IEEE Trans Vis Comput Graph. 2022 Mar;28(3):1573-1584. doi: 10.1109/TVCG.2020.3022340. Epub 2022 Jan 28.
Recent years have witnessed a growing interest in serious games (SGs), i.e., digital games for education and training. However, although the potential scalability of SGs to large player populations is often praised in the literature, available SG evaluations did not provide evidence of it because they did not study learning on large, varied, international samples in naturalistic conditions. This article considers a SG that educates players about aircraft cabin safety. It presents the first study of learning in a SG intervention conducted in naturalistic conditions with a very large, worldwide sample, which includes 45,000 players who accepted to answer a knowledge questionnaire before and after playing the game, and more than 400,000 players whose in-game behavior was analyzed. Results show that the SG led to improvement in players' knowledge, assessed with different metrics. Moreover, analysis of repeated play shows that participants improved their in-game safety behavior over time. We also focus on the role of making errors in the game, showing how they led to improvement in knowledge. Finally, we highlight the theoretical models, such as error-based learning and Protection Motivation Theory, that oriented the game design, and can be reused to create SGs for other domains.
近年来,人们对严肃游戏(SGs)越来越感兴趣,即用于教育和培训的数字游戏。然而,尽管文献中经常称赞 SG 具有向大量玩家群体扩展的潜力,但现有的 SG 评估并没有提供证据支持,因为它们没有在自然条件下对大型、多样化的国际样本进行学习研究。本文考虑了一个教育玩家有关飞机客舱安全的 SG。它介绍了第一个在自然条件下对 SG 干预进行的学习研究,该研究使用了一个非常大的全球样本,其中包括 45000 名玩家,他们在玩游戏前后都愿意回答知识问卷,还有超过 400000 名玩家的游戏内行为被分析。结果表明,SG 导致玩家的知识得到了不同指标的提高。此外,对重复游戏的分析表明,参与者随着时间的推移改善了他们的游戏内安全行为。我们还重点介绍了指导游戏设计的理论模型,如基于错误的学习和保护动机理论,并可以将其重新用于为其他领域创建 SGs。