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Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Fact...
We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the...
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering...
A new class of space-time models derived from standard dynamic fac- tor models is proposed. The temporal dependence is modeled by latent factors while the spatial dependence is modeled by the factor...
Expected Posterior Priors in Factor Analysis
Applying principal component analysis as a substitute for factor analysis, we often adopt the following greater-than-one rule to decide the number of factors, k: Take the number of eigenvalues of the ...

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