Mining Academic Analytics for Student Success on Any Budget

Mining Academic Analytics for Student Success on Any Budget

Where: College of the Holy Cross
Hogan Campus Center Third Floor
College Street Gate 7
Worcester, MA
Directions and Campus Map 

Carpool information to this event can be found here.

When: Tuesday, April 23, 2019
9:00am - 3:00pm 
Note: Registration starts at 7:30am

Workshop Organizer: David Demers of University of Maine System and Amber Vaill of Becker College

Registration Fee:
NERCOMP Member: $145
Non Member $290
Your fee includes am break and lunch.

Event Overview
Predictive analytics have proven valuable in identifying risk factors amongst student populations. ‘Traditional’ approaches have relied on human-initiated interaction (e.g. instructor ‘flags’) or predictive models based on historical institutional data (e.g. final course grade resulting, in some instances, in risk detection that come too late).

This workshop will explore a broad array of enhanced predictive analytics approaches to inform the delivery of early alerts for student success initiatives, including mining transactional student activity data within the SIS, LMS and additional data systems.  

The approaches that will be explored during the workshop will cover a wide array of software tools and platforms to demonstrate how to get started with Academic Analytics regardless of available budget!  

The workshop is intended to bring together professionals with interest in data mining and data-driven student enrollment and success strategies to explore, discuss and debate the merits and pitfalls of a variety of approaches ranging from fully home-grown systems to leveraging full vendor-based retention management systems.

Session Outcomes:
 - Understand the levels of analytics and the value and limitations of each.
 - Understand the benefits and limitations of a variety of analytics and data mining approaches, including funding requirements.
 - Understand the concept of predictive modeling and the balance between the ‘art’ and ‘science’ of utilizing predictive scores.
 - Understand there is no ‘one-size-fits-all’ approach to academic analytics and what may work for one institution may not work for another.



Registration Cancellation Policy:
By clicking on the "Order Now" button, you are indicating a commitment to attend and will be held responsible for the registration fee. Your fee can be refunded if you notify us of a cancellation at least 8 days prior to the event via email to

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