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Predictive Analytics Reporting (PAR) Framework: Overview, Applications, Results Ellen Wagner Chief Research and Strategy Officer June 18, 2014 @edwsonoma
Are You “Scorecard-Ready”? http: //collegecost. ed. gov/
Performance Based Funding and US Post-Secondary Institutions http: //www. ncsl. org/research/education/performance-funding. aspx
A Stronger Nation Through Higher Education: Lumina Foundation, April 2014 http: //strongernation. luminafoundation. org/report/
While “Big Data” raise expectations, student data drive big decisions in. edu
How Do Institutions Deal With the “Apples to Zebras” Problem?
National Non-profit Multi-institutional Collaborative Institutional Effectiveness + Student Success
PAR: “standard, equal, normal”
The Predictive Analytics Reporting (PAR) Framework • PAR is a “massive data” analysis effort using predictive analytics to identify drivers related to loss and momentum and to inform student loss prevention • PAR member institutions voluntarily contribute de-identified student records to create a single federated database. • Descriptive, inferential and predictive analyses have been used to create benchmarks, institutional predictive models and to map student success interventions to predictor behaviors
PAR Framework video introduction https: //www. dropbox. com/s/ll 6 qmo 9 fru 869 un/ PAR_1080 p_storyeyed. mp 4
PAR distributes efforts associated with analysis and modeling processes • Analysis and model building is an iterative process • Around 70 -80% efforts are spent on data exploration and understanding.
PAR’s Common Data Definitions Enable Shared Understandings and Results.
PAR uses structured, readily available data from all of its members for generalizability • Common data definitions = reusable predictive models and meaningful comparisons. • Openly published via a cc license @ https: //public. datacook book. com/public/institu tions/par
PAR Input data are available for ALL students from ALL US institutions Student Demographics & Descriptive Gender Race Prior Credits Perm Res Zip Code HS Information Transfer GPA Student Type Course Catalog Subject Course Number Subject Long Course Title Course Description Credit Range Student Course Information Course Location Subject Course Number Section Start/End Dates Initial/Final Grade Delivery Mode Instructor Status Course Credit Lookup Tables Credential Types Offered Course Enrollment Periods Student Types Instructor Status Delivery Modes Grade Codes Institution Characteristics Student Financial Information FAFSA on File – Date Pell Received/Awarded – Date Student Academic Progress Curent Major/CIP Earned Credential/CIP Possible Additional ** Placement Tests NSC Information SES Information Satisfaction Surveys College Readiness Surveys Intervention Measures ** Future
PAR’s Actionable Benefits/Outcomes IDENTIFY: Benchmarks Show institutions compare to their peers in student outcomes, by scaling a multiinstitutional database for benchmarking and research purposes. TARGET: Predictive models Identify which students need assistance, by using in -depth, institutional specific predictive models. Models are unique to the needs and priorities of our member institutions based on their specific data. TREAT: Intervention measures Determine best ways to address weaknesses identified in benchmarks and models by scaling and leveraging a member, data and literature validated framework for examining interventions within and across institutions (SSMx).
Feedback loops for enabling institutional performance improvements Performance Benchmarks Measurable Results Action Common Data Definitions and Data Warehouse Intervention Benchmarks Predictive Models Scalable cross institutional improvements enabled by Collaboration via PAR
Descriptive and Predictive Insight PAR Benchmarks Descriptive Analytics Cross Institutional Student/degree/major level insight into: 1. What did the retention look like for students entering in the same cohort 2. How does your institution compare to peer institutions / institutions in other sectors 3. How did performance vary by student attributes PAR Models Predictive Analytics Institutional Specific insight into: 1. 2. 3. 4. What students are being retained over time? Which students are currently at risk for completing and why? Which factors are directly correlated to student success? What is the predicted course completion rate for a particular program?
Collaborative Benchmarking Student-level data + common data definitions = deeply drillable comparative reports Partners determine measures and content
INSTITUTIONAL SPECIFIC PREDICTIVE MODELS Institution X
Predicting retention aimed at taking action finding the most important factors
Actionable information at the student level 1 st, 2 nd and 3 rd most important factors contributing to risk PAR anonymized ID Risk they will not be retained
Student Success Matrix (SSMX) Review • Inventorying & categorizing student success interventions/ supports using a common framework – Based on known predictors of risk and success – In the context of the academic life cycle • Addresses “Now What? ” by linking predictions to action – Enables cross institutional benchmarking – Supports local and cross institutional cost/ benefit analyses. ©PAR Framework 2013
SSMX Progress From this To this Launched June 2013 Student Success Matrix (SSMx) Publically available, 1, 400+ downloads https: //public. datacookbook. com/public/institutions/par Launched April 22, 2014 Members only, managed environment ©PAR Framework 2013
Comprehensive view – completed SSMx ©PAR Framework 2013
©PAR Framework 2013
Examine interventions by predictor category ©PAR Framework 2013
©PAR Framework 2013
Isolate interventions Find gaps ©PAR Framework 2013
Applying Interventions at the Greatest point of Need/Value • A fundamental objective for developing common language and frameworks for reviewing student interventions is so that the most effective interventions can be applied at the points of greatest need to effectively remediate risk at the student level. • PAR has paved the way for creating common understanding of student risk and common tools for diagnosing risk, but the road to developing consistent and applied measurement to student impact of intervention will take time and vigilance.
From Hindsight to Foresight
PAR Futures • PAR, Inc. , a 501. c. 3 non-profit educational organization launching Dec 9, 2014 as an Analytics-As-A-Service (AAAS) provider. • PAR will focus on benchmarks, predictive models, the student success intervention mapping and measurement, “Rosetta Stone” cross-walks to other data projects and platform providers. • New reports that emphasize pathways to achieving outcomes (e. g. Adult learners, PLAs, CBE). • New reports that consider “big issues” impact on learning outcomes, e. g. , online-blended-onground programs; for-profitpublic-private institutions. • Support/resources/services for community of research and practice.
Thank you! http: //parframework. org @PARFRamework