair task not completed.
3.6. Data Analysis
3.6.1. One-Way Analysis of Covariance for Time Parameters
3.6.2. Nonparametric Analysis of CPSI Score
3.6.3. One-Way Analysis of Covariance on Cognitive Load Scores
4. Results and Discussion
4.1. General Conclusions from the Analysis of Experimental Results
4.2. Limitations
4.3. Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Parameter Table in CPSI Model
Options and Scores | |||||
---|---|---|---|---|---|
Items | One Point | Two Points | Three Points | Four Points | Five Points |
Q1: How many times have you interacted with AR or VR? | 0 | 1 | 2 | 3 | >4 |
Q2: How many times have you studied mechanical maintenance systems? | 0 | 1 | 2 | 3 | >4 |
Q3: How many times have you experienced the head-mounted display device? | 0 | 1 | 2 | 3 | >4 |
Q4: How many times have you experienced AR or VR games? | 0 | 1 | 2 | 3 | >4 |
Q5: How well do you know AR or VR? | Not at all | Little | Not sure | Know something | Know it quite well |
Items | Task I | Task II | Task III |
---|---|---|---|
X | [1–3, 5] | [1–3, 5] | [1–3, 5] |
apt | 5 | 8 | 10 |
Appendix B. The Experimental Data Sheet That Needs to Be Explained
Descriptive Analysis | S-W Test | Levene Test | Slope Homogeneity Test | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Items | Average | SD | df | Sig. | F | Sig. | Ss(Ⅲ) | Ms | F | Sig. |
T11 T12 T13 | 35.689 28.817 37.789 | 3.196 2.918 3.877 | 20 | 0.678 0.174 0.644 | 0.604 | 0.550 | 21.714 | 10.857 | 0.966 | 0.387 |
20 | ||||||||||
20 | ||||||||||
T21 T22 T23 | 170.669 155.570 163.833 | 12.148 9.820 9.972 | 20 | 0.094 0.158 0.078 | 1.760 | 0.181 | 54.677 | 27.338 | 0.257 | 0.774 |
20 | ||||||||||
20 | ||||||||||
T31 T32 T33 | 172.439 175.486 156.697 | 20.347 27.491 20.862 | 20 | 0.218 0.989 0.082 | 0.819 | 0.446 | 701.595 | 350.797 | 0.704 | 0.499 |
20 | ||||||||||
20 |
Task I | Task II | Task III | ||||
---|---|---|---|---|---|---|
Groups | Mean | SD | Mean | SD | Mean | SD |
VR | 0.992 | 0.017 | 0.921 | 0.054 | 0.842 | 0.157 |
TT | 0.995 | 0.011 | 0.963 | 0.024 | 0.808 | 0.168 |
AR | 0.983 | 0.030 | 0.953 | 0.030 | 0.930 | 0.109 |
Descriptive Analysis | S-W Test | Levene Test | Slope Homogeneity Test | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Items | Average | SD | df | Sig. | F | Sig. | Ss(Ⅲ) | Ms | F | Sig. |
N11 N12 N13 | 100.50 92.75 103.85 | 25.673 19.950 22.885 | 20 20 20 | 0.417 0.309 0.517 | 0.980 | 0.382 | 619.685 | 309.843 | 0.588 | 0.559 |
N21 N22 N23 | 164.80 154.95 180.10 | 28.382 37.884 28.591 | 20 20 20 | 0.664 0.416 0.165 | 0.727 | 0.488 | 2.883 | 1.441 | 0.001 | 0.999 |
N31 N32 N33 | 212.85 206.75 189.80 | 30.505 26.929 23.539 | 20 20 20 | 0.506 0.646 0.482 | 0.424 | 0.656 | 3242.488 | 1621.244 | 2.302 | 0.110 |
References
- Gao, Y.; Gonzalez, V.A.; Yiu, T.W. The effectiveness of traditional tools and computer-aided technologies for health and safety training in the construction sector: A systematic review. Comput. Educ. 2019, 138, 101–115. [Google Scholar] [CrossRef] [Green Version]
- Daponte, P.; De Vito, L.; Picariello, F.; Riccio, M.J. State of the art and future developments of the Augmented Reality for measurement applications. Measurement 2014, 57, 53–70. [Google Scholar] [CrossRef]
