Effective Air Traffic Control Learning: New Approaches and Innovative Methods

Ridho Rinaldi; Dimas Arya Soeadyfa; Wasito Utomo

doi:10.55927/fjas.v3i10.11131

Authors

Ridho Rinaldi Politeknik Penerbangan Surabaya
Dimas Arya Soeadyfa Politeknik Penerbangan Surabaya
Wasito Utomo Politeknik Penerbangan Surabaya

DOI:

https://doi.org/10.55927/fjas.v3i10.11131

Keywords:

ATC Learning, Innovative Learning Methods, Learning Effectiveness Factors, Simulation in ATC, VR/AR in Training

Abstract

This research discusses more effective Air Traffic Control (ATC) learning through new approaches and innovative methods. The new approaches described include project-based, problem-based, skill-based, and game-based approaches. Meanwhile, the innovative methods discussed include simulation, virtual reality (VR), and augmented reality (AR). Factors that influence the effectiveness of ATC learning are also discussed, including the ability of instructors, facilities, and student motivation. This research used qualitative methods by conducting interviews with 10 ATC personnel and 10 ATC instructors, and performing a Two Sample Mean Test on 96 ATC students in Indonesia. Based on the analysis, it was found that innovative methods and new approaches can improve the effectiveness of ATC learning compared to Conventional methods. However, further research is needed to deepen the use of technology in ATC learning.

Downloads

Download data is not yet available.

References

Allen, D. E., Donham, R. S., & Bernhardt, S. A. (2011). Problem-based learning. New Directions for Teaching and Learning, 2011(128), 21–29. https://doi.org/10.1002/tl.465

Ball, M. O., Chen, C. Y., Hoffman, R., & Vossen, T. (2001). Collaborative decision making in air traffic management: Current and future research directions. Proceedings, 17–30.

Bauer, M., & Langr, D. (2017). Workload - New possibilities for the ATC simulation environment. ICMT 2017 - 6th International Conference on Military Technologies, 447–451. https://doi.org/10.1109/MILTECHS.2017.7988801

Di Stasi, L. L., Marchitto, M., Antolí, A., Baccino, T., & Cañas, J. J. (2010). Approximation of on-line mental workload index in ATC simulated multitasks. Journal of Air Transport Management, 16(6), 330–333. https://doi.org/10.1016/j.jairtraman.2010.02.004

Fothergill, S., & Neal, A. (2008). The effect of workload on conflict decision making strategies in air traffic control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 52(1), 39–43. https://doi.org/10.1177/154193120805200110

Fothergill, S., Loft, S., & Neal, A. (2009). ATC-LabAdvanced: An air traffic control simulator with realism and control. Behavior Research Methods, 41(1), 118–127. https://doi.org/10.3758/BRM.41.1.118

Gerring, J. (2017). Qualitative methods. Annual Review of Political Science, 20(1), 15–36. https://doi.org/10.1146/annurev-polisci-092415-024158

Gorbunov, A. L., & Nechaev, E. E. (2022). Augmented reality technologies in air transport control systems. Proceedings of the 2022 Systems of Signals Generating and Processing in the Field of on Board Communications, 1–5. IEEE.

Gordon, R., Kirwan, B., & Perrin, E. (2007). Measuring safety culture in a research and development centre: A comparison of two methods in the air traffic management domain. Safety Science, 45(6), 669–695. https://doi.org/10.1016/j.ssci.2007.04.004

Grishin, I. Y., & Timirgaleeva, R. R. (2017). Air navigation: Optimisation control of means cueing of the air-traffic control system. Proceedings of the 2017 21st Conference of Open Innovations Association (FRUCT), 134–140. IEEE.

Gretton, A., Borgwardt, K. M., Rasch, M. J., Smola, A., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13, 723–773.

Grushka-Cockayne, Y., De Reyck, B., & Degraeve, Z. (2008). An integrated decision-making approach for improving European air traffic management. Management Science, 54(8), 1395–1409. https://doi.org/10.1287/mnsc.1080.0878

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn?. Educational Psychology Review, 16(3), 235–266.

Jamal Uddin, M., Panda, B. N., Agarwal, P. C., Nunaki, J. H., Damopolii, I., Nusantari, E., & Kandowangko, N. Y. (2019). The contribution of metacognitive in the inquiry-based learning to students’ thinking skill based on SOLO taxonomy. Journal of Physics: Conference Series, 1321(3), 032044. https://doi.org/10.1088/1742-6596/1321/3/032044

Kendall-Taylor, N., Erard, M., & Haydon, A. (2013). The use of metaphor as a science communication tool: Air traffic control for your brain. Journal of Applied Communication Research, 41(4), 412–433. https://doi.org/10.1080/00909882.2013.836678

Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733

Koskela, I., & Palukka, H. (2011). Trainer interventions as instructional strategies in air traffic control training. Journal of Workplace Learning, 23(5), 293–314. https://doi.org/10.1108/13665621111141902/full/xml

Lin, C. J., & Wang, T. I. (2012). Applying augmented reality in high school chemistry education. Proceedings of the 11th International Conference on Computer Supported Collaborative Learning, 32(3), 369–374.

Lin, Y. (2021). Spoken instruction understanding in air traffic control: Challenge, technique, and application. Aerospace, 8(3), 65. https://doi.org/10.3390/aerospace8030065

Loft, S., Hill, A., Neal, A., Humphreys, M., & Yeo, G. (2004). ATC-Lab: An air traffic control simulator for the laboratory. Behavior Research Methods, Instruments, & Computers, 36(2), 331–338. https://doi.org/10.3758/BF03195579

Manning, C., Fox, C., Pfleiderer, E., Mills, S., & Mogilka, H. (2002). The relationship between air traffic control communication events and measures of controller taskload and workload. Air Traffic Control Quarterly, 10(2), 69–83. https://doi.org/10.2514/atcq.10.2.69

Manske, P. G., & Schier, S. L. (2015). Visual scanning in an air traffic control tower – A simulation study. Procedia Manufacturing, 3, 3274–3279. https://doi.org/10.1016/j.promfg.2015.07.397

Masotti, N., Bagassi, S., & De Crescenzio, F. (2016). Augmented reality for the control tower: The RETINA concept. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9768, 444–452. https://doi.org/10.1007/978-3-319-40621-3_32/cover

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.

Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1–12.

Reisman, R., & Brown, D. (2006). Design of augmented reality tools for air traffic control towers. Proceedings of the 6th AIAA Aviation Technology, Integration and Operations Conference (ATIO). American Institute of Aeronautics and Astronautics.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528.

Soldatov, S. K., Zasyad’ko, K. I., Bogomolov, A. V., Vonarshenko, A. P., & Solomka, A. V. (2018). Professionally important skills of air traffic controllers. Human Physiology, 44(7), 775–778. https://doi.org/10.1134/S0362119718070150/metrics