February  2021, 1(1): 47-59. doi: 10.3934/steme.2021004

The Virtual reality electrical substation field trip: Exploring student perceptions and cognitive learning

1. 

Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia

* Correspondence: sasha@uow.edu.au; Tel: +61-2-4221 3418

Academic Editor: Jingjie Yeo

Received  November 2020 Revised  January 2021 Published  February 2021

COVID19 has disrupted many higher education's learning experiences, including those related to work integrated learning. This included the cancelling of the annual electrical engineering field trip to a local electrical substation. Field trips provides students an opportunity to connect their classroom learning with industry relevant engaging experiences. While virtual reality (VR) alternatives to electrical substations have been implemented and researched, the focus has been on the innovation and not on the educational benefits. The impact on learning is not well documented and understood. To address this gap an experimental study is conducted on fifty electrical engineering students at the University of Wollongong to determine if a VR replica of an electrical substation can provide an equal or better learning and student experience compared to traditional methods. A successful finding would provide confidence to implement such alternatives for situations that include: addressing COVID disruptions; for students that miss the field trip; and for providers that don't have the funds or resources to visit a substation. It was found that the VR substation simulation provided a comparable student experience and stronger cognitive learning benefits than traditional methods. Further research is needed to explore learning impact beyond the cognitive domain.

Citation: Erdem Memik, Sasha Nikolic. The Virtual reality electrical substation field trip: Exploring student perceptions and cognitive learning. STEM Education, 2021, 1 (1) : 47-59. doi: 10.3934/steme.2021004
References:
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Kayhani, N., Taghaddos, H., Noghabaee, M. (2018) Utilization of virtual reality visualizations on heavy mobile crane planning for modular construction. The International Association for Automation and Robotics in Construction Conference. Google Scholar

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Tanaka, E.H., Paludo, J.A., Bacchetti, R., Gadbem, E.V., Domingues, L.R., Cordeiro, C.S. (2017) Immersive virtual training for substation electricians. 2017 IEEE Virtual Reality. Google Scholar

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Grivokostopoulou, F., Paraskevas, M., Perikos, I., Nikolic, S., Kovas, K., Hatzilygeroudis, I. (2018) Examining the impact of pedagogical agents on students learning xxperience in virtual worlds. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering. Google Scholar

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J. FogartyJ. McCormick and S. El-Tawil, Improving student understanding of complex spatial arrangements with virtual reality, Journal of Professional Issues in Engineering Education and Practice, 144 (2018), 04017013.   Google Scholar

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W. TarngC-Y. LeeC-M. Lin and W-H. Chen, Applications of virtual reality in learning the photoelectric effect of liquid crystal display, Computer Applications in Engineering Education, 26 (2018), 1956-67.  doi: 10.1002/cae.21957.  Google Scholar

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H. ShenJ. ZhangB. Yang and B. Jia, Development of an educational virtual reality training system for marine engineers, Computer Applications in Engineering Education, 27 (2019), 580-602.   Google Scholar

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Makransky, G., Andreasen, N.K., Baceviciute, S., Mayer, R.E. (2020) Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality. Journal of Educational Psychology (online first). https://doi.org/10.1037/edu0000473 Google Scholar

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R. AlbertA. PatneyD. Luebke and J. Kim, Latency requirements for foveated rendering in virtual reality, ACM Transactions on Applied Perception, 14 (2017), 1-13.   Google Scholar

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P.N.A. BarataM.R. Filho and Nunes M.V. Alves, Virtual reality applied to the study of the integration of transformers in substations of power systems, International journal of electrical engineering education, 52 (2015), 203-18.   Google Scholar

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Nikolic, S., Suesse, T., Jovanovic, K., Stanisavljevic, Z. (2020) Laboratory learning objectives measurement: Relationships between student evaluation scores and perceived learning. IEEE Transactions on Education (online first). doi: 10.1109/TE.2020.3022666. Google Scholar

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M. Tavakol and R. Dennick, Making sense of Cronbach's alpha, International journal of medical education, 2 (2011), 53-55.   Google Scholar

