4CAST19 audience

On the Thursday before spring classes begin, the 4CAST (Campus Academic Strategies & Technologies) conference provides University of Iowa faculty and lecturers an opportunity to share and explore new, effective teaching technologies.

“I always enjoy kicking off the spring semester with this conference,” Stephen Cummings, clinical assistant professor in the School of Social Work, says. “That the UI hosts this right on campus, with no cost to attend, is truly remarkable to me. It encapsulates cutting-edge commentary on technology advancement.”

This year’s event focused on teaching, machine learning, and the academic panopticon, and Sudha Ram, professor of MIS, entrepreneurship and innovation at the University of Arizona, began the day with a keynote presentation, “Integration of Data