- Qin, Z.; Tai, Y.; Xia, C.; Peng, J.; Huang, X.; Chen, Z.; Li, Q.; Shi, J. Towards Virtual VATS, Face, and Construct Evaluation for Peg Transfer Training of Box, VR, AR, and MR Trainer. J. Heal. Eng. 2019, 2019, 6813719. [Google Scholar] [CrossRef] [PubMed]
- Daling, L.M.; Schlittmeier, S.J. Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality-Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review. Hum. Factors 2022, 1–38. [Google Scholar] [CrossRef]
- Mao, C.C.; Chen, C.H. Augmented Reality of 3D Content Application in Common Operational Picture Training System for Army. Int. J. Hum. Comput. Interact. 2021, 37, 1899–1915. [Google Scholar] [CrossRef]
- Champney, R.; Lackey, S.J.; Stanney, K.; Quinn, S. Augmented Reality Training of Military Tasks: Reactions from Subject Matter Experts. In Virtual, Augmented and Mixed Reality; Springer: Berlin/Heidelberg, Germany, 2015; pp. 251–262. [Google Scholar]
- Abich, J.; Eudy, M.; Murphy, J.; Garneau, C.; Raby, Y.; Amburn, C. Use of the Augmented REality Sandtable (ARES) to Enhance Army CBRN Training. In Proceedings of the 20th International Conference on Human-Computer Interaction (HCI International), Las Vegas, NV, USA, 15–20 July 2018; pp. 223–230. [Google Scholar]
- Schaffernak, H.; Moesl, B.; Vorraber, W.; Braunstingl, R.; Herrele, T.; Koglbauer, I. Design and Evaluation of an Augmented Reality Application for Landing Training. In Human Interaction, Emerging Technologies and Future Applications IV—Proceedings of the 4th International Conference on Human Interaction and Emerging Technologies, Strasbourg, France, 28–30 April 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 107–114. [Google Scholar]
- Moesl, B.; Schaffernak, H.; Vorraber, W.; Holy, M.; Herrele, T.; Braunstingl, R.; Koglbauer, I.V. Towards a More Socially Sustainable Advanced Pilot Training by Integrating Wearable Augmented Reality Devices. Sustainability 2022, 14, 2220. [Google Scholar] [CrossRef]
- Velosa, J.D.; Cobo, L.; Castillo, F.; Castillo, C. Methodological proposal for use of Virtual Reality VR and Augmented Reality AR in the formation of professional skills in industrial maintenance and industrial safety. In Online Engineering & Internet of Things; Springer: Berlin/Heidelberg, Germany, 2018; pp. 987–1000. [Google Scholar]
- Bosch, T.; Van Rhijn, G.; Krause, F.; Könemann, R.; Wilschut, E.S.; de Looze, M. Spatial augmented reality: A tool for operator guidance and training evaluated in five industrial case studies. In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, 30 June–3 July 2020; pp. 1–7. [Google Scholar]
- Gavish, N.; Gutierrez, T.; Webel, S.; Rodriguez, J.; Peveri, M.; Bockholt, U.; Tecchia, F. Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks. Interact. Learn. Environ. 2015, 23, 778–798. [Google Scholar] [CrossRef]
- Vergel, R.S.; Tena, P.M.; Yrurzum, S.C.; Cruz-Neira, C. A Comparative Evaluation of a Virtual Reality Table and a HoloLens-Based Augmented Reality System for Anatomy Training. IEEE Trans. Hum. Mach. Syst. 2020, 50, 337–348. [Google Scholar] [CrossRef]
- Ferrer-Torregrosa, J.; Jiménez-Rodríguez, M.Á.; Torralba-Estelles, J.; Garzón-Farinós, F.; Pérez-Bermejo, M.; Fernández-Ehrling, N.J. Distance learning ects and flipped classroom in the anatomy learning: Comparative study of the use of augmented reality, video and notes. BMC Med. Educ. 2016, 16, 230. [Google Scholar] [CrossRef] [Green Version]
