A General Application of Singular Value Decomposition Techniques in Automated Learning Environments

In the automated learning environments such as AI Assisted Unschooling, Singular Value Decomposition (SVD) has become a bread and butter technique for generating student progress reports in noisy and unstructured learning environments.  It's difficult to use automatically generated data to evaluate student performance because of the large dimensionality of the datasets, the presence of noise, a large number of fairly arbitrary and meaningless data, and interactions between students in the data set.  A myriad of SVD techniques have been demonstrated to aid each of these issues, but current best practices fail to apply these techniques in a coherent and interoperable way.  This paper seeks to combine a range of proven SVD analysis techniques into methodological framework suitable for general application in the field of AI Assisted Unschooling and other automated learning environments.
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