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Makale Özeti:
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The aim of this research was to construct a motivational model which would stimulate voluntary and proactive
learning using digital game methods offering players more freedom and control. The theoretical framework of
this research lays the foundation for a pedagogical learning model based on digital games. We analyzed the
game reward system, which is recognized as one of the most important mechanisms to engage players in active
sustainable digital game playing. In general, the reward system is designed similar to an exponential learning
model. This paper compares the reward systems of four typical digital games which have more than 10 million
school-age players around the world. Based on the results, we propose a computer-assisted exponential learning
model similar to that applied in digital game based learning models. By applying these results to educational
algorithms associated with the field of artificial intelligence, we are able to motivate emergent learning. Using
the proposed method, it is possible to form a model of computer-assisted learning, adequate for all learning
levels.
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