- Koutitas, G.; Smith, S.; Lawrence, G.J. Performance evaluation of AR/VR training technologies for EMS first responders. Virtual Real. 2021, 25, 83–94. [Google Scholar] [CrossRef]
- Papakostas, C.; Troussas, C.; Krouska, A.; Sgouropoulou, C. Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System. Sensors 2021, 21, 3888. [Google Scholar] [CrossRef] [PubMed]
- Langley, A.; Lawson, G.; Hermawati, S.; D’Cruz, M.; Apold, J.; Arlt, F.; Mura, K. Establishing the Usability of a Virtual Training System for Assembly Operations within the Automotive Industry. Hum. Factors Ergon. Manuf. Serv. Ind. 2016, 26, 667–679. [Google Scholar] [CrossRef]
- Chang, Y.S.; Hu, K.J.; Chiang, C.W.; Lugmayr, A. Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory. Sensors 2019, 20, 105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gonzalez, A.V.; Koh, S.; Kapalo, K.; Sottilare, R.; Garrity, P.; Billinghurst, M.; LaViola, J. A comparison of desktop and augmented reality scenario based training authoring tools. In Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Beijing, China, 14–18 October 2019; pp. 339–350. [Google Scholar]
- Moghaddam, M.; Wilson, N.C.; Modestino, A.S.; Jona, K.; Marsella, S.C. Exploring augmented reality for worker assistance versus training. Adv. Eng. Inform. 2021, 50, 101410. [Google Scholar] [CrossRef]
- Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. Collaborative Augmented Digital Twin: A Novel Open-Source Augmented Reality Solution for Training and Maintenance Processes in the Shipyard of the Future. Eng. Proc. 2021, 7, 10. [Google Scholar]
- Henderson, S.J.; Feiner, S. Evaluating the benefits of augmented reality for task localization in maintenance of an armored personnel carrier turret. In Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality, Orlando, FL, USA, 19–22 October 2009; pp. 135–144. [Google Scholar]
- Henderson, S.; Feiner, S.J. Exploring the benefits of augmented reality documentation for maintenance and repair. IEEE Trans. Vis. Comput. Graph. 2010, 17, 1355–1368. [Google Scholar] [CrossRef] [Green Version]
- Siyaev, A.; Jo, G.S. Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality. Sensors 2021, 21, 2066. [Google Scholar] [CrossRef]
- Wiederhold, B.K.; Bouchard, S. Advances in Virtual Reality and Anxiety Disorders; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
- Vovk, A.; Wild, F.; Guest, W.; Kuula, T. Simulator Sickness in Augmented Reality Training Using the Microsoft HoloLens. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada, 21–26 April 2018; pp. 1–9. [Google Scholar]
- Kaplan, A.D.; Cruit, J.; Endsley, M.; Beers, S.M.; Sawyer, B.D.; Hancock, P.A. The Effects of Virtual Reality, Augmented Reality, and Mixed Reality as Training Enhancement Methods: A Meta-Analysis. Hum. Factors 2021, 63, 706–726. [Google Scholar] [CrossRef]
- Borsci, S.; Lawson, G.; Broome, S. Empirical evidence, evaluation criteria and challenges for the effectiveness of virtual and mixed reality tools for training operators of car service maintenance. Comput. Ind. 2015, 67, 17–26. [Google Scholar] [CrossRef]
- Daling, L.M.; Abdelrazeq, A.; Isenhardt, I. Abdelrazeq, A.; Isenhardt, I. A Comparison of Augmented and Virtual Reality Features in Industrial Trainings. In Virtual, Augmented and Mixed Reality. Industrial and Everyday Life Applications; Springer: Berlin/Heidelberg, Germany, 2020; pp. 47–65. [Google Scholar]
