|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A comparison of two methods of using a serious game for teaching marine ecology in a university setting|
Sustainable fishery management
|Citation:||Ameerbakhsh O, Maharaj S, Hussain A & McAdam B (2019) A comparison of two methods of using a serious game for teaching marine ecology in a university setting. International Journal of Human-Computer Studies, 127, pp. 181-189. https://doi.org/10.1016/j.ijhcs.2018.07.004|
|Abstract:||There is increasing interest in the use of serious games in STEM education. Interactive simulations and serious games can be used by students to explore systems where it would be impractical or unethical to perform real world studies or experiments. Simulations also have the capacity to reveal the internal workings of systems where these details are hidden in the real world. However, there is still much to be investigated about the best methods for using these games in the classroom so as to derive the maximum educational benefit. We report on an experiment to compare two different methods of using a serious game for teaching a complex concept in marine ecology, in a university setting: expert demonstration versus exploration-based learning. We created an online game based upon a mathematical simulation of fishery management, modelling how fish populations grow and shrink in the presence of stock removal through fishing. The player takes on the role of a fishery manager, who must set annual catch quotas, making these as high as possible to maximise profit, without exceeding sustainable limits and causing the stock to collapse. There are two versions of the game. The "white-box" or "teaching" game gives the player full information about all model parameters and actual levels of stock in the ocean, something which is impossible to measure in reality. The "black-box" or "testing" game displays only the limited information that is available to fishery managers in the real world, and is used to test the player's understanding of how to use that information to solve the problem of estimating the optimal catch quota.|
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