Applications of Stochastic Analyses for Collaborative Learning and Cognitive Assessment

Abstract : This paper presents a basic introduction to some popular stochastic analysis methods from an unbiased disciplinary perspective. Examples ranging from fields as diverse as defense analysis, cognitive science, and instruction are illustrated throughout to demonstrate the variety of applications that benefit from such stochastic analysis methods and models. Two applications of longitudinal stochastic analysis methods to collaborative and cognitive training environments are discussed in detail. The first application applies a combination of latent mixed Markov modeling and multidimensional scaling for modeling, analyzing, and supporting the process of online knowledge sharing. In the second application, a combination of iterative nonlinear machine learning algorithms is applied to identify latent classes of problem-solving strategies. The examples illustrated in this paper are instances of an increasing global trend toward interdisciplinary research. As this trend continues to grow, research that takes advantage of the gaps and overlaps in analytical methodologies between disciplines will save time, effort, and research funds.