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UMD researchers will use natural language processing and machine learning to analyze the language used in the U.S. Congress in an attempt to understand how legislators decide how to vote, before expanding beyond politics. (Illustration by iStock) Up to now, computational models in political science often zeroed in on decisionmakers’ characteristics—where they’re from, their party, their age—but a new UMD project goes a step further, adding how lawmakers use language to analyze why they vote the way they do.

A new $2.5 million National Science Foundation grant will support University of Maryland-led research on how family-school collaborations that build on the strengths of multilingual families and teachers might improve math learning for multilingual students. Projections suggest that multilingual students will make up 40% of all K-12 students by 2030. Because of language and cultural mismatches and scarcity of school resources needed for success, many such students are in severe need of expanded learning opportunities, the researchers said.

University of Maryland College of Information Studies (UMD iSchool) researchers, in collaboration with Penn State researchers, have been awarded a four-year $1.2 million grant from the National Science Foundation (NSF) to fund their Teachable Activity Trackers for Older Adults project. The project team is developing innovative wearable technology tailored to track the movements and activities of older adults, age 60 and over.