March 2015, Education Canaries:
Can Data Rich Systems Predict and Prevent Failure?
One of the significant promises of big data in education is to predict and prevent student failure.
Of course we want to see learners succeed and data can help us with that as well, however, catching students before they get lost or even drop out is essential. Some visionaries see technology as driving learning through increasingly intelligent prompts, assessments of learning, and directing learners to ever more complex and advanced concepts… all driven by models for learning driven by student generated data. More recently many educational leaders have a more holistic view of data and recognize that while we need to educate the whole child, we need to also look at information about the whole child to diagnose and address learning deficiencies and help students on a path where they can be successful. Beyond their inherent interest in helping children, educators and the institutions in which they work are being held accountable to show learner progress. In this show we will explore the many different kinds of data that can be used to monitor student progress, identify those who require assistance, and the systems that make sense of those data to support educators and parents in driving student success.

Addressed in this episode. 

What sorts of data do such systems use?
How do we protect learner privacy while making good use of these data?
How can products leverage these data to provide more effective solutions?
Are educators and administrators being prepared to make use of these sorts of systems?
What are the barriers to implementing these technologies.

Guests at the table:

Barbara S. Clements, Ph.D. Consultant, ESP Solutions Group
Dan Ginsberg, VP Global Product Strategy at Pearson

Bill Meade, Data Science Director at Neal Analytics

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