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NSF Learning Analytics Workshop

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This NSF workshop brings together learning and data scientists with various backgrounds and prior expertise to collaboratively solve the research challenges of development of instruction, assessment of competence of current and would-be workers, and evaluation of learning tools.  Three specific questions will be addressed: how to define competence, measure competence and evaluate new approaches to learning.  Speakers are invited from across industry and academia to ensure a broad perspective and specifically to take into account employer’s perspectives.  Please join us for an exciting event and lively discussions.

Please register if you would like to attend.

Schedule:
March 18, 2019
8:00 a.m. – Registration
8:30 a.m.  – Welcome and Introductions

  • Stephanie Teasley, Research Professor, School of Information, University of Michigan
  • Rada Mihalcea, Professor, Computer Science and Engineering, University of Michigan
  • Henry Kelly, Senior Scientist, Michigan Institute for Data Science, University of Michigan

8:45 a.m. – Talks and discussion on defining competence

Marie Cini

President and CEO

The Council for Adult and Experiential Learning

 

 

David Blake

CEO

Degreed

 

 

9:40 a.m. – Talks and discussion on measuring competence

Bror Saxberg

Vice President

Learning Science at Chan Zuckerberg Initiative

 

 

Tammy Wang

Vice President

Data Science and Analytics at Riviera Partners

 

 

10:35 a.m. – Talks and discussion on evaluating new approaches to learning

Norman Bier

Director

Open Learning Initiative, DataLab, Carnegie Mellon University

 

 

Yun Jin Rho

Director

Efficacy Analytics and Studies, Pearson

 

 

11:30 a.m. to 12:00 p.m. – Networking

March 19, 2019

9:45 a.m. to 12:50 p.m. –  Panel discussions on each of the three topics
12:50 p.m. – Concluding remarks and discussion of next steps
1:00 p.m. – Adjourn

Peers Health and U-M begin research partnership using disability and workers’ comp healthcare data

By | General Interest, Happenings, News, Research

Peers Health and the University of Michigan are starting a two-year research project that will apply advanced learning technologies to a proprietary global database of millions of de-identified disability and workers’ compensation cases. The goals of the project include developing a prescriptive modeling framework to facilitate development of optimal return-to-work plans for injured or ill patients.

Public policy experts have begun to connect patients’ ability to perform their productive endeavors, such as their job, to their state of general health and well-being. The findings from this project, by helping define when someone objectively has returned to health, could inform decision-making in virtually every healthcare episode.

The principal investigators in the project, Dr. Brian Denton and Dr. Jenna Wiens, are both renowned experts in medical machine learning. Dr. Denton, a professor of Industrial and Operations Engineering and Urology, and Dr. Wiens, an assistant professor of Computer Science and Engineering, are both affiliated with the Michigan Institute of Data Science (MIDAS) at U-M.

Peers Health recently announced an expanded partnership with ODG, an MCG company and part of the Hearst Health Network, to aggressively acquire new data to enhance ODG functionality and to fuel this research. Jon Seymour, MD, CEO of Peers, said, “This is a new phase in medical publishing where raw data collection is the editorial function and cutting-edge machine learning is the technology factor. We turned to the University of Michigan due to its impressive data science programs spanning multiple departments, as well as the specific experience of Dr. Denton and Dr. Wiens in medical applications. We’re confident this initiative will attract many new data contributors along the way.”

“The collaboration with Peers Health is exciting because it provides data that can help build a model that will reduce the time — from both a safety and productivity perspective — for people to return to work following sickness or injury,” Denton said. “Streaming data in from existing patients will allow our model to adapt and improve over time.”

Wiens added: “These data contain a particularly interesting training label: days away from work. We hypothesize that this will be a strong signal for the type, timing, and effectiveness of the treatments and therapies.”

The U-M partnership with Peers was established by MIDAS and the university’s Business Engagement Center (BEC).

“This partnership illustrates the power of combining data from the healthcare industry with the data science expertise of U-M faculty,” said Dr. Alfred Hero, professor of Engineering and co-director of MIDAS.

“It is energizing for the BEC to be part of these innovative collaborative relationships that create real impact in the world,” added BEC Director Amy Klinke.