Based on an NSF-funded workshop, Drs. Teasley (School of Information) and Kelly discuss learning analytics goals and research priorities for the coming decade. They report on a research agenda that would strengthen the connection between learning analytics and recognition of learner competencies. This could have a transformative impact on the relationship between higher education and employment. The agenda will also generate significant new research questions leading to insights about learning and the data science techniques for analyzing learning.
A MIDAS Challenge Award team, led by Stuart Soroka, professor of Communication and Media & Political Science, works with CNN on a Sentiment Analysis of recalled news about the candidates for the U.S. 2020 election. The analysis shows Net Sentiment for all respondents, then each of the Democratic, Independent and Republican respondents.
Depictions of people in museum visual arts collections often reflect the histories of inclusion and exclusion. For example, museum collections in the 1920s likely reflect much less the lives of African Americans than those in the 1960s. U-M Museum of Art’s (UMMA) entire collection of ~24,000 pieces of artwork, spanning 150 years, has been digitized. A group of data scientists and artists will apply algorithms to recognize faces and to examine how art collections can (mis)represent humanity and how that representation changes with time.
The project team includes:
- Jing Liu, Michigan Institute for Data Science (MIDAS)
- Kerby Shedden, MIDAS, Consulting for Statistics, Computing and Analytics Research and Dept. Statistics
- David Choberka and John Turner (UMMA)
- Sophia Brueckner (Stamps School of Art & Design).
This project, funded by the U-M Arts Initiative, is the result of months of discussion about how data science and arts can give each other a stronger voice to promote social justice.
The issue of fair and unbiased representation looms large as Big Data and Artificial Intelligence (AI) are impacting many aspects of our society, from political messaging to targeted marketing, from parole granting to hiring practices. With biased data, algorithms will reach superficially compelling but ultimately false conclusions and amplify the biases and the injustices that exist in the data. This project will illustrate this point by generating a collection of composite representations of human faces from the UMMA dataset, and presenting these representations through an art installation, interactive digital displays and narratives. Through this new way to communicate research that is understandable, meaningful, and impactful to the public, this project highlights the caveats that lie beneath Big Data and AI applications and inspire the public to participate in shaping our transition into a data-driven society.
Drs. Josh Pasek (LSA Communication and Media), Michael Traugott (LSA Political Science), Ceren Budak (School of Information) and Stuart Soroka (LSA Communication and Media), along with their Georgetown colleagues, have been collaborating with CNN to improve survey questions and carry out data analysis. This work is partly an extension of the Challenge Award project that MIDAS has funded. Read more: https://www.cnn.com/2020/08/16/politics/election-2020-polls-the-breakthrough-methodology/index.html
In collaboration with the US Environmental Protection Agency (EPA) National Vehicle and Fuel Emissions Laboratory (NVFEL), MIDAS will develop and administer a comprehensive educational program during the 2020-2021 academic year. The overall goal of the program is to support the EPA in its mission of improving air quality through applied data science, while providing experiential learning opportunities for students. In this program, senior-level undergraduate and graduate students will apply advanced data science techniques to real-world problems related to reducing the environmental impact of personal and freight mobility systems, design systems to reduce overall vehicle emissions using connected and autonomous vehicles, and engage in environmental policy research by analyzing the potential environmental benefits shared and/or automated vehicles.
EECS 498-009 will be on Reducing Emissions through Applied Data Science, the course syllabus is attached.