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J. MaR. JaradatO. AshourM. HamiltonP. Jones and V.L. Dayarathna, Efficacy investigation of virtual reality teaching module in manufacturing system design course, Journal of Mechanical Design, 141 (2019).  doi: 10.1115/1.4041428.  Google Scholar

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Gregory, S., Gregory, B., Grant, S., McDonald, M., Nikolic, S., Farley, H. (2016) Exploring virtual world innovations and design through learner voices. Australasian Society for Computers in Learning and Tertiary Education. Google Scholar

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Nikolic, S., Lee, M.J.W., Vial, P.J. (2015) 2D versus 3D collaborative online spaces for student team meetings: Comparing a web conferencing environment and a video-augmented virtual world. The 26th Annual Conference of the Australasian Association for Engineering Education. Google Scholar

[26]

Wang, W., Li, G. (2010) Virtual reality in the substation training simulator. The 14th International Conference on Computer Supported Cooperative Work in Design. Google Scholar

[27]

Ribeiro, T.R., dos Reis, P.R.J., Júnior, G.B., de Paiva, A.C., Silva, A.C., Maia, I.M.O. (2014) Agito: Virtual reality environment for power systems substations operators training. International Conference on Augmented and Virtual Reality. Google Scholar

[28]

G. Singh.A. MantriO. SharmaR. Dutta and R. Kaur, Evaluating the impact of the augmented reality learning environment on electronics laboratory skills of engineering students, Computer Applications in Engineering Education, 27 (2019), 1361-75.   Google Scholar

[29]

Jackson, T., Nikolic, S., Shen, J., Xia, G. (2018) Knowledge sharing in digital learning communities: A comparative review of issues between education and industry. The IEEE International Conference on Teaching, Assessment, and Learning for Engineering. Google Scholar

show all references

References:
[1]

Gregory, S., Gregory, B., Wood, D., Grant, S., Nikolic, S., Hillier, M. (2017) Me, us and IT: Insiders' views of the complex technical, organisational and personal elements in using virtual worlds in education. ASCILITE Annual Conference. Google Scholar

[2]

Kayhani, N., Taghaddos, H., Noghabaee, M. (2018) Utilization of virtual reality visualizations on heavy mobile crane planning for modular construction. The International Association for Automation and Robotics in Construction Conference. Google Scholar

[3]

Nikolic. S., Lee, M.J.W., Goldfinch, T., Ritz, C.H. (2016). Addressing misconceptions about engineering through student–industry interaction in a video-augmented 3D immersive virtual world. Frontiers in Education Conference. Google Scholar

[4]

Arroyo, E., Arcos, J.L.L. (1999) SRV: A virtual reality application to electrical substations operation training. Proceedings IEEE International Conference on Multimedia Computing and Systems. Google Scholar

[5]

Tanaka, E.H., Paludo, J.A., Bacchetti, R., Gadbem, E.V., Domingues, L.R., Cordeiro, C.S. (2017) Immersive virtual training for substation electricians. 2017 IEEE Virtual Reality. Google Scholar

[6]

S. De FreitasG. Rebolledo-MendezF. LiarokapisG. Magoulas and A. Poulovassilis, Learning as immersive experiences: Using the four-dimensional framework for designing and evaluating immersive learning experiences in a virtual world, British Journal of Educational Technology, 41 (2010), 69-85.   Google Scholar

[7]

Gregory, S., Gregory, B., Wood, D., O'Connell, J., Grant, S., Hillier, M. (2015) New applications, new global audiences: Educators repurposing and reusing 3D virtual and immersive learning resources. Australasian Society for Computers in Learning in Tertiary Education. Google Scholar

[8]

B. Dalgarno. and M.J. Lee, What are the learning affordances of 3-D virtual environments?, British Journal of Educational Technology, 41 (2010), 10-32.   Google Scholar

[9]

M. Perez-RamirezG. Arroyo-Figueroa and A. Ayala, The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks, Interactive Learning Environments, 2019 (2019), 1-18.  doi: 10.1080/10494820.2019.1587636.  Google Scholar

[10]

Mu, Z., Huang, R., Liu, M. (2017) A study on the application of virtual reality technology in the field of nuclear power. 2017 International Conference on Smart Grid and Electrical Automation. Google Scholar

