Vol. 12 No. 5 (2026): In Progress
Open Access
Peer Reviewed

Effectiveness of Vidio-Based Learning in Flat Pattern Design to Improve Learning Outcomes

Authors

Puspaneli , Puji Hujria Suci , Mimi Yupelmi , Sri Zulfia Nofrita , Ilham Zamil , Vina Oktaviani , Rafikah Husni , Reni Fitria , Hadiastuti , Hazevi Atila Yazel Aze , Yulia Aryati , Rima Agustia Utami

DOI:

10.29303/jppipa.v12i5.15101

Published:

2026-05-25

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Abstract

This study examined the effectiveness of video tutorials in enhancing students’ cognitive understanding and psychomotor skills in Flat Pattern Design learning. A quasi-experimental pretest–posttest design was employed involving 30 fashion design students selected through purposive sampling. The instructional intervention utilized video tutorials that demonstrated each stage of the flat pattern design process, enabling students to review learning materials repeatedly and practice independently. Data were collected through cognitive achievement tests and psychomotor performance assessments and analyzed using descriptive and inferential statistics. The findings revealed statistically significant improvements in students’ learning outcomes following the intervention (p < 0.05). Cognitive achievement scores increased from pretest to posttest, accompanied by substantial gains in psychomotor performance. Classroom observations also indicated high levels of participation and engagement throughout the learning activities. These results suggest that video tutorials provide effective support for procedural learning by facilitating visual demonstration, self-paced practice, and skill mastery. The study contributes to the growing body of research on digital learning in vocational education by demonstrating the potential of video-based instruction to strengthen both conceptual understanding and practical competencies in fashion design courses

Keywords:

Effectiveness Flat pattern design Slash method Video tutorial

References

Adekunle, A. A., Fofana, I., Picher, P., Rodriguez-Celis, E. M., Arroyo-Fernandez, O. H., & Zemouri, R. (2025). Optimizing deep learning predictive models: a comprehensive review of RNN and its variant architectures. Applied Soft Computing, 185, 114015. https://doi.org/10.1016/j.asoc.2025.114015 DOI: https://doi.org/10.1016/j.asoc.2025.114015

Alzarok, H., Fletcher, S., & Longstaff, A.-P. (2020). Survey of the current practices and challenges for vision systems in industrial robotic grasping and assembly applications. Advances in Industrial Engineering and Management, 9(1), 19–30. https://doi.org/10.7508/aiem.01.2020.19.30

Cai, J., & Lei, T. (2021). An autonomous positioning method of tube-to-tubesheet welding robot based on coordinate transformation and template matching. IEEE Robotics and Automation Letters, 6(2), 787–794. https://doi.org/10.1109/LRA.2021.3050741 DOI: https://doi.org/10.1109/LRA.2021.3050741

Chen, R., Chen, F., Xu, G., Li, X., Shen, H., & Yuan, J. (2021). Precision analysis model and experimentation of vision reconstruction with two cameras and 3D orientation reference. Scientific Reports, 11(1), 3875. https://doi.org/10.1038/s41598-021-83390-y DOI: https://doi.org/10.1038/s41598-021-83390-y

Chowdary, B.-V., Richards, M.-A., & Gokool, T. (2019). An integrated approach for sustainable product design: concurrent application of DFMA, DFE and CAD/CAE principles and tools. Latin American Journal of Management for Sustainable Development, 4(4), 259. https://doi.org/10.1504/LAJMSD.2019.100836 DOI: https://doi.org/10.1504/LAJMSD.2019.100836

Cong, Y., Chen, R., Ma, B., Liu, H., Hou, D., & Yang, C. (2021). A comprehensive study of 3-D vision-based robot manipulation. IEEE Transactions on Cybernetics, 53(3), 1682–1698. https://doi.org/10.1109/TCYB.2021.3108165 DOI: https://doi.org/10.1109/TCYB.2021.3108165

Dohan, M., Mu, M., Ajit, S., & Hill, G. (2024). Real-walk modelling: a deep learning model for user mobility in virtual reality. Multimedia Systems, 30(1), 44. https://doi.org/10.1007/s00530-023-01200-z DOI: https://doi.org/10.1007/s00530-023-01200-z

Fang, C. (2025). AI-driven digital sculpture design: optimising fusion algorithms with deep learning and virtual reality. International Journal of Information and Communication Technology, 26(22), 55–71. https://doi.org/10.1504/IJICT.2025.146908 DOI: https://doi.org/10.1504/IJICT.2025.146908

Fang, R. (2025). Exploring the role of virtual reality in transforming the environmental art experience. International Journal of E-Collaboration (IJeC), 21(1), 1–15. https://doi.org/10.4018/IJeC.370951 DOI: https://doi.org/10.4018/IJeC.370951

