Larry Cuban on School Reform and Classroom Practice

So much of the public admiration for Big Data and algorithms avoids answering basic questions: Why are some facts counted and others ignored? Who decides what factors get included in an algorithm? What does an algorithm whose prediction might lead to  getting fired actually look like? Without a model, a theory in mind, every table, each chart, each datum gets counted, threatens privacy, and, yes, becomes overwhelming. A framework for quantifying data and making algorithmic decisions based on data is essential. Too often, however, they are kept secret or, sadly, missing-in-action.

Here is the point I want to make. Big Data are important; algorithmic formulas are important. They matter. Yet without data gatherers and analyzers using frameworks that make sense of the data, that asks questions about the what and why of phenomena–all the quantifying, all the regression equations and analysis can send researchers, policymakers, and practitioners down dead ends. Big…

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