Cognitive-Oriented Serious Game Design for Manufacturing Production-Flow Problem Solving Using Mini Drone Assembly

Handini Fatma Sari; Chanifah Indah Ratnasari; Rina Mulyati

doi:10.57152/malcom.v6i3.2916

Authors

Handini Fatma Sari Universitas Islam Indonesia
Chanifah Indah Ratnasari Universitas Islam Indonesia https://orcid.org/0000-0001-7328-8242
Rina Mulyati Universitas Islam Indonesia

DOI:

https://doi.org/10.57152/malcom.v6i3.2916

Keywords:

Cognitive-Oriented Serious Game, Game Design, Manufacturing Production-Flow, Mini Drone Assembly

Abstract

Manufacturing production-flow problem-solving requires Industrial Engineering students to understand how queues, work-in-process (WIP), bottlenecks, quality control, rework, and system stability interact to determine production performance. Yet conventional materials rarely allow students to observe how a single decision affects the whole system. This study aims to produce a refined, cognitive-oriented serious game design that explicitly links cognitive problem-solving, simplified production-flow rules, and game-system interaction across four progressive levels, and to evaluate it formatively as a basis for further development. Using Design-Based Research, the design was represented through Figma-based static mockups covering level progression, rule-based production behavior, feedback, and performance reports, operationalizing an Observe–Diagnose–Predict–Adjust–Stabilize–Reflect loop. Two game-design experts and five Industrial Engineering students evaluated the design through researcher-guided walkthroughs. Expert judgment produced an overall mean of 2.86 out of 5, indicating refinement needs in dynamic-system behavior and feedback. In contrast, prospective users gave 3.74 out of 5, with the strongest perception in cognitive support. Consistent with its formative objective, these results were not treated as evidence of learning effectiveness but were directly used to refine production-flow rules, feedback, and level structure, producing a four-level design artifact that provides a basis for future prototype development and effectiveness testing.

Downloads

Download data is not yet available.

References

S. N. Groesser and M. Schaffernicht, “Mental models of dynamic systems: taking stock and looking ahead,” Syst. Dyn. Rev., vol. 28, no. 1, pp. 46–68, Jan. 2012, doi: 10.1002/sdr.476.

W. Terkaj, M. Urgo, P. Kovács, E. Tóth, and M. Mondellini, “A framework for virtual learning in industrial engineering education: development of a reconfigurable virtual learning factory application,” Virtual Real., vol. 28, no. 3, p. 148, Aug. 2024, doi: 10.1007/s10055-024-01042-8.

M. C. Magnanini, O. Demir, M. Colledani, and T. Tolio, “Performance evaluation of multi-stage manufacturing systems operating under feedback and feedforward quality control loops,” CIRP Annals, vol. 73, no. 1, pp. 349–352, Jan. 2024, doi: 10.1016/j.cirp.2024.04.015.

R. Tikhonov and S. DeDeo, “Prediction, Explanation, and Control: The Use of Mental Models in Dynamic Environments,” Open Mind, vol. 7, pp. 894–916, Nov. 2023, doi: 10.1162/opmi_a_00112.

L. Y. Lee et al., “Cognitive flexibility training for impact in real-world settings,” Curr. Opin. Behav. Sci., vol. 59, p. 101413, Oct. 2024, doi: 10.1016/j.cobeha.2024.101413.

M. K. Goddu and A. Gopnik, “The development of human causal learning and reasoning,” Nature Reviews Psychology, vol. 3, no. 5, pp. 319–339, Apr. 2024, doi: 10.1038/s44159-024-00300-5.

E. Bauer, N. Heitzmann, and F. Fischer, “Simulation-based learning in higher education and professional training: Approximations of practice through representational scaffolding,” Studies in Educational Evaluation, vol. 75, p. 101213, Dec. 2022, doi: 10.1016/j.stueduc.2022.101213.

S. Olschewski, A. Luckman, A. Mason, E. A. Ludvig, and E. Konstantinidis, “The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes,” Perspectives on Psychological Science, vol. 19, no. 1, pp. 82–102, Jan. 2024, doi: 10.1177/17456916231179138.

J. L. Plass, B. D. Homer, and C. K. Kinzer, “Foundations of Game-Based Learning,” Educ. Psychol., vol. 50, no. 4, pp. 258–283, Oct. 2015, doi: 10.1080/00461520.2015.1122533.

