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Five Steps for Achieving Learning Analytics Success
The document outlines five steps for achieving success with learning analytics: 1) Start with a focus on desired outcomes, 2) Clearly define what constitutes success, 3) Use common definitions to enable shared understanding, 4) Focus on generating insights from data rather than just collecting data, and 5) Share results and findings with others. It then provides more details on the Predictive Analytics Reporting (PAR) Framework, a collaborative focused on institutional effectiveness and student success using predictive analytics of integrated student data from member institutions.
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Five Steps for Achieving Learning Analytics Success
- 1. Five Steps forAchieving (Learning) Analytics Success Ellen D. Wagner Ph.D. Chief Research and Strategy Officer PAR Framework @edwsonoma edwsonoma@gmail.com
- 2. Common Definitions forToday 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 CHANGINGEVERYTHING
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- 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
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- 12. Making Sense of All The Data
- 13. Data Readiness inHigher 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 Dataraise expectations, student data drive big decisions in .edu
- 15. Costs and CompletionRates Source: New York Times; NCES 0 10 20 30 40 50 60 70 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2-‐yr colleges 4-‐yr colleges Gradua7on rates at 150% of 7me Cohort year
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- 18. Google Trends: LearningAnalytics
- 19. Google Trends: PredictiveAnalytics
- 20. What do wewant? The RIGHT Answers!! When do we want them? NOW!!
- 21. The Predictive AnalyticsReporting (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
- 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 AvailableData • Common data defini*ons = reusable predic*ve models and meaningful comparisons. • Openly published via a cc license @ 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. 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) Interven7on Matrix
- 26. Faculty Student Success IT Academic Affairs Enrollment Management Financial Aid Ins*tu*onal Research PAR is redefining ins*tu*onal conversa*ons Students
- 27. 5 Steps For Achieving (Learning) Analy*cs Success
- 28. START WITH ANEYE ON YOUR OUTCOMES.
- 29. BE CLEAR ABOUTWHAT YOU MEAN BY SUCCESS.
- 30. COMMON DEFINITIONS ENABLE SHAREDUNDERSTANDING.
- 31. FOCUS ON INSIGHTS, NOTJUST ON DATA.
- 32. SHARE YOUR WORK With thanks to Jane Bozarth, 2014
- 33. THANK YOU FORYOUR INTEREST 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 email edwsonoma@gmail.com