- Keighrey, C.; Flynn, R.; Murray, S.; Murray, N. A Physiology-Based QoE Comparison of Interactive Augmented Reality, Virtual Reality and Tablet-Based Applications. IEEE Trans. Multimed. 2021, 23, 333–341. [Google Scholar] [CrossRef]
- Werrlich, S.; Daniel, A.; Ginger, A.; Nguyen, P.-A.; Notni, G. Comparing HMD-Based and Paper-Based Training. In Proceedings of the 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Munich, Germany, 16–20 October 2018; pp. 134–142. [Google Scholar]
- Young, K.-Y.; Cheng, S.-L.; Ko, C.-H.; Su, Y.-H.; Liu, Q.-F. A novel teaching and training system for industrial applications based on augmented reality. J. Chin. Inst. Eng. 2020, 43, 796–806. [Google Scholar] [CrossRef]
- Münzer, S. Facilitating spatial perspective taking through animation: Evidence from an aptitude–treatment-interaction. Learn. Individ. Differ. 2012, 22, 505–510. [Google Scholar] [CrossRef]
- Sweller, J. Cognitive load during problem solving: Effects on learning. Cogn. Sci. 1988, 12, 257–285. [Google Scholar] [CrossRef]
- Sweller, J. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 2010, 22, 123–138. [Google Scholar] [CrossRef]
- Gasteiger, N.; van der Veer, S.N.; Wilson, P.; Dowding, D. How, for Whom, and in Which Contexts or Conditions Augmented and Virtual Reality Training Works in Upskilling Health Care Workers: Realist Synthesis. JMIR Serious Games 2022, 10, e31644. [Google Scholar] [CrossRef]
- Tziner, A.; Fisher, M.; Senior, T.; Weisberg, J. Assessment, Effects of trainee characteristics on training effectiveness. Int. J. Sel. Assess. 2007, 15, 167–174. [Google Scholar] [CrossRef]
- Baddeley, A. Working memory. Science 1992, 255, 556–559. [Google Scholar] [CrossRef] [PubMed]
- Stanton, N.A. Hierarchical task analysis: Developments, applications, and extensions. Appl. Erg. 2006, 37, 55–79. [Google Scholar] [CrossRef] [Green Version]
- Reason, J. Human Error; Cambridge University Press: Cambridge, UK, 1990. [Google Scholar]
- Randeniya, N.; Ranjha, S.; Kulkarni, A.; Lu, G. Virtual reality based maintenance training effectiveness measures—A novel approach for rail industry. In Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada, 12–14 June 2019; pp. 1605–1610. [Google Scholar]
- Bond, A.; Neville, K.; Mercado, J.; Massey, L.; Wearne, A.; Ogreten, S. Evaluating Training Efficacy and Return on Investment for Augmented Reality: A Theoretical Framework. In Advances in Human Factors in Training, Education, and Learning Sciences; Springer: Berlin/Heidelberg, Germany, 2019; pp. 226–236. [Google Scholar]
- Aziz, F.A.; Alsaeed, A.S.; Sulaiman, S.; Mohd Ariffin, M.K.A.; Al-Hakim, M.F. Mixed Reality Improves Education and Training in Assembly Processes. J. Eng. Technol. Sci. 2020, 52, 598. [Google Scholar] [CrossRef]
- Papakostas, C.; Troussas, C.; Krouska, A.; Sgouropoulou, C. User acceptance of augmented reality welding simulator in engineering training. Educ. Inf. Technol. 2021, 27, 791–817. [Google Scholar] [CrossRef]
- Nee, A.Y.C.; Ong, S.K.; Chryssolouris, G.; Mourtzis, D. Augmented reality applications in design and manufacturing. CIRP Ann. 2012, 61, 657–679. [Google Scholar] [CrossRef]
- Catal, C.; Akbulut, A.; Tunali, B.; Ulug, E.; Ozturk, E. Evaluation of augmented reality technology for the design of an evacuation training game. Virtual Real. 2019, 24, 359–368. [Google Scholar] [CrossRef] [Green Version]
