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Dave King , Unizin/Oregon State University Liv Gjestvang , The Ohio State University

Building Together: The Consortium-based NGDLE Educause Learning Initiative January 31, 2018 9:30 a.m. to 10:15 a.m. Dave King , Unizin/Oregon State University Liv Gjestvang , The Ohio State University Maggie Jesse , University of Iowa Donalee Attardo , University of Minnesota.

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Dave King , Unizin/Oregon State University Liv Gjestvang , The Ohio State University

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  1. Building Together: The Consortium-based NGDLEEducause Learning InitiativeJanuary 31, 2018 9:30 a.m. to 10:15 a.m. • Dave King, Unizin/Oregon State University • Liv Gjestvang, The Ohio State University • Maggie Jesse, University of Iowa • Donalee Attardo, University of Minnesota

  2. Learning Environment Analytics Content Relay Learning Ecosystem Standards-based

  3. Granular Parts of the Learning Ecosystem Content Learner experience UDP UCDM Canvas Learning Ecosystem Advisor Tools Engage Faculty Member Top Hat Early Warning Bridge Tutoring Systems Learner Open EdX Additional Data Instructor Dashboard PressBooks Unizin Common Data Model Unizin Data Platform Advisor Coach

  4. Data Types • Foundational • From SIS • Other sources • Demographic • First gen student • Zip code • Don’t change much • SLOW • Structural • Term based • Enrollment based • Course based • Financial Aid • Change by the term • MEDIUM • Behavioral • Quiz based • Test based • Question/Problem based • Adaptive learning based • Change by the minute • FAST

  5. Steve Scott, Unizin CTOSolving the Learning Data Puzzle: The Unizin Common Data ModelUnizin Bloghttp://unizin.org/2017/09/13/solving-the-learning-data-puzzle-the-unizin-common-data-model/

  6. What kind of data? Person Academic term Course offering Course section Enrollment Demographic data, test scores, etc. Type, season, length, etc. Type, credits, etc. Section number, etc. Learning activities Learning activity result Quiz Quiz item response Grades Learner actions Created and submitted by students All kinds Most kinds Course grade, assignment grades Process data (event stream) Unified View of the Learner

  7. Unify data with a single data ontology UCDM Canvas SIS Engage Demo- graphics TopHat Unizin Common Data Model Engine Other LTI Unified View of the Learner Bridge ????? Open EdX

  8. Unizin Data Platform (UDP) Unified Data Representation Data products and services Teaching and learning systems and tools Student Information System (SIS) Data lake Research datasets Warehouse (batch) data Real-time analytics Learning Management System (LMS) Event stream data LTI tool LTI tool Event stream Event Store LTI tool LTI tool Data marts API data Unizin Common Data Model Engine APIs T&L tool T&L tool Relational Store (for all T&L data) UNIFIED VIEW OF THE LEARNER

  9. UCDM Entities • Person (35) • Institutional affiliation (5) • Academic term (7) • Course offering (7) • Course section (5) • Course section enrollment (8) • Learner activity (12) • Learner activity group (6) • Learning activity override (5) • Learning activity result (10) • Learning group (5) • Quiz (7) • Quiz item (12) • Quiz item response (6) • Quiz response (8) • Course grade (4) • Learner action (special!)

  10. Integrated Capabilities for a Better Learning Ecosystem Data Driven Standards-based Learning Ecosystem Research Content Learner Experience DATA

  11. Granular Parts of the Learning Ecosystem Content Learner experience UDP UCDM Canvas Learning Ecosystem Advisor Tools Engage Faculty Member Top Hat Early Warning Bridge Tutoring Systems Learner Open EdX Additional Data Instructor Dashboard PressBooks Advisor Coach

  12. Personalized Learning at Scale

  13. Liv Gjestvang

  14. AFFORDABLE CONTENT @livgjestvang Ohio State

  15. Pricey Text Books Source: BLS

  16. Why Textbook Affordability?

  17. Why Textbook Affordability?

  18. Hundreds ofhigh-quality, peer-reviewedand collaboratively authoredopen textbooks available.

  19. There is at least one free, open alternative textbook for 16 of our top 20 courses.

  20. 92.7%of faculty will consider using OER

  21. Affordable Learning Exchange focuses on studentsavings and pedagogical change.

  22. PRESSBOOKS

  23. CONTENT CAMP

  24. Three months, six institutions, fourteen faculty.

  25. 3,000 questions

  26. . CC license . Limited access . Best practice . Peer review . Moderators . Upvoting

  27. Maggie Jesse

  28. The Challenge The ongoing search for the right data, to the right people, at the right time, visualized effectively…..

  29. The Strategy • Partner with Unizin member schools • Use and support the Unizin Data Platform (UDP) • Involve local learning analytics researchers, database experts and motivated faculty • Engage students

  30. The Win • Pot of gold = access to our data • Student data aggregation • Learning analytics dashboards • Program outcomes • Research opportunities The Challenge

  31. Donalee Attardo

  32. Unizinand the Canvas LMS Learning Ecosystem Digital Education Help direct the future of digital education, teaching technology, learning analytics, and advising Create the learning ecosystem of the 21st century University Control Cost Keep control of our intellectual property and data Save money for institutions and ultimately students

  33. Decision Points Unizin Services/Products Content Learning Platform (Canvas or Moodle) Vendor Solutions UMN Developed Services/Products Analytics and Data Warehousing

  34. UMN NGDLE Building Together Unizin has provided the framework, tools, and consortial resources to support a new University-wide conversation about how to create a responsive and sustainable NGDLE. Governance User Education System-wide participation in Unizin governance and projects Building understanding of issues related to teaching & learning (data, technology) Gerisima/iStock Community Input Thinking Like a System Providing process and space for community discussion before decision-making Wearing the University hat, not the college or system hat, when doing the work Alignment with UMN Priorities Principles of Unizin align with institutional priorities and mission

  35. Analytics & Data @ UMN Successfully developed a proof of concept to create a common data layer to be created inthe UDW, populated by data from PeopleSoft, Canvas, and APLUS, UMN’s central advising system (developed by our College of Liberal Arts) Work proceeding to utilize UDW in conjunction with in-progress UMN data warehouse redesign UDW data for research beginning to be used by faculty. Faculty-led Learning Analytics Community of Practice began to meet in spring 2017; inquiries to OIT re: what tools would best support learning analytics

  36. Your Turn: Who’s Doing What?

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