V-LAMOT: A Cognitive-Load Optimized Virtual Lab for Three-Phase Motor Control
Abstract
Purpose of the study: This study aims to design and validate V-LAMOT, a web-based virtual laboratory for three-phase motor starting simulation. The system is intended to address limitations of physical laboratories by providing an accessible and safe environment while maintaining conceptual accuracy and supporting the development of practical motor control skills.
Methodology: The study adopted the Systems Development Life Cycle (SDLC) to develop the V-LAMOT platform using HTML5, CSS, JavaScript, and state-machine modeling. The design was guided by Cognitive Load Theory principles. Data were obtained through expert validation instruments and the System Usability Scale (SUS), and analyzed using Shapiro–Wilk tests, one-sample t-tests, Cohen’s d, and Pearson correlation with 30 students.
Main Findings: Expert validation indicated high feasibility, with conceptual accuracy reaching a mean score of 4.50/5. SUS evaluation produced an overall score of 78.83 (“Good”), with learnability scoring highest at 82.00. All usability measures were significantly above the benchmark (p < 0.001) with large effect sizes (d > 0.8). A strong correlation between usability and learnability (r = 0.823) suggested effective cognitive load reduction.
Novelty/Originality of this study: This study presents an integrated virtual laboratory that combines state-machine modeling with Cognitive Load Theory-based interface design for three-phase motor control. Unlike conventional simulations, V-LAMOT integrates multiple motor starting methods in one environment and empirically links usability, learnability, and cognitive load reduction, advancing virtual laboratory development through systematic integration of technical accuracy and pedagogical principles.
References
U. Sengamalai et al., “Three phase induction motor drive: A systematic review on dynamic modeling, parameter estimation, and control schemes,” Energies, vol. 15, no. 21, p. 8260, Nov. 2022, doi: 10.3390/en15218260.
A. O. Kurniawan, A. R. Hakiki, K. N. Banjarnahor, M. A. Hady, A. Santoso, and A. Fatoni, “Internet based remote laboratory architecture for 3-phase induction motor control system experiment,” in Proc. Int. Seminar Intell. Technol. Its Appl. (ISITIA), Jul. 2021, pp. 381–385, doi: 10.1109/ISITIA52817.2021.9502222.
V. Potkonjak, M. Vukobratovic, K. Jovanovic, and M. Medenica, “Virtual laboratories for education in science, technology, and engineering: A review,” Comput. Educ., vol. 95, pp. 309–327, 2016, doi: 10.1016/j.compedu.2016.02.002.
E. Fabregas, G. Farias, S. Dormido-Canto, S. Dormido, and F. Esquembre, “Developing a remote laboratory for engineering education,” Comput. Educ., vol. 57, no. 2, pp. 1686–1697, 2011, doi: 10.1016/j.compedu.2011.02.015.
T. de Jong, M. C. Linn, and Z. C. Zacharia, “Physical and virtual laboratories in science and engineering education,” Science, vol. 340, no. 6130, pp. 305–308, May 2013, doi: 10.1126/science.1230576.
J. Martín-Gutiérrez, C. E. Mora, B. Añorbe-Díaz, and A. González-Marrero, “Virtual technologies trends in education,” Eurasia J. Math. Sci. Technol. Educ., vol. 13, no. 2, pp. 469–486, 2017, doi: 10.12973/eurasia.2017.00626a.
A. K. Mohammed, H. M. El Zoghby, and M. M. Elmesalawy, “Remote controlled laboratory experiments for engineering education in the post-COVID-19 era: Concept and example,” in Proc. 2nd Novel Intell. Leading Emerging Sci. Conf. (NILES), 2020, pp. 629–634, doi: 10.1109/NILES50944.2020.9257888.
S. Asgari, J. Trajkovic, M. Rahmani, W. Zhang, R. C. Lo, and A. Sciortino, “An observational study of engineering online education during the COVID-19 pandemic,” PLOS ONE, vol. 16, no. 4, p. e0250041, Apr. 2021, doi: 10.1371/journal.pone.0250041.
