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+. WHAT PAR IS:  Predictive modeling to determine student attrition risk  Visual presentation of commonly defined data to allow for benchmarking to.

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Presentation on theme: "+. WHAT PAR IS:  Predictive modeling to determine student attrition risk  Visual presentation of commonly defined data to allow for benchmarking to."— Presentation transcript:

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2 WHAT PAR IS:  Predictive modeling to determine student attrition risk  Visual presentation of commonly defined data to allow for benchmarking to comparison groups  Currently being implemented at all institutions in the NDUS  Recently acquired by Hobson’s (and Starfish) WHAT PAR IS NOT:  NOT a required tool for faculty or advisors  NOT to replace other measures of student success and progression  NOT just at UND  NOT just another software separate from what we already have

3 DATA INPUTS:  Student demographics and descriptive data – gender, race, prior credits, high school information, transfer GPA  Student course information – subject, start/end dates, grades, delivery mode  Student financial information – FAFSA on file, Pell received  Student academic progress (called credential) provides the major, not student progress in the major  Course catalog – subject, course number, course title, course description, credit range  Over 70 different individual data points  UND data from 2009-2015 – data added each semester PAR HIGHLIGHTS FOR UND:  Taking a majority of courses online  Non-traditional age (older than 24)  Low on-going GPA (less than 2.0)  Developmental Education Requirements more than 50% of the courses taken

4 MOST UND STUDENTS ARE HERE: ZERO ONLINE COURSES HIGH RISK FOR COURSE SUCCESS & RETENTION FULLY ONLINE RESULTS

5 RESULTS

6 NOW PAR RESULTS DRIVE STARFISH FLAGS: Students that drop 3-6 or 6+ credits Both student and advisor receive an e-mail STARFISH PROGRESS SURVEY: A separate survey and communications plan is used for online courses to address the retention concerns raised by PAR results for our online population SOON PAR SCORE TO PREDICT LIKELIHOOD OF RETAINING EACH STUDENT Available in Starfish FUTURE Course Explorer: Identify and explore barrier courses Student Explorer: Enhanced predictive risk score

7 integrated COACHING & ADVISING NETWORK FALL 2016 iCAN: Pilot iCAN concepts in College of Business & Public Administration & Student Success Center meaningful interventions tracking student outcomes THAT MATTER results

8 WHO CAN HELP DATA STRATEGY TEAM:  Lori Lindenberg – lori.Lindenberg@und.edulori.Lindenberg@und.edu  John Mitzel – john.mitzel2@und.edujohn.mitzel2@und.edu  PHONE: 777-2498 iCAN GROUP  Thomasine Heitkamp – thomasine.heitkamp@und.edu  Lisa Burger – lisa.burger@und.edulisa.burger@und.edu Data leads for PAR Analyzing PAR results at an aggregate level Developing a plan for real-time scoring in Starfish Developing a methodology for tracking interventions to integrate into model and drive real-time adjustments to PAR scores in Starfish iCAN Group Co-Chairs Leading Starfish implementation Driving iCAN pilot Creating faculty champions for advising & retention by involving faculty in decisions


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