Fang, W., Xu, X., & Tian, X. (2022). A vision-based method for narrow weld trajectory recognition of arc welding robots. The International Journal of Advanced Manufacturing Technology, 121(11–12), 8039–8050. https://doi.org/10.1007/s00170-022-09804-x DOI: https://doi.org/10.1007/s00170-022-09804-x

Favi, C., Mandolini, M., Campi, F., & Germani, M. (2021). A CAD-based design for manufacturing method for casted components. Procedia CIRP, 100(4), 235–240. https://doi.org/10.1016/j.procir.2021.05.061 DOI: https://doi.org/10.1016/j.procir.2021.05.061

Gao, Y., Zhang, L., & Kim, J. (2026). Deep learning image generation technology for enhancing the presentation effect of image art based on artificial intelligence. Scientific Reports, 16, 14982. https://doi.org/10.1038/s41598-026-45739-z DOI: https://doi.org/10.1038/s41598-026-45739-z

Gómez, E.-A., Rodríguez, S.-J.-B., Cuan, U.-E., Cabello, J.-A.-E., & Swenson, R.-L. (2021). Colored 3D path extraction based on depth-RGB sensor for welding robot trajectory generation. Automation, 2(4), 252–265. https://doi.org/10.3390/automation2040016 DOI: https://doi.org/10.3390/automation2040016

Gong, Y. (2021). Application of virtual reality teaching method and artificial intelligence technology in digital media art creation. Ecological Informatics, 63, 101304. https://doi.org/10.1016/j.ecoinf.2021.101304 DOI: https://doi.org/10.1016/j.ecoinf.2021.101304

Gui-Wei, B., & Guo-Bao, Z. (2024). Research on the visual impact of digital Media art based on augmented reality technology. Computer Aided Design and Applications, 21(S2), 186–201. https://doi.org/10.14733/cadaps.2024.S2.186-201 DOI: https://doi.org/10.14733/cadaps.2024.S2.186-201

Liu, Y., Zhao, L., & Su, Y. S. (2022). The Impact of Teacher Competence in Online Teaching on Perceived Online Learning Outcomes during the COVID-19 Outbreak: A Moderated-Mediation Model of Teacher Resilience and Age. International Journal of Environmental Research and Public Health, 19(10), 6282. https://doi.org/10.3390/ijerph19106282 DOI: https://doi.org/10.3390/ijerph19106282

Peng, B., & Sirisuk, M. (2024). Exploration of CAD and neural network integration in art design and cultural heritage protection. Computer Aided Design and Applications, 21(18), 128–144. https://doi.org/10.14733/cadaps.2024.S18.128-144 DOI: https://doi.org/10.14733/cadaps.2024.S18.128-144

Qian, J. (2022). Research on artificial intelligence technology of virtual reality teaching method in digital media art creation. Journal of Internet Technology, 23(1), 127–134. https://doi.org/10.53106/160792642022012301013 DOI: https://doi.org/10.53106/160792642022012301013

Raza, A., Rehman, A., Sehar, R., Alamri, F. S., Alotaibi, S., Al Ghofaily, B., & Saba, T. (2024). Optimized virtual reality design through user immersion level detection with novel feature fusion and explainable artificial intelligence. PeerJ Computer Science, 10, e2150. https://doi.org/10.7717/peerj-cs.2150 DOI: https://doi.org/10.7717/peerj-cs.2150

Tang, J. (2026). Analysis of the use and effectiveness of artificial intelligence-assisted creation tools in digital art and design. International Journal of Computer Information Systems and Industrial Management Applications, 18, 12. https://doi.org/10.70917/ijcisim-2026-0104 DOI: https://doi.org/10.70917/ijcisim-2026-0104

Tian, Q., & Li, Q. (2024). Combining creative adversarial networks with art design models and machine vision feedback optimization. Computer-Aided Design and Applications, 21, 103–116. https://doi.org/10.14733/cadaps.2024.S16.103-116 DOI: https://doi.org/10.14733/cadaps.2024.S16.103-116

Wan, G., Li, F., Zhu, W., & Wang, G. (2020). High-precision six-degree-of-freedom pose measurement and grasping system for large-size object based on binocular vision. Sensor Review, 40(1), 71–80. https://doi.org/10.1108/SR-05-2019-0123 DOI: https://doi.org/10.1108/SR-05-2019-0123

Wang, H., & Li, J. (2024). Integration path of digital media art and environmental design based on virtual reality technology. Open Computer Science, 14(1), 20240012. https://doi.org/10.1515/comp-2024-0012 DOI: https://doi.org/10.1515/comp-2024-0012

Wang, J., Li, L., & Xu, P. (2023). Visual sensing and depth perception for welding robots and their industrial applications. Sensors, 23(24), 9700. https://doi.org/10.3390/s23249700 DOI: https://doi.org/10.3390/s23249700

Wang, P., & Xu, P. (2024). Graph Neural networks-based virtual reality data fusion display for new Media art. Computer-Aided Design and Applications, 21, 15–27. https://doi.org/10.14733/cadaps.2024.S28.15-27 DOI: https://doi.org/10.14733/cadaps.2024.S28.97-109