M. Urgo, W. Terkaj, M. Mondellini, and G. Colombo, “Design of serious games in engineering education: An application to the configuration and analysis of manufacturing systems,” CIRP J. Manuf. Sci. Technol., vol. 36, pp. 172–184, Jan. 2022, doi: 10.1016/j.cirpj.2021.11.006.

A. Skoogh, M. Thürer, M. Subramaniyan, A. Matta, and C. Roser, “Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes,” Prod. Manuf. Res., vol. 11, no. 1, Dec. 2023, doi: 10.1080/21693277.2023.2283031.

A. Huchzermeier and T. Mönch, “Mixed?model assembly lines with variable takt and open stations,” Prod. Oper. Manag., vol. 32, no. 3, pp. 704–722, Mar. 2023, doi: 10.1111/poms.13893.

N. Szilas, “Understanding Learning Content Integration in Games,” International Journal of Game-Based Learning, vol. 15, no. 1, pp. 1–25, Sep. 2025, doi: 10.4018/IJGBL.389730.

C. Udeozor, R. Toyoda, F. Russo Abegão, and J. Glassey, “Digital games in engineering education: systematic review and future trends,” European Journal of Engineering Education, vol. 48, no. 2, pp. 321–339, Mar. 2023, doi: 10.1080/03043797.2022.2093168.

G. Romagnoli, M. Galli, D. Mezzogori, and D. Reverberi, “A Cooperative and Competitive Serious Game for Operations and Supply Chain Management,” International Journal of Online and Biomedical Engineering (iJOE), vol. 18, no. 15, pp. 17–30, Dec. 2022, doi: 10.3991/ijoe.v18i15.35089.

M. Gianpiero, P. Pedrazzoli, G. Landolfi, F. Daniele, and E. Montini, “Introducing Active Learning and Serious Game in Engineering Education: ‘Experience from Lean Manufacturing Course,’” in Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures, vol. 690, Cham, 2023, pp. 363–377. doi: 10.1007/978-3-031-43666-6_25.

F. U. Hasanah, R. W. Satrio, M. I. Hanafri, and S. Maisaroh, “Implementasi Design Thinking pada UI/UX Game Edukasi 2D Hafalan Haditst Menggunakan System Usability Scale (SUS) dan User Experience Questionnaire (UEQ),” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 2, pp. 711–721, Apr. 2025, doi: 10.57152/malcom.v5i2.1969.

M. A. J. Aprilianto, E. Wijayanti, and A. A. Chamid, “Game Adventure of Cakra Versi Cerita Rakyat Indonesia Sebagai Media Pembelajaran Interaktif,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 1, pp. 414–422, Jan. 2025, doi: 10.57152/malcom.v5i1.1879.

M. M. Bolick, E. A. Mikhailova, and C. J. Post, “Teaching Innovation in STEM Education Using an Unmanned Aerial Vehicle (UAV),” Educ. Sci. (Basel)., vol. 12, no. 3, p. 224, Mar. 2022, doi: 10.3390/educsci12030224.

F. Akasheh et al., “Additive Manufacturing-Enabled Modular Drone Design Development by Multidisciplinary Engineering Student Team,” in 2022 ASEE Annual Conference & Exposition, Minneapolis, MN: ASEE Conferences, Aug. 2022. doi: 10.18260/1-2--42122.

C. Hoadley and F. C. Campos, “Design-based research: What it is and why it matters to studying online learning,” Educ. Psychol., vol. 57, no. 3, pp. 207–220, Jul. 2022, doi: 10.1080/00461520.2022.2079128.

S. McKenney and T. C. Reeves, “Educational design research for relevant & robust scholarship,” J. Comput. High. Educ., vol. 37, no. 2, pp. 614–638, Jun. 2025, doi: 10.1007/s12528-025-09456-2.

G. M. Sullivan and A. R. Artino, “Analyzing and Interpreting Data From Likert-Type Scales,” J. Grad. Med. Educ., vol. 5, no. 4, pp. 541–542, Dec. 2013, doi: 10.4300/JGME-5-4-18.

S. K. Ahmed et al., “Using thematic analysis in qualitative research,” Journal of Medicine, Surgery, and Public Health, vol. 6, p. 100198, Aug. 2025, doi: 10.1016/j.glmedi.2025.100198.