[11]

P. AbichandaniW. McintyreW. Fligor and D. Lobo, Solar energy education through a cloud-based desktop virtual reality system, IEEE Access, 7 (2019), 147081-93.   Google Scholar

[12]

Woodworth, J.W., Ekong, S., Borst, C.W. (2017) Virtual field trips with networked depth-camera-based teacher, heterogeneous displays, and example energy center application. 2017 IEEE Virtual Reality. Google Scholar

[13]

Grivokostopoulou, F., Paraskevas, M., Perikos, I., Nikolic, S., Kovas, K., Hatzilygeroudis, I. (2018) Examining the impact of pedagogical agents on students learning xxperience in virtual worlds. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering. Google Scholar

[14]

J. FogartyJ. McCormick and S. El-Tawil, Improving student understanding of complex spatial arrangements with virtual reality, Journal of Professional Issues in Engineering Education and Practice, 144 (2018), 04017013.   Google Scholar

[15]

W. TarngC-Y. LeeC-M. Lin and W-H. Chen, Applications of virtual reality in learning the photoelectric effect of liquid crystal display, Computer Applications in Engineering Education, 26 (2018), 1956-67.  doi: 10.1002/cae.21957.  Google Scholar

[16]

Hatchard, T., Azmat, F., Al-Amin, M., Rihawi, Z., Ahmed, B., Alsebae, A. (2019) Examining student response to virtual reality in education and training. The 17th IEEE International Conference on Industrial Informatics. Google Scholar

[17]

H. ShenJ. ZhangB. Yang and B. Jia, Development of an educational virtual reality training system for marine engineers, Computer Applications in Engineering Education, 27 (2019), 580-602.   Google Scholar

[18]

Makransky, G., Andreasen, N.K., Baceviciute, S., Mayer, R.E. (2020) Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality. Journal of Educational Psychology (online first). https://doi.org/10.1037/edu0000473 Google Scholar

[19]

R. AlbertA. PatneyD. Luebke and J. Kim, Latency requirements for foveated rendering in virtual reality, ACM Transactions on Applied Perception, 14 (2017), 1-13.   Google Scholar

[20]

P.N.A. BarataM.R. Filho and Nunes M.V. Alves, Virtual reality applied to the study of the integration of transformers in substations of power systems, International journal of electrical engineering education, 52 (2015), 203-18.   Google Scholar

[21]

Nikolic, S., Suesse, T., Jovanovic, K., Stanisavljevic, Z. (2020) Laboratory learning objectives measurement: Relationships between student evaluation scores and perceived learning. IEEE Transactions on Education (online first). doi: 10.1109/TE.2020.3022666. Google Scholar

[22]

M. Tavakol and R. Dennick, Making sense of Cronbach's alpha, International journal of medical education, 2 (2011), 53-55.   Google Scholar

[23]

J. MaR. JaradatO. AshourM. HamiltonP. Jones and V.L. Dayarathna, Efficacy investigation of virtual reality teaching module in manufacturing system design course, Journal of Mechanical Design, 141 (2019).  doi: 10.1115/1.4041428.  Google Scholar

[24]

Gregory, S., Gregory, B., Grant, S., McDonald, M., Nikolic, S., Farley, H. (2016) Exploring virtual world innovations and design through learner voices. Australasian Society for Computers in Learning and Tertiary Education. Google Scholar

[25]

Nikolic, S., Lee, M.J.W., Vial, P.J. (2015) 2D versus 3D collaborative online spaces for student team meetings: Comparing a web conferencing environment and a video-augmented virtual world. The 26th Annual Conference of the Australasian Association for Engineering Education. Google Scholar

[26]

Wang, W., Li, G. (2010) Virtual reality in the substation training simulator. The 14th International Conference on Computer Supported Cooperative Work in Design. Google Scholar

[27]

Ribeiro, T.R., dos Reis, P.R.J., Júnior, G.B., de Paiva, A.C., Silva, A.C., Maia, I.M.O. (2014) Agito: Virtual reality environment for power systems substations operators training. International Conference on Augmented and Virtual Reality. Google Scholar

