Five Steps for Achieving (Learning) Analytics Success

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1 Five Steps for Achieving (Learning) Analytics Success Ellen D. Wagner Ph.D. Chief Research and Strategy Officer PAR

2 Common Definitions for Today Data is informa*on, everywhere. It comes in all kinds and shapes and sizes. It s not all digital, but most of it is. Analy(cs are methods and tools to parse streams of digital bits and bytes into meaningful pa>erns that can be explored to help stakeholders make more effec*ve decisions. Learning analy(cs are methods and tools needed to parse the stream of digital bits into meaningful pa>erns that explore dimensions of cogni*on, instruc*on and academic experience, including student success. Data- readiness ranges from essen*al individual knowledge and skills to ins*tu*onal capacity for crea*ng a culture that values evidence- based decision- making.

3 DATA ARE CHANGING EVERYTHING

4 Google Trends: Analytics

5 Google Trends: Big Data

6

7 h>p://bit.ly/1gotbmp

8 1 Gigabyte = 1,024 Megabytes 1 Terabyte = 1,024 Gigabytes 1 Petabyte = 1,024 Terabytes 1 Exabyte = 1,024 Petabytes 1 Ze>abyte = 1,024 Exabytes 1 Yo>abyte = 1,024 Ze>abytes 1 ZB 1,000,000,000,000,000,000,000 bytes

9 h>p://bit.ly/1gotbmp

10 h>p://bit.ly/1gotbmp

11 h>p://bit.ly/1gotbmp

12 Making Sense of All The Data

13 Data Readiness in Higher Ed Analy*cs have ramped up everyone s expecta*ons of personaliza*on, accountability and transparency. Academic enterprises simply cannot live outside the ins*tu*onal focus on tangible, measurable results driving IT, finance, recruitment and other mission cri*cal concerns.

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

15 Costs and Completion Rates Gradua7on rates at 150% of 7me yr colleges 4- yr colleges Cohort year Source: New York Times; NCES

16 Performance Based Funding h>p:// research/educ/performance- funding.aspx

17 Institutional Accountability h>p:// educa*on/college- score- card

18 Google Trends: Learning Analytics

19 Google Trends: Predictive Analytics

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

21 The Predictive Analytics Reporting (PAR) Framework PAR is a na*onal, non- profit mul*- ins*tu*onal collabora*ve focused on ins*tu*onal effec*veness and student success. PAR is a big data analysis effort using predic7ve analy7cs to iden*fy drivers related to loss and momentum and to inform student loss preven7on PAR member ins*tu*ons voluntarily contribute de- iden7fied student records to create a single federated database. Descrip*ve, inferen*al and predic*ve analyses have been used to create benchmarks, ins*tu*onal predic7ve models and to map student success interven7ons to predictor behaviors

22

23 Analysis/Modeling Process Analysis and model building is an itera7ve process Around 70-80% efforts are spent on data explora*on and understanding.

24 Structured, Readily Available Data Common data defini*ons = reusable predic*ve models and meaningful comparisons. Openly published via a cc h>ps:// public.datacookbook.co m/public/ins*tu*ons/ par

25 PAR Outputs Descrip7ve Benchmarks Show how ins*tu*ons compare to their peers in student outcomes, by scaling a mul7- ins7tu7onal database for benchmarking and research purposes. Predic7ve Models Iden*fy which students need assistance, by using in- depth, ins7tu7onal specific predic7ve models. Models are unique to the needs and priori*es of our member ins*tu*ons based on their specific data. Interven7on Matrix Ins*tu*ons address areas of weakness iden*fied in benchmarks and models by scaling and leveraging a member, data and literature validated framework for examining interven*ons within and across ins*tu*ons (SSMx)

26 Financial Aid Academic Affairs Faculty Enrollment Management Student Success Ins*tu*onal Research Students IT PAR is redefining ins*tu*onal conversa*ons

27 5 Steps For Achieving (Learning) Analy*cs Success

28 START WITH AN EYE ON YOUR OUTCOMES.

29 BE CLEAR ABOUT WHAT YOU MEAN BY SUCCESS.

30 COMMON DEFINITIONS ENABLE SHARED UNDERSTANDING.

31 FOCUS ON INSIGHTS, NOT JUST ON DATA.

32 SHARE YOUR WORK With thanks to Jane Bozarth, 2014

33 For more informa*on about PAR please visit our website: h>p://parframework.org Ellen Wagner: Twi>er h>p://twi>er.com/edwsonoma Google+ edwsonoma On THANK YOU FOR YOUR INTEREST

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