Predictive Analytics In Higher Ed

See also: Using SAS® Enterprise BI and SAS® Enterprise Miner™ to

Reduce Student Attrition & A Mandate For Causal Inference in Higher Education

A colleague shared with me the following article The Predictive Analytics Reporting Framework Moves Forward . This is a great example of the 'multicultural' approach to data analysis that I had discussed in 'Culture War: Classical Statistics vs. Machine Learning'

From the article:

Wagner: "For some in education what we’re doing might seem a bit heretical at first--we’ve all been warned in research methods classes that data snooping is bad! But the newer technologies and the sophisticated analyses that those technologies have enabled have helped us to move away from looking askance at pattern recognition. That may take a while in some research circles, but in decision-making circles it’s clear that pattern recognition techniques are making a real difference in terms of the ways numerous enterprises in many industries are approaching their work"

The fact that they are willing to step out of what Leo Brieman described as the 'statistical straight jacket' of traditional research methods and embrace 'heretical' pattern recognition and data mining algorithms is impressive. The paragraph excerpted above and the distinction between 'research circles' and 'decision making circles' indicates to me they clearly get what he was talking about over a decade ago in his famous article 'Two Statistical Cultures' ( http://bit.ly/bqtZ6I ).

"There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. (i.e. 'research circles') This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems.Algorithmic modeling,both in theory and practice,has developed rapidly in fields outside statistics."(like 'decision making circles').

As far as 'newer' technologies and the 'sophisticated analysis' that they can employ, I can only see opportunities for programs like SAS Enterprise Miner, JMP, and R.

Reference:
Leo Breiman. Statistical Modeling: The Two Cultures.  Statist. Sci. Volume 16, Issue 3 (2001), 199-231.