On Jan. 6th, Poll Everywhere will launch a new course management feature to simplify how instructors use polling and to improve the process of synchronizing grade data with Blackboard's gradebook.

Using a dedicated icon within the Poll Everywhere interface, this feature allows faculty to create and manage polling activities for their courses. Specific Course Management functions include:

• Accessing Courses
• Creating Assignments
• Grade Synchronization
• Managing Multiple Assignments and Activities
• Viewing Grades

Image 1: Example of new Poll Everywhere Course Management page showing the dedicated course management icon

Starting Jan. 6th, presenters will see a course management "opt in" banner. This banner will allow presenters to try out the Course Management experience throughout the spring semester, with the option to return to the existing experience if needed.

Poll Everywhere Resources

For Poll Everywhere resources, please see our FAQ articles:

• Poll Everywhere for Instructors and Presenters
• Poll Everywhere for Students and Participants
• How do I manage Attendance with Poll Everywhere? (instructors)
• How do I check in for Attendance with Poll Everywhere? (students)

Connect with Instructional Technology

As always, if you have any questions about teaching, learning, and technology at UMBC, please consider the following options:

• Check our extensive FAQ collection 
• What's new in Ultra? | Known Issues 
• Open a ticket via RT
• Follow the Instructional Technology & DoIT myUMBC groups
• Request a consult with instructional technology staff
• PIVOT | Academic Continuity | Keep On Teaching | Student Technology Resources

Advancing machine learning methodology for applications in gerontology

Dr. Jaime Lynn Speiser, Wake Forest University School of Medicine

11-12pm EST Mon, Dec. 11, 2024 U. of Maryland, Baltimore & online

Prediction models aim to help medical providers, individuals, and caretakers make informed, data-driven decisions about the risk of developing poor health outcomes, such as fall injury or mobility limitation in older adults. Most models for outcomes in older adults use cross-sectional data, although leveraging repeated measurements of predictors and outcomes over time may result in higher prediction accuracy. This seminar talk will focus on a novel methodology for longitudinal, repeated measures prediction models called Binary Mixed Model(BiMM) forest. I will give an overview of longitudinal data analysis and introduce the BiMM forest method with variable selection. I will then apply a BiMM forest for predicting mobility limitation in older adults using the Health, Aging, and Body Composition dataset.

Dr. Speiser received her B.S. in mathematics at Elon University and M.S. in statistics at the Ohio State University. She completed her Ph.D. in biostatistics at the Medical University of South Carolina, where she was awarded the Distinguished Graduate Student Award at the top of her class. Dr. Speiser received the Lester R Curtin Award from the American Statistical Association and several conference travel awards. She serves on the executive committees of the American Statistical Association’s Statistical Learning and Data Science Section and Statistics and Data Science in Aging Interest Group. Dr. Speiser is currently funded by an NIH K25 award and is the Leadership Fellow for Wake Forest’s Pepper Center and Clinical and Translational Research Institute. Her paper comparing random forest variable selection methods is a top-cited paper in the journal Expert Systems with Applications with over 1000 citations.

Register here to get a Zoom link.