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Generalization

Our strategy is to develop a learning management system as a social media separated from the experience of the freshman math class.  This separation allows the second school to be something that the school and college system is not. 

Specific methodology creates an awareness of deep structure in the freshman curriculum, in student behavioral responses to past experiences and to pathways that allow the individual to shift one’s perception about what higher mathematics is.

Deep structure based social media will be fully instrumented to acquire massive amounts of data about student opinions and to provide guidance to a freshman student on all sorts of inquiries.  Monitoring is real time.  Individual data is removed in real time so that there is no personal data to protect. 

A reification of each instance produces data that we store in a Hadoop data persistence repository where various analytics produce an ability to direct a layer of guidance in all aspects of freshman life.  However, the mission remains to open access to higher mathematics and science for each individual who now feel fear and pain in the classroom. 

This architecture is applicable to any social phenomenon, such as the health care system, the system supporting medicine research, first responder systems, or investment guidance systems focused on various utility functions such as individual gains or social value.  The architecture may be applied to charting out the details of supply chains leading into a manufacturing industry, or the needs emerging that service industries may wish to respond to. 

The challenges are largely due to conceptual boxes that have been around a long time and that are seen even in the “big data” leading edge.  These boxes each are assumed to have a unique deep structure, and although deep structure itself my change suddenly, the architecture is made sensitive to when change occurs. 

The study of how this architecture is implemented and used by the freshman class has great value in refining the data analytics community’s understanding of how big data repositories might be used in the near future.

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