Researchers are delving into the important question which asks can predictive analytics really change education?  As predictive analytics grows in popularity, K-12 districts are working together with universities and businesses at a faster pace, using advanced methods to analyze and advance student performance.  Innovative new models are being created, and many school districts are using predictive analytics nationally to measure, monitor, and predict student performance.  This improves learning and student performance.

Can Predictive Analytics Really Change Education?

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This article’s purpose is to describe the advantages and limitations of predictive analytics and describe the advantages and limitations.  It’s also important to note what school districts need to conduct these analyses. It is known that predictive analytics are used by school districts to make decisions, but there has been less news coverage of the process itself.

Another purpose of this article is to provide information, resources and examples of how school districts are using results obtained by predictive analytics to improve instruction and make decisions regarding resource allocation.

There are several ways that school districts use predictive analytics:

  1. To build early warning indicators based on students’ attendance, course failure and behavior to predict dropouts1, 2;
    Can Predictive Analytics Really Change Education?

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  2. To predict on-time high school graduation and being on track in Grade 93,4,5;
  3. To examine indicators that predict college- and career-readiness and postsecondary success5;
  4. Recently predictive analytics has also gained momentum in identifying and retaining great teachers6.

What Is Predictive Analytics?
Predictive models are mostly regression-based analyses conducted to examine which student-, classroom- and/or school-based indicators empirically predict student outcomes.  The statistical analysis uses a combination of potentially actionable indicators to predict an outcome that needs attention and improvement. Most prediction models tend to use lagging indicators, for example, using Grade 9 indicators to predict the likelihood of on-time graduation from high school; indicators in Grades 8-11 predicting the likelihood of college readiness and immediate college enrollment.

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Can Predictive Analytics Really Change Education?

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