This research aimed to (1) develop game-based learning activities for basic passenger risk assessment training and (2) improve the learning achievement of immigration officers newly appointed to the Immigration Bureau. The researcher designed the learning activities by integrating pre- and post-learning tests with an interactive simulation game. The study population consisted of 116 police officers of immigration officers newly appointed to the Immigration Bureau in the 2025 fiscal year, selected using a purposive sampling method. Statistical analysis included percentage, mean, and standard deviation. The study found that out of 116 trainees, 23 persons (19.80%) scored more than 60% on the pre-test, while 111 persons (95.68%) scored more than 60% on the post-test. This indicates a statistically significant increase in learning achievement of 75.88% between the pre-test and post-tests. The average score of the trainees was 49.46 on the pre-test and 81.87 on the post-test. In conclusion, Game-based learning is an effective method for training frontline immigration officers who require specialized knowledge and skills to screen and assess the risk of passengers traveling into and out of the kingdom.

Authors:
Chayanit Pichitronnachai, King Mongkut's University of Technology Thonburi, Thailand
Tanapon Tamrongkunanan, King Mongkut's University of Technology Thonburi, Thailand

About the Presenter(s)
Dr Tanapon Tamrongkunanan is a University Assistant Professor/Lecturer at King Mongkut's University of Technology Thonburi in Thailand

See this presentation on the full schedule – Thursday Schedule

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