IMS Global Learning Analytics Summit 2019
Opening New Opportunities to Use Data for Maximum Learning Impact
Learning Analytics Summit: 13 November 2019
Location: Microsoft, 15010 Northeast 36th Street, Redmond, WA 98052
Special Thanks to Our Meeting Host:
Hotel: We have secured room blocks at the Aloft Seattle Redmond and the Element Seattle Redmond, 15220 NE Shen Street, Redmond, Washington 98052. Please make your hotel reservation no later than Monday, 21 October.
Registration Options & Rates (through 28 October):
- Quarterly Meeting & Learning Analytics Summit (IMS Members Only) – $450
- Learning Analytics Summit: One Day Only (Open to All) – $300
Registration Fee Deadlines & Policy:
Please complete your registration by 28 October 2019. Registrations received after 28 October will be assessed a $100 late fee. Online registration ends 5 November. Onsite registration is an additional $200. All cancellations received on or before 28 October are eligible for a full refund of fees paid, less a $20 processing fee. Absolutely no refunds will be issued after 28 October but you may transfer your paid registration to another person.
The annual IMS Global Learning Analytics Summit is held during the November Quarterly Meeting for IMS members. The Summit is open to all organizations.
Thank You for Your Support and Leadership!
Learning Analytics Summit 2019 Sponsors
Interested in sponsorship? Contact us for details.
Wednesday – 13 November
Opening New Opportunities to Use Data for Maximum Learning Impact
|Open to IMS Members and the Public/Non-Members|
|7:30 AM – 8:30 AM||
|8:30 AM – 8:45 AM||
Welcome and Opening Comments
Rob Abel, CEO, IMS Global Learning Consortium
|8:45 AM – 9:15 AM||
Groping in the Dark: Getting Real About Learning Analytics
Michael Feldstein, Chief Accountability Officer, e-Literate
The natural exuberance for applying new data science techniques to learning analytics is slowly giving way to the equally understandable puzzlement and frustration over lack of rapid progress in this domain. Because cognitive processes are fundamentally unobservable, digital learning environments are and always will be radically data-impoverished relative to the task at hand. In other words, no matter how much data we gather about clicks within the learning environment, it will never be enough for machines to learn about learning through algorithm training processes alone.
Michael will argue that the way to make progress in learning analytics is through an interdisciplinary, theory-driven approach that applies data science techniques in the service of testing hypotheses about learning that have drawn from other bodies of evidence. IMS is in a particularly strong position to be a leader in this kind of work but would need to add some approaches to its standards-making facilitation to do so.
|9:15 AM – 10:00 AM||
State of Analytics: Where Are We Now and What’s Next
Rachel Scherer, Blackboard
Amin Qazi, University of California, San Diego
This interactive discussion led by the IMS Analytics Product Steering Committee chairs shares insights into the work of the IMS member community related to the adoption of Caliper Analytics, student data privacy, integrated analytics, and much more. Bring your questions and ideas for how IMS can help K-20 institutions advance the use of analytics to impact education.
|10:00 AM – 10:30 AM||
|10:30 AM – 11:00 AM||
Student Data Privacy in Learning Analytics
|11:00 AM – 12:00 PM||
Lighting Talks: Highlighting K-12 and Higher Education Successes, Roadblocks, and What's Next
|1:00 PM – 1:30 PM||
Finding a Black Cat in the Dark Room: The Integrated Analytics Ecosystem
Bracken Mosbacker, Technical Standards Architect, IMS Global
In the past, IMS has traditionally focused on the development and promulgation of standards around problems that were well understood, broadly practiced, and relatively well defined: the specification process was a primarily technical undertaking. As the education community continues to struggle with the definition of an analytics ecosystem, however, it is increasingly clear that from a standards perspective, this is new territory.
While we can solve the technical aspects of moving data between systems and even analyzing that data, foundational issues are emerging around not just how, but what and why. We're grappling with coming to common understandings of the problems we’re trying to solve, both within the larger community and within institutions themselves. What does an integrated analytics ecosystem look like, or even mean? And what is the appropriate role for IMS in this discussion?
|1:00 PM – 2:15 PM||
Supplier Lightning Talks: Real-World Implementation
|2:15 PM – 2:45 PM||
|2:45 PM – 3:15 PM||
Mapping Georgia's Academic Genome: Connect Data from K to 20
As the requirements and roles for academic data increase within Georgia’s education sectors, conversations and opportunities to share and connect data across governmental units are growing as well. These promising discussions are occurring at both the institutional level and at the Georgia Department of Education and the University System of Georgia level. While not without challenges, the opportunities are obvious and enticing. This panel will focus on how determined partnerships are envisioning and specifying the next generation of academic data and analytics for education in Georgia.
|3:15 PM – 4:00 PM||
Evidence-based Courseware Improvement with OpenSimon Analytics
Norman Bier, Director of OLI and Executive Director for the Simon Initiative, Carnegie Mellon University
Carnegie Mellon University’s Open Learning Initiative and broader learning engineering community has designed data capturing courseware with sophisticated analytic techniques to support the iterative improvement of courseware to demonstrably enact learning. This workshop will give participants the opportunity to put these analytics to work, using real data to inform improvements to an OLI course. CMU’s Simon Initiative is working to accelerate the learning engineering approach, and disseminate it’s adoption to a broader community. Through the OpenSimon toolkit, we offer a set of open and integrated educational technologies and methods that any person or institution can adopt to improve outcomes for their own learners.
Join us as we share the Simon approach to learning engineering, the technologies to support each part of the iterative improvement process, and the role of data in the design and improvement of learning materials. Participants will engage in a hands-on exercise to use analytics to improve courseware -- exploring sources of educational data, analyzing that data, and enacting improvements.
|4:00 PM – 4:30 PM||
Eliminating Equity Gaps through Data and Analytics
Timothy M. Renick, Senior Vice President for Student Success and Professor, Georgia State University
For the past decade, Georgia State University has been at the leading edge of demographic shifts in the southeast. While doubling the numbers of non-white and low-income students it enrolls, the university has simultaneously committed to the use of data to inform systematic institutional change. In the process, Georgia State has raised graduation rates by 23 percentage points and closed all achievement gaps based on race, ethnicity, and income-level. It now awards more bachelor’s degrees to African Americans than any other non-profit college or university in the nation. Through a discussion of a series of analytics-based innovations ranging from chat bots and predictive analytics to meta-majors and completion grants, the session will cover lessons learned from Georgia State’s transformation and outline several practical and low-cost steps that campuses can take to improve outcomes for underserved students.