What registrants said about this webcast:

• "Outstanding, exceeded expectations; we had 19 people watching!"
• "I am hopeful that faculty will incorporate this into what they are doing in Blackboard."
• "Great presentation by all those involved–provided a lot for us to think about as we begin to use predictive analytics at our university."
• "Fabulous session! Could have been much longer!"

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As higher education is confronted with fewer resources, increasing demands, and higher expectations, the imperative to help every enrolled student to graduate acquires greater urgency.

Predictive analytics offers an important new avenue for identifying at-risk students so that effective, timely intervention can occur to improve student learning.

Institutions are already actively experimenting with and implementing these new tools. With appropriate and timely support the academy knows it can do a much better job of helping students complete their academic program. But how can we know which students need our help—and when?

This two-hour webcast from AASCU and SCUP features several of the key predictive analytics practitioners who examine the experiences of their respective four institutions, touch upon the different approaches and working models used to develop reliable predictions for interventions, illustrate with screen shots, and address the success track records.

Two of the featured institutions developed their own systems, and two institutions are using commercial products—which were born on university campuses and now benefit from full entrepreneurial support.

• Rio Salado College developed their own system: STARS (Students at Risk)
• Capella University has developed its own in-house predictive modeling methodologies, and uses proprietary algorithms and tools
• Ball State University partnered with EBI to develop and use MAP-Works, which is now a commercial product
• Purdue University uses Signals, a product from SunGard

At the end of the webcast, we will include a brief look at the data, approaches, and statistical methods needed to develop a predictive system. There will be time for questions and answers with the presenters. Don't miss this informative program!

Learning Outcomes From This Webcast:

• Build your awareness of the various emerging tools to support student success
• Consider how to use the predictive data for student interventions
• Understand the kind of data needed to support predictive analytics
• Assess the techniques and processes for building a predictive analytics system

• Presidents/CEOs
• Academic leaders/deans
• Institutional research professionals
• Retention officers
• Student affairs leaders
• Faculty leaders
• Enrollment management professionals

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