L. Rassudov and A. Korunets, “COVID-19 pandemic challenges for engineering education,” in Proc. XI Int. Conf. Electr. Power Drive Syst. (ICEPDS), 2020, pp. 1–3, doi: 10.1109/ICEPDS47235.2020.9249285.
M. Travassos Valdez, C. Machado Ferreira, M. J. M. Martins, and F. P. Maciel Barbosa, “Virtual labs in electrical engineering education: The VEMA environment,” in Proc. Inf. Technol. Based Higher Educ. Training (ITHET), 2014, pp. 1–5, doi: 10.1109/ITHET.2014.7155714.
R. Heradio, L. de la Torre, and D. Galan, “Virtual and remote labs in education: A bibliometric analysis,” Comput. Educ., vol. 98, pp. 14–38, 2016, doi: 10.1016/j.compedu.2016.03.010.
J. Li and W. Liang, “Effectiveness of virtual laboratory in engineering education: A meta-analysis,” PLOS ONE, vol. 19, p. e0316269, 2024, doi: 10.1371/journal.pone.0316269.
S. E. Muilwijk and A. W. Lazonder, “Learning from physical and virtual investigation: A meta-analysis of conceptual knowledge acquisition,” Front. Educ., vol. 8, p. 1163024, Mar. 2023, doi: 10.3389/feduc.2023.1163024.
T. I. Wijayanti, S. J. Field, and K. Rashedy, “The effectiveness of the jigsaw learning model in improving students’ learning outcomes in carbon chemistry,” J. Chem. Learn. Innov., vol. 2, no. 2, pp. 178–185, 2025, doi: 10.37251/jocli.v2i2.2583.
S. M. Reeves and K. J. Crippen, “Virtual laboratories in undergraduate science and engineering courses: A systematic review, 2009–2019,” J. Sci. Educ. Technol., vol. 30, no. 1, pp. 16–30, 2021, doi: 10.1007/s10956-020-09866-0.
K. Frady, “Use of virtual labs to support demand-oriented engineering pedagogy in engineering technology and vocational education training programmes: A systematic review of the literature,” Eur. J. Eng. Educ., vol. 48, no. 5, pp. 822–841, 2023, doi: 10.1080/03043797.2022.2141610.
A. J. Ali, L. A. Khalaf, and A. H. Ahmed, “Modeling and simulation of a 3-phase induction motor based on two types of WFA,” Int. J. Electr. Comput. Eng. (IJECE), vol. 11, no. 2, pp. 1105–1113, 2021, doi: 10.11591/ijece.v11i2.pp1105-1113.
F. Paas and J. Sweller, “Implications of cognitive load theory for multimedia learning,” in The Cambridge Handbook of Multimedia Learning, R. E. Mayer, Ed., Cambridge, U.K.: Cambridge Univ. Press, 2014, pp. 27–42.
J. Sweller, J. J. G. van Merriënboer, and F. Paas, “Cognitive architecture and instructional design: 20 years later,” Educ. Psychol. Rev., vol. 31, no. 2, pp. 261–292, 2019, doi: 10.1007/s10648-019-09465-5.
F. Nikmah, Maison, and Syamsurizal, “Online vs. offline: Comparison of effectiveness of PhET simulation and science kit in junior high school,” Integr. Sci. Educ. J., vol. 4, no. 3, pp. 96–103, 2023, doi: 10.37251/isej.v4i3.472.
J. Leppink, “Cognitive load theory: Practical implications and an important challenge,” J. Taibah Univ. Med. Sci., vol. 12, no. 5, pp. 385–391, Oct. 2017, doi: 10.1016/j.jtumed.2017.05.003.
J. Sweller, “Cognitive load theory and educational technology,” Educ. Technol. Res. Dev., vol. 68, no. 1, pp. 1–16, Feb. 2020, doi: 10.1007/s11423-019-09701-3.