Wang, T., Zhang, Y., & Liu, B. (2023). Model-based visual servoing for automatic docking system of circular symmetrical target with large displacement. International Journal of Control, Automation and Systems, 21(4), 1273–1284. https://doi.org/10.1007/s12555-021-0417-1 DOI: https://doi.org/10.1007/s12555-021-0417-1

Wu, P., Liu, Y., Chen, H., Li, X., & Wang, H. (2025). VR-empowered interior design: enhancing efficiency and quality through immersive experiences. Displays, 86, 102887. https://doi.org/10.1016/j.displa.2024.102887 DOI: https://doi.org/10.1016/j.displa.2024.102887

Xu, B. (2024). Practice of digital media art education based on virtual reality technology. Journal of Electrical Systems, 20(3s), 1714–1723. https://doi.org/10.52783/jes.1711 DOI: https://doi.org/10.52783/jes.1711

Yan, S., Tao, X., & Xu, D. (2021). High-precision robotic assembly system using three-dimensional vision. International Journal of Advanced Robotic Systems, 18(3), 17298814211027028. https://doi.org/10.1177/17298814211027029 DOI: https://doi.org/10.1177/17298814211027029

Yang, H., Jiang, P., & Wang, F. (2020). Multi-view-based pose estimation and its applications on intelligent manufacturing. Sensors, 20(18), 5072. https://doi.org/10.3390/s20185072 DOI: https://doi.org/10.3390/s20185072

Yanqiong, Y. (2024). Analysis of utilizing artificial intelligence to improve the efficiency of digital Media art creation. Journal of Artificial Intelligence Practice, 7(4), 101. https://doi.org/10.23977/jaip.2024.070412 DOI: https://doi.org/10.23977/jaip.2024.070412

Ye, C., & Kuang, C. (2024). Online works display system of art design based on VR technology and machine vision. Computer Aided Design and Applications, 21, 126–143. https://doi.org/10.14733/cadaps.2024.S2.126-143 DOI: https://doi.org/10.14733/cadaps.2024.S2.126-143

Zhang, X., & Wu, L. (2024). Automated method for digital art creation and display based on computer aided design. Computer-Aided Design and Applications, 21, 140–153. https://doi.org/10.14733/cadaps.2024.S14.140-153 DOI: https://doi.org/10.14733/cadaps.2024.S14.140-153

Zhao, X. (2025). The Impact of Live Polling Quizzes on Student Engagement and Performance in Computer Science Lectures: A Post-COVID19 Study. International Conference on Computer Supported Education, CSEDU - Proceedings, 1(1), 291–298. https://doi.org/10.5220/0013218700003932 DOI: https://doi.org/10.5220/0013218700003932

Zheng, S., & An, S. (2023). Digital art design and media practice integrating CAD and virtual reality technology. Computer-Aided Design and Applications, 20(S13), 86–97. https://doi.org/10.14733/cadaps.2023.S13.86-97 DOI: https://doi.org/10.14733/cadaps.2023.S13.86-97

Zhuge, W., & Li, Y. (2024). Augmented reality based on network physics and 6G for immersive experience of digital Media art. Wireless Personal Communications, 1–19. https://doi.org/10.1007/s11277-024-11148-6 DOI: https://doi.org/10.1007/s11277-024-11148-6

Author Biographies

Puspaneli, Universitas Negeri Padang

Author Origin : Indonesia

Puji Hujria Suci, Universitas Negeri Padang

Author Origin : Indonesia

Mimi Yupelmi, Universitas Negeri Padang

Author Origin : Indonesia

Sri Zulfia Nofrita, Universitas Negeri Padang

Author Origin : Indonesia

Ilham Zamil, Universitas Negeri Padang

Author Origin : Indonesia

Vina Oktaviani, Universitas Negeri Padang

Author Origin : Indonesia

Rafikah Husni, Universitas Negeri Padang

Author Origin : Indonesia

Reni Fitria, Universitas Negeri Padang

Author Origin : Indonesia

Hadiastuti, Universitas Negeri Padang

Author Origin : Indonesia

Hazevi Atila Yazel Aze, Universitas Negeri Padang

Author Origin : Indonesia

Yulia Aryati, Universitas Negeri Padang

Author Origin : Indonesia

Rima Agustia Utami, Universitas Negeri Padang

Author Origin : Indonesia

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How to Cite

Puspaneli, Suci, P. H., Yupelmi, M., Nofrita, S. Z., Zamil, I., Oktaviani, V., … Utami, R. A. (2026). Effectiveness of Vidio-Based Learning in Flat Pattern Design to Improve Learning Outcomes. Jurnal Penelitian Pendidikan IPA, 12(5), 896–905. https://doi.org/10.29303/jppipa.v12i5.15101