The Bill & Melinda Gates Foundation awarded WCET (the WICHE Cooperative for Educational Technologies) a grant to identify variables that influence student retention and progression.  Announced in a May 16 press release, the findings are intended to assist institutional and student decision-making with the ultimate goals of improving postsecondary student completion.

In last week’s blog, WCET’s Executive Director Ellen Wagner introduced us to the analytics concept:

“’Learning Analytics’ is a term used to describe an emerging professional practice that systematically applies statistics and research methods to large “n” data sets. Analysts look for patterns among the analyzed results that can inform more accountable decision-making.”

The Predictive Analytics Reporting (PAR) Framework that will be created by the grant will aggregate data representing more than 400,000 student records gathered from six WCET member institutions and systems, including:

Each of these postsecondary entities has performed extensive research on their own datasets.  The combination of large data sets across a wide variety of types of institutions (public/private, two-year/four-year, traditional/non-traditional) will seek to find patterns identifiable only through analyzing large data sets representing a widely diverse set of students.

The grant of $1,000,000 over the next six months is planned as the first step in on-going research and promotion on the use of learning analytics to improve service to students.  You can learn more about analytics from the archive of the recent webcast on this topic.  If you’re not already a member, we invite you to join us, as we will be providing much more information on this topic in the coming months.

For more information:

Russ Poulin
Deputy Director, Research & Analysis
WCET – WICHE Cooperative for Educational Technologies
rpoulin@wiche.edu

3 replies on “WCET Awarded Bill & Melinda Gates Grant on Predictive Analytics”

Congratulations! How exciting for WCET. We are very interested in the findings of this project as well. We have a great deal of data collected from SmarterMeasure (READI) and while we aren’t in the predictor business, many of the variables the assessment measures will be indicators of student success. In particular life factors, individual attributes, and learning styles perhaps…please let me know if there is any way we can contribute to the research as a member of WCET.

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