G. Makransky, T. S. Terkildsen, and R. E. Mayer, “Adding immersive virtual reality to a science lab simulation causes more presence but less learning,” Learn. Instr., vol. 60, pp. 225–236, 2019, doi: 10.1016/j.learninstruc.2017.12.007.
D. T. Ogunjobi, S. Abu, A. B. Ajayi, M. I. Emenike, E. E. Obong, and T. K. Mensah, “A systematic review of augmented and virtual reality for STEM learning: Engagement, cognitive load, and transfer outcomes,” Aust. J. Biomed. Res., vol. 1, no. 2, Art. no. aubm010, Nov. 2025, doi: 10.63946/aubiomed/17464.
Y. C. Chen, Y. S. Chang, and M. J. Chuang, “Virtual reality application influences cognitive load-mediated creativity components and creative performance in engineering design,” J. Comput. Assist. Learn., vol. 38, no. 1, pp. 6–18, 2022, doi: 10.1111/jcal.12588.
S. Alarsan, “The effect of health education on mothers’ knowledge, attitudes, and practices in early detection of toddler growth and development,” J. Health Innov. Environ. Educ., vol. 1, no. 2, pp. 121–127, 2024, doi: 10.37251/jhiee.v1i2.2658.
J. H. Berssanette and A. C. de Francisco, “Cognitive load theory in the context of teaching and learning computer programming: A systematic literature review,” IEEE Trans. Educ., vol. 65, no. 3, pp. 440–449, Aug. 2022, doi: 10.1109/TE.2021.3127215.
S. C. Carpiuc and C. Lazar, “Modeling of synchronous electric machines for real-time simulation and automotive applications,” J. Franklin Inst., vol. 354, no. 14, pp. 6258–6281, 2017, doi: 10.1016/j.jfranklin.2017.07.030.
Y. Chang, E. S. Aziz, Z. Zhang, M. Zhang, and S. K. Esche, “Usability evaluation of a virtual educational laboratory platform,” in Proc. ASEE Annu. Conf. Expo., 2015, doi: 10.18260/p.24973.
E. W. de Vries and H. J. Wörtche, “Remote labs and their didactics in engineering education: A case study,” in Proc. Int. Conv. Inf. Commun. Electron. Technol. (MIPRO), 2021, pp. 1598–1600, doi: 10.23919/MIPRO52101.2021.9597027.
D. A. Norman, The Design of Everyday Things, rev. ed. New York, NY, USA: Basic Books, 2013.
J. Brooke, “SUS: A quick and dirty usability scale,” in Usability Evaluation in Industry, London, U.K.: Taylor & Francis, 1996, pp. 189–194.
P. Vlachogianni and N. Tselios, “Perceived usability evaluation of educational technology using the System Usability Scale (SUS): A systematic review,” J. Res. Technol. Educ., vol. 54, no. 3, pp. 392–409, 2022, doi: 10.1080/15391523.2020.1867938.
R. Elmoazen, M. Saqr, M. Khalil, and B. Wasson, “Learning analytics in virtual laboratories: A systematic literature review of empirical research,” Smart Learn. Environ., vol. 10, no. 1, Art. no. 23, 2023, doi: 10.1186/s40561-023-00244-y.
H. Birkeland, M. Khalil, and B. Wasson, “Understanding collaboration in virtual labs: A learning analytics framework development,” in Learning in the Age of Digital and Green Transition, M. E. Auer, W. Pachatz, and T. Rüütmann, Eds., Cham, Switzerland: Springer, 2023, pp. 185–196, doi: 10.1007/978-3-031-26876-2_18.
A. Pfeiffer and D. Uckelmann, “Fostering lab-based learning with learning analytics: A literature review,” Int. J. Online Biomed. Eng., vol. 18, no. 14, 2022, doi: 10.3991/ijoe.v18i14.35073.