- Uttal, D.H.; Meadow, N.G.; Tipton, E.; Hand, L.L.; Alden, A.R.; Warren, C.; Newcombe, N.S. The malleability of spatial skills: A meta-analysis of training studies. Psychol Bull. 2013, 139, 352–402. [Google Scholar] [CrossRef] [PubMed]
Factors | Factor Loading | Cumulative Contribution Rate | Cronbach’s α | CR | AVE |
---|---|---|---|---|---|
F1 | Q1:0.913 Q2:0.712 Q3:0.946 Q4:0.780 Q5:0.897 | 72.972% | 0.904 | 0.930 | 0.730 |
Items | ss | Partial η2 | df | MS | F | Sig. |
---|---|---|---|---|---|---|
T1 | 878.525 | 0.583 | 2 | 439.262 | 39.140 | <0.001 *** |
T2 | 2060.539 | 0.262 | 2 | 1030.270 | 9.096 | <0.001 *** |
T3 | 3651.474 | 0.117 | 2 | 1825.737 | 3.702 | 0.031 * |
Descriptive Statistics | Pairwise Comparison | |||||
---|---|---|---|---|---|---|
Groups | n | Average | SD | Comparison Group | Sig. a | |
T1 | VR | 20 | 35.689 | 3.196 | TT | <0.001 *** |
TT | 20 | 28.816 | 2.918 | AR | <0.001 *** | |
AR | 20 | 37.789 | 3.877 | VR | 0.123 | |
T2 | VR | 20 | 170.669 | 12.148 | TT | <0.001 *** |
TT | 20 | 155.570 | 9.820 | AR | 0.033 * | |
AR | 20 | 163.833 | 9.972 | VR | 0.228 | |
T3 | VR | 20 | 172.439 | 20.347 | TT | 1.000 |
TT | 20 | 175.486 | 27.491 | AR | 0.034 * | |
AR | 20 | 156.697 | 20.862 | VR | 0.168 |
Task I | Task II | Task III | |||||||
---|---|---|---|---|---|---|---|---|---|
H(K) | df | Sig. | H(K) | df | Sig. | H(K) | df | Sig. | |
CPSI | 1.809 | 2 | 0.405 | 7.498 | 2 | 0.024 * | 8.687 | 2 | 0.013 * |
Task Ⅱ | Task Ⅲ | |||||||
---|---|---|---|---|---|---|---|---|
Items | Mean Rank | U | Z | Sig(2-Tailed) | Mean Rank | U | Z | Sig(2-Tailed) |
VR vs. TT | 15.88 25.13 | 107.5 | −2.528 | 0.011 * | 22.53 18.48 | 159.5 | −1.097 | 0.273 |
TT vs. AR | 22.48 18.52 | 160.5 | −1.087 | 0.277 | 15.00 26.00 | 90.0 | −2.987 | 0.003 * |
VR vs. AR | 17.00 24.00 | 130.0 | −1.912 | 0.056 | 17.40 23.60 | 138.0 | −1.686 | 0.092 |
Items | ss | Partial η2 | df | MS | F | Sig. |
---|---|---|---|---|---|---|
N1 | 1303.642 | 0.043 | 2 | 651.821 | 1.256 | 0.293 |
N2 | 6365.045 | 0.099 | 2 | 3182.522 | 3.071 | 0.054 |
N3 | 6111.054 | 0.129 | 2 | 3055.527 | 4.145 | 0.021 * |
Task I | Task II | Task III | ||||
---|---|---|---|---|---|---|
Items | SE | Sig. b | SE | Sig. b | SE | Sig. b |
VR vs. TT | 7.233 | 0.999 | 10.222 | 0.983 | 8.588 | 0.999 |
TT vs. AR | 7.206 | 0.364 | 10.183 | 0.051 | 8.621 | 0.134 |
VR vs. AR | 7.251 | 0.999 | 10.247 | 0.447 | 8.642 | 0.022 * |
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Liu, X.-W.; Li, C.-Y.; Dang, S.; Wang, W.; Qu, J.; Chen, T.; Wang, Q.-L. Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology. Sustainability 2022, 14, 14351. https://doi.org/10.3390/su142114351
Liu X-W, Li C-Y, Dang S, Wang W, Qu J, Chen T, Wang Q-L. Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology. Sustainability. 2022; 14(21):14351. https://doi.org/10.3390/su142114351
Chicago/Turabian StyleLiu, Xiao-Wei, Cheng-Yu Li, Sina Dang, Wei Wang, Jue Qu, Tong Chen, and Qing-Li Wang. 2022. "Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology" Sustainability 14, no. 21: 14351. https://doi.org/10.3390/su142114351
APA StyleLiu, X. -W., Li, C. -Y., Dang, S., Wang, W., Qu, J., Chen, T., & Wang, Q. -L. (2022). Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology. Sustainability, 14(21), 14351. https://doi.org/10.3390/su142114351