Big Data: What s the Big Deal?

Transcription

1 Big Data: What s the Big Deal? Ellen D. Wagner Ph.D. Chief Research and Strategy Officer PAR edwsonoma@parframework.org

2 Common Definitions for Today Data are bits of information, everywhere. They comes in all kinds and shapes and sizes. Analytics are methods and tools to parse streams of digital bits into meaningful patterns that can be explored to help stakeholders make more effective decisions. Learning analytics are methods and tools to parse streams of digital bits into meaningful patterns that cognition, instruction and academic experience, including student success. Data-readiness ranges from essential individual knowledge and skills to institutional capacity for creating a culture that values evidence-based decision-making.

3 Big Data Are Changing EVERYTHING

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5 Google Trends

6 Google Trends

7 Google Trends

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9 1 Gigabyte = 1,024 Megabytes 1 Terabyte = 1,024 Gigabytes 1 Petabyte = 1,024 Terabytes 1 Exabyte = 1,024 Petabytes 1 Zettabyte = 1,024 Exabytes 1 Yottabyte = 1,024 Zettabytes 1 ZB 1,000,000,000,000,000,000,000 bytes

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13 Where are we headed? Business Models Provide Guidance Courtesy Phil Ice, APUS

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15 While Big Data raise expectations, student data drive big decisions in.edu

16 Costs and Completion Rates Graduation rates at 150% of time yr colleges 4-yr colleges Cohort year Source: New York Times; NCES

17 Are You Scorecard-Ready?

18 Performance Based Funding and US Post-Secondary Institutions

19 Data Readiness in Higher Ed Analytics have ramped up everyone s expectations of personalization, accountability and transparency. Academic enterprises simply cannot live outside the institutional focus on tangible, measurable results driving IT, finance, recruitment and other mission critical concerns.

20 What do we want? The RIGHT Answers!! When do we want them? NOW!!

21 Use Case: The Predictive Analytics Reporting (PAR) Framework A national, non-profit, multi-institutional collaborative focused on institutional effectiveness and student success. A massive data analysis effort using using predictive analytics to identify drivers related to student risk PAR uses descriptive, inferential and predictive analyses to create benchmarks, institutional predictive models and to inventory, map and measure student success interventions that have direct positive impact on behaviors correlated with success.

22 PAR Framework per the Institute for Higher Education Policy Draft, August 2014, used with permission, IHEP

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24 Structured, Readily Available Data Common data definitions = reusable predictive models and meaningful comparisons. Openly published via a cc book.com/public/institu tions/par

25 PAR Data Inputs 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 Financial Information FAFSA on File Date Pell Received/Awarded Date Student Academic Progress Curent Major/CIP Earned Credential/CIP Course Catalog Subject Course Number Subject Long Course Title Course Description Credit Range 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 ** Future

26 PAR Outputs Descriptive Benchmarks Show how institutions compare to their peers in student outcomes, by scaling a multiinstitutional database for benchmarking and research purposes. 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. Intervention Matrix Institutions address areas of weakness identified in benchmarks and models by scaling and leveraging a member, data and literature validated framework for examining interventions within and across institutions (SSMx)

27 5 Steps For Achieving (Learning) Analytics Success

28 START WITH AN EYE ON YOUR OUTCOMES.

29 BE CLEAR ABOUT WHAT YOU MEAN BY SUCCESS.

30 SHARED UNDERSTANDINGS ENABLE COMPARISONS, COLLABORATION.

31 FOCUS ON INSIGHTS, NOT JUST ON DATA.

32 SHARE YOUR WORK

33 For more information about PAR please visit our website: Ellen Wagner: Twitter Google+ edwsonoma On THANK YOU FOR YOUR INTEREST