C. N. Tulha, M. A. G. Carvalho, and L. N. de Castro, “LEDA: A learning analytics based framework to analyze remote labs interaction,” in Proc. ACM Conf. Learn. Scale (L@S), 2022, pp. 379–383, doi: 10.1145/3491140.3528324.
Y. N. Somantri, “Analysis of the physical education learning process through online media,” Multidiscip. J. Tour. Hosp. Sport Phys. Educ., vol. 1, no. 1, pp. 11–15, 2024, doi: 10.37251/jthpe.v1i1.1037.
J. Solis-Lastra and B. Albertini, “A light systematic literature review on remote laboratories for engineering,” in Proc. IEEE Sci. Humanit. Int. Res. Conf. (SHIRCON), 2021, pp. 1–4, doi: 10.1109/SHIRCON53068.2021.9652258.
R. S. Pressman and B. R. Maxim, Software Engineering: A Practitioner’s Approach, 8th ed. New York, NY, USA: McGraw-Hill Educ., 2015.
A. Agustina and R. A. Rahman, “Structured inquiry learning: Enhancing critical thinking and conceptual understanding through productive questioning,” J. Acad. Biol. Biol. Educ., vol. 2, no. 2, pp. 165–171, 2025, doi: 10.37251/jouabe.v2i2.2604.
T. Dyba, B. A. Kitchenham, and M. Jorgensen, “Evidence-based software engineering for practitioners,” IEEE Softw., vol. 22, no. 1, pp. 58–65, Jan.–Feb. 2005, doi: 10.1109/MS.2005.6.
D. H. Uttal, K. V. Scudder, and J. S. DeLoache, “Manipulatives as symbols: A new perspective on the use of concrete objects to teach mathematics,” J. Appl. Dev. Psychol., vol. 18, no. 1, pp. 37–54, 1997, doi: 10.1016/S0193-3973(97)90013-7.
T. Senthilrajah and S. Ahangama, “Cognitive load and usability assessment in health information systems,” in Augmented Cognition, D. D. Schmorrow and C. M. Fidopiastis, Eds., Cham, Switzerland: Springer, 2025, pp. xxx–xxx, doi: 10.1007/978-3-031-93724-8_13.
G. Pelangi and P. Akkhakraisi, “Improving students’ writing skills in advertising text through audio visual media at the junior high school level,” J. Lang. Lit. Educ. Res., vol. 2, no. 2, pp. 197–205, 2025, doi: 10.37251/jolle.v2i2.2297.
T. Kosch, J. Karolus, J. Zagermann, H. Reiterer, A. Schmidt, and P. W. Woźniak, “A survey on measuring cognitive workload in human-computer interaction,” ACM Comput. Surv., vol. 55, no. 13s, Art. no. 283, Dec. 2023, doi: 10.1145/3582272.
J. Hassan, A. Devi, and B. Ray, “Virtual laboratories in tertiary education: Case study analysis by learning theories,” Educ. Sci., vol. 12, no. 8, p. 554, Aug. 2022, doi: 10.3390/educsci12080554.
A. Schmeck et al., “Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings,” Instr. Sci., vol. 43, no. 1, pp. 93–114, 2015, doi: 10.1007/s11251-014-9328-3.
D. Abraham, G. Erceylan, V. Gkioulos, et al., “Towards enhanced cybersecurity in industrial control systems: A systematic review of context-based modeling, digital twins, and machine learning approaches,” Int. J. Inf. Secur., vol. 24, p. 243, 2025, doi: 10.1007/s10207-025-01158-1.
E. Pacheco-Velazquez, V. Rodes-Paragarino, and A. Marquez-Uribe, “Exploring educational simulation platform features for addressing complexity in Industry 4.0: A qualitative analysis of insights from logistics experts,” Front. Educ., vol. 9, p. 1331911, 2024, doi: 10.3389/feduc.2024.1331911.
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