[28]

G. Singh.A. MantriO. SharmaR. Dutta and R. Kaur, Evaluating the impact of the augmented reality learning environment on electronics laboratory skills of engineering students, Computer Applications in Engineering Education, 27 (2019), 1361-75.   Google Scholar

[29]

Jackson, T., Nikolic, S., Shen, J., Xia, G. (2018) Knowledge sharing in digital learning communities: A comparative review of issues between education and industry. The IEEE International Conference on Teaching, Assessment, and Learning for Engineering. Google Scholar

Figure 1.  Distance view of simulation environment
Figure 2.  Drawing in correct location with correct text
Table 1.  Questionnaire results (substation visit/virtual reality)
Category Questions Average score (substation) Average score (virtual reality) Difference between traditional and VR Significance level
Learning Contents 1. I understood most of the learning contents throughout the teaching activity 3.7 3.9 0.2
4%
P = 0.8282
2. I can identify the major components in an electrical substation and what they look like after the teaching activity 3.6 4.1 0.5
10%
P = 0.6045
3. I understood the operation of major components within a substation during the teaching activity 3.6 3.8 0.2
4%
P = 0.8525
4. The learning activity provided useful knowledge on electrical substations 3.6 4.1 0.5
10%
P = 0.6045
5. The learning activity was helpful at learning components in an electrical substation and their operation 3.7 4.0 0.3
6%
P = 0.7560
Operational Experience 6. It was easy to coordinate through the teaching activity 3.8 3.8 0.0
0%
P = 1.0000
7. The speed and execution of the teaching activity was easy to keep up with 3.0 4.4 1.4
28%
P = 0.1642
8. The teaching activity was not disorienting 3.9 3.4 0.5
10%
P = 0.6235
9. The teaching activity motivated me to learn more about electrical substations 4.0 4.4 0.4
8%
P = 0.6344
10. I am satisfied by the experience of the learning activity 4.3 4.3 0.0
0%
P = 1.0000
Total 37.2 40.2 3.0
6%
P = 0.7545
*Statistical Significance (P < 0.05)
Category Questions Average score (substation) Average score (virtual reality) Difference between traditional and VR Significance level
Learning Contents 1. I understood most of the learning contents throughout the teaching activity 3.7 3.9 0.2
4%
P = 0.8282
2. I can identify the major components in an electrical substation and what they look like after the teaching activity 3.6 4.1 0.5
10%
P = 0.6045
3. I understood the operation of major components within a substation during the teaching activity 3.6 3.8 0.2
4%
P = 0.8525
4. The learning activity provided useful knowledge on electrical substations 3.6 4.1 0.5
10%
P = 0.6045
5. The learning activity was helpful at learning components in an electrical substation and their operation 3.7 4.0 0.3
6%
P = 0.7560
Operational Experience 6. It was easy to coordinate through the teaching activity 3.8 3.8 0.0
0%
P = 1.0000
7. The speed and execution of the teaching activity was easy to keep up with 3.0 4.4 1.4
28%
P = 0.1642
8. The teaching activity was not disorienting 3.9 3.4 0.5
10%
P = 0.6235
9. The teaching activity motivated me to learn more about electrical substations 4.0 4.4 0.4
8%
P = 0.6344
10. I am satisfied by the experience of the learning activity 4.3 4.3 0.0
0%
P = 1.0000
Total 37.2 40.2 3.0
6%
P = 0.7545
*Statistical Significance (P < 0.05)
Table 2.  Overview of pre-test and post-test results
Achievement Test Mean Standard Error of the mean Standard Deviation
Control Group
Pre-Test 8.5333 0.1919 0.7432
Post-Test 11.2667 0.3581 1.3870
Experimental Group
Pre-Test 8.4000 0.2545 0.9856
Post-Test 13.6667 0.2702 1.0465
Achievement Test Mean Standard Error of the mean Standard Deviation
Control Group
Pre-Test 8.5333 0.1919 0.7432
Post-Test 11.2667 0.3581 1.3870
Experimental Group
Pre-Test 8.4000 0.2545 0.9856
Post-Test 13.6667 0.2702 1.0465
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