learning analytics

A technology to improve STEM retention?

Educational data mining, learning analytics will prove invaluable in helping improve students' course retention.

Colorado State University (CSU) and the McGraw-Hill Education Learning Science Research Council will partner on a new academic research project designed to use learning analytics and educational data mining to improve student retention in STEM courses.

This new research initiative, announced at SXSWedu, will investigate the use of advanced techniques in learning analytics and educational data mining to reduce the Drop-Fail-Withdraw (DFW) rates in STEM gateway courses.

Unsuccessful course completion in these gateway courses is often associated with significantly lower retention and graduation rates. CSU researchers said they are hopeful the data to come from the partnership will inform other courses and faculty insight.

“Learning analytics is developing quickly as an area of academic research, and we want to use this type of research to solve strategic challenges at the university,” said Patrick Burns, CIO, Colorado State University. “We hope to discover new techniques for solving the persistent challenge of high attrition rates in STEM gateway courses. We expect that the research will also benefit other courses and allow faculty to access data and insights in novel ways for enhancing teaching effectiveness.”

(Next page: How predictive models can serve as early indicators for at-risk students)

Laura Ascione

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