Game-based Learning (Interactive Demo)

What does the lesson “Finding Citations,” the game “Trivial Pursuit,” and the mechanic “Bluffing” all have in common? In this bootcamp brainstorm facilitated by a CUNY professor, attendees are broken up into design teams whose job it is to enhance a traditional lesson with the mechanics of popular board games in only 20 minutes. Whether you have to teach the rules of citation or the rules of interviewing, there is usually a game plan that can help. This game teaches you how to integrate educational games into your classroom, while providing a fun introduction to the principles of game-based learning.

Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region

Jakarta is vulnerable to flooding mainly caused by prolonged and heavy rainfall and thus a robust hydrological modeling is called for. A good quality of spatial precipitation data is therefore desired so that a good hydrological model could be achieved. Two types of rainfall sources are available: satellite and gauge station observations. At-site rainfall is considered to be a reliable and accurate source of rainfall. However, the limited number of stations makes the spatial interpolation not very much appealing. On the other hand, the gridded rainfall nowadays has high spatial resolution and improved accuracy, but still, relatively less accurate than its counterpart. To achieve a better precipitation data set, the study proposes cokriging method, a blending algorithm, to yield the blended satellite-gauge gridded rainfall at approximately 10-km resolution. The Global Satellite Mapping of Precipitation (GSMaP, 0.1⁰×0.1⁰) and daily rainfall observations from gauge stations are used. The blended product is compared with satellite data by cross-validation method. The newly-yield blended product is then utilized to re-calibrate the hydrological model. Several scenarios are simulated by the hydrological models calibrated by gauge observations alone and blended product. The performance of two calibrated hydrological models is then assessed and compared based on simulated and observed runoff.