eLearning Roadtrip

PAR Framework: Learning Analytics as a Service for Higher Ed

Thanks to Libby Nelson's great article Big Data 101: Colleges are hoping predictive analytics can fix their dismal graduation rates, we have been fielding a lot of questions about the PAR Framework. Here is an update on who we are and the work in which we are engaged.

MEET THE PAR FRAMEWORK - The Predictive Analytics Reporting (PAR) Framework is a national, non-profit provider of learning analytics as a service. PAR brings 2-year, 4-year, public, proprietary, traditional and progressive colleges and universities together to collaborate on mitigating student loss by identifying the effective practices that support student progress toward their academic goals. The PAR Framework offers a unique multi-institutional perspective for examining dimensions of student success that will help improve retention in US higher education.  PAR is distinguished among the many data analytics solutions emerging in the education domain by its common, openly published data definitions and student success frameworks.

HOW PAR WORKS - PAR uses predictive analytics to deliver tools and reports aimed at improving academic success for students in online, blended and on-ground programs. PAR members receive flexible predictive models, cross-institutional benchmarks, and an intervention insight toolkit supporting a student success methodology that links predictions with interventions and student supports.

• PAR institutions provide a full set of anonymized undergraduate student data that allows for comparative investigation of student success trends over time, at the individual student, course and degree level. (PAR members provide periodic updates of their data ensuring the ability to measure changes over time, the impact of student interventions and enabling the predictive models that PAR produces to be adjusted and tuned for current data.)

• In return, PAR institutions receive actionable reports and student watch lists developed using predictive analytics.  Once institutions deliver their student-anonymized and institutionally de-identified data, PAR researchers and data scientists use statistical and data mining techniques to identify and reveal the factors that most impact student success in the combined dataset and the individual institution level.  Members quickly receive institutionally-specific models that predict the likelihood of each student achieving institutional specific milestones. These include but are not limited to passing a course or courses and being retained at the institution.  Member institutions also receive academic models for their institution that include detailed student level data to create watch lists, structure interventions and inform local decisions about the factors identified as having the greatest impact on student success. They also receive institutionally anonymized and de-identified findings from the entire dataset that yield over-arching insights about retention patterns in U.S. higher education to inform practice and policy.

PAR TOOLS FOR BENCHMARKING:

• The PAR Student Success Matrix (SSM^X) helps institutions comprehensively assess their student success policies, interventions and programs. The SSM^X makes it possible to organize the wide variety of student supports – from orientation to mentoring to advising – into a systematic validated framework designed to quantify the impact of student success practices and determine the best support for students at the point of need.  The SSM^x also reveals gaps and overlaps in student support programs and gives institutions the tools to evaluate the efficacy of their investments at the program level.  
•  Quantified intervention effectiveness results. The common PAR measures for assessing and predicting risk and the validated frameworks categorizing student support services create the mechanism to effectively measure the impact of student supports within and across institutions.  PAR member institutions are encouraged to take part in quantitative, closed loop field tests, measuring the effect of targeted student supports (e.g. tutoring, student services, email, text messages alerts) against at-risk students.
• Institutional benchmarks. PAR Framework members receive institutionally-reflective and cross-institutional benchmark reports of student level outcomes such as retention, successful credit accumulation, academic performance indicators, and earning a credential.

COMPREHENSIVE INSIGHTS BASED ON VARIATIONS IN:

• Student demographics
• Financial aid factors
• Traditional vs Non-traditional status
• Full-time/Part-time status
• Major/Program pursued
• Credential pursued
• Online vs. On-ground vs. Other modalities of course taking
• Prior academic history

PARTICIPATION IN A COLLABORATIVE COMMUNITY OF EXPERTS: PAR Framework members represent an engaged community of forward thinking institutions, each with perspective and practices around student retention, progression, and completion. PAR membership provides institutional leaders with the models, technical support, services, and guidance needed to evaluate techniques, tools and solutions used to improve student outcomes. PAR provides the collaborative environment needed to support policy implementation and effective practice guidance necessary to capitalize on the power of data-driven decision-making for improving student success in reliable, repeatable, scalable ways.

 For more information about joining PAR, please contact us at [email protected].

 

Karen Swan and the Amazing Multi-institutional PAR Student Success Matrix

Back in June 2013 the PAR Framework openly published an intervention assessment framework called the Student Success Matrix to help PAR members figure out how to respond when confronted with predictions of which students were likely to drop out. "What should we do to respond to these predictions showing our students at risk?" our members asked.  At that time Dr. Karen Swan, Stukel Distinguished Professor of Educational Leadership at the University of Illinois Springfield, reminded us that there are already lots and lots and lots of things that academic advisors, faculty and other student success professional already do to help students succeed in school. Going back through the professional literature, she and Dr. Peter Shea, SUNY-Albany, helped us find models of student engagement and participation relevant to our work at hand.

Karen proposed a framework to map interventions using predictor behaviors of success and points in the academic completion cycle.  Over the past year Karen and Peter have worked closely with Sandy Daston, PAR Framework Director of Student Success and the entire PAR SSMx working group (Hae Okimoto, University of Hawaii System; Jennifer Freed, Dan Huston, and Fermin Ornelas, Rio Salado College; David Shulman, Broward College; Denise Nadasen, Jack Neill and Karen Vignare, UMUC; Patsy Moskal and DeLayne Priest, University of Central Florida; Michelle Wiley, Penn State World Campus; Mysti Reneau, Community Colleges of Spokane) to refine processes and tools for promoting intervention measurement. This group of experienced educational practitioners and decisionmaker has helped determine what can actually be done for student at risk - even down to knowing how to support those 37 students that are most likely to fail Math 101 this semester. 

PAR institutions have begun the process of inventorying, tagging, describing measuring and evaluating literally hundreds of interventions currenty in use on campuses to help student succeed. Thanks to the SSMx we have accelerated the process of connecting predictions of risk to the interventions that will mitigate risk. 

This post is to acknowledge Karen's leadership and contributions to PAR's thinking about using predictions as cataysts for taking action, and for linking interventions to predictions to underscore the importance of linking predictions to action.