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Beverly Woolf <[log in to unmask]>
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Beverly Woolf <[log in to unmask]>
Mon, 30 Oct 2017 14:56:35 -0400
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The Educational Data Mining Competition.

This competition invites participants to make long term predictions about students’ academic and career achievements based on students’ work in online mathematics tutors 10 years earlier.. This NSF initiative is designed to spur progress in educational research using big data.  Competing individuals/teams use educational data from ASSISTments, an intelligent tutoring system for middle school mathematics, to make long term predictions regarding STEM career entry from 7th grade click-stream data. The 2017 ASSISTments Data Mining Competition is sponsored by the Big Data for Education Spoke of the Northeast Big Data Hub. 

Why do we need this?    School are already using 'drop out' detectors for early warning system, but they also need early warning systems for 'these students are losing interest in STEM' detectors. '  The results of this competition could help inform the design of systems that will help try to reignite student's interest in studying STEM. 

Successful entries will be invited to submit both to a conference workshop (at EDM2018, in Buffalo, NY) and to a special issue of the Journal of Educational Data Mining.  Currently, more than 40 researchers have added their predictions to the competition board - apply your own cross-validated prediction models using your preferred data mining techniques and join in our pursuit to better understand the predictive powers of early STEM engagement. For more information, please visit <>.

Beverly Woolf, [log in to unmask]
Neil Heffernan, [log in to unmask]

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