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Predictive Analytics Reporting (PAR) Framework: Overview, Applications, Results Ellen Wagner Chief Research and Strategy Officer June 18,

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Presentation on theme: "Predictive Analytics Reporting (PAR) Framework: Overview, Applications, Results Ellen Wagner Chief Research and Strategy Officer June 18,"— Presentation transcript:

1 Predictive Analytics Reporting (PAR) Framework: Overview, Applications, Results Ellen Wagner Chief Research and Strategy Officer June 18,

2 Are You “Scorecard-Ready”?

3 Performance Based Funding and US Post-Secondary Institutions

4 A Stronger Nation Through Higher Education: Lumina Foundation, April 2014

5 While “Big Data” raise expectations, student data drive big decisions in.edu

6 How Do Institutions Deal With the “Apples to Zebras” Problem?

7 Collaborative National Multi-institutional Non-profit Institutional Effectiveness + Student Success

8 PAR: “standard, equal, normal”

9 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

10 PAR Framework video introduction https://www.dropbox.com/s/ll6qmo9fru869un/ PAR_1080p_storyeyed.mp4

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12 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.

13 PAR’s Common Data Definitions Enable Shared Understandings and Results.

14 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 https://public.datacook book.com/public/institu tions/par https://public.datacook book.com/public/institu tions/par

15 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 Student Demographics & Descriptive Gender Race Prior Credits Perm Res Zip Code HS Information Transfer GPA Student Type Student Course Information Course Location Subject Course Number Section Start/End Dates Initial/Final Grade Delivery Mode Instructor Status Course Credit Student Course Information Course Location Subject Course Number Section Start/End Dates Initial/Final Grade Delivery Mode Instructor Status Course Credit Student Academic Progress Curent Major/CIP Earned Credential/CIP Student Academic Progress Curent Major/CIP Earned Credential/CIP Student Financial Information FAFSA on File – Date Pell Received/Awarded – Date Student Financial Information FAFSA on File – Date Pell Received/Awarded – Date Course Catalog Subject Course Number Subject Long Course Title Course Description Credit Range Course Catalog Subject Course Number Subject Long Course Title Course Description Credit Range ** Future Lookup Tables Credential Types Offered Course Enrollment Periods Student Types Instructor Status Delivery Modes Grade Codes Institution Characteristics Lookup Tables Credential Types Offered Course Enrollment Periods Student Types Instructor Status Delivery Modes Grade Codes Institution Characteristics Possible Additional ** Placement Tests NSC Information SES Information Satisfaction Surveys College Readiness Surveys Intervention Measures Possible Additional ** Placement Tests NSC Information SES Information Satisfaction Surveys College Readiness Surveys Intervention Measures

16 PAR’s Actionable Benefits/Outcomes IDENTIFY: Benchmarks Show how institutions compare to their peers in student outcomes, by scaling a multi- institutional 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. 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). TREAT: Intervention measures

17 Feedback loops for enabling institutional performance improvements Performance Benchmarks Intervention Benchmarks Predictive Models Action Measurable Results Common Data Definitions and Data Warehouse Scalable cross institutional improvements enabled by Collaboration via PAR

18 Descriptive and Predictive Insight 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 Institutional Specific insight into : 1.What students are being retained over time? 2.Which students are currently at risk for completing and why? 3.Which factors are directly correlated to student success? 4.What is the predicted course completion rate for a particular program? PAR Benchmarks Descriptive Analytics PAR Models Predictive Analytics

19 Collaborative Benchmarking Student-level data + common data definitions = deeply drillable comparative reports Partners determine measures and content

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25 INSTITUTIONAL SPECIFIC PREDICTIVE MODELS Institution X

26 Predicting retention aimed at taking action - finding the most important factors

27 Actionable information at the student level PAR anonymized ID 1 st, 2 nd and 3 rd most important factors contributing to risk Risk they will not be retained

28 Student Success Matrix (SSM X ) 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

29 From this ©PAR Framework 2013 Launched June 2013 Student Success Matrix (SSM x ) Publically available, 1,400+ downloads https://public.datacookbook.com/public/institutions/par https://public.datacookbook.com/public/institutions/par Launched April 22, 2014 Members only, managed environment To this SSM X Progress

30 Comprehensive view – completed SSM x ©PAR Framework 2013

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32 Examine interventions by predictor category ©PAR Framework 2013

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34 Isolate interventions Find gaps

35 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.

36 From Hindsight to Foresight

37 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-profit- public-private institutions. Support/resources/services for community of research and practice.

38 Thank you!


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