Chapter 9: Applications of Stochastic Analyses for Collaborative Learning and Cognitive Assessment¹

The growing trend in interdisciplinary graduate programs appropriately breeds small developing communities of researchers that close the gaps between mainstream fields such as artificial intelligence, statistics, engineering, cognitive science, and psychology. The result is twofold and realizes both advantages and disadvantages. While we may discover new applications of existing algorithms and methods, we may also uncover similarities among the algorithms and methods that cross discipline boundaries and realize that some effort has been wasted in reinventing the wheel. As an alumnus of an interdisciplinary graduate program in applied artificial intelligence, the first author notes that she is often struck by the distance between communities that concurrently develop and apply identical methods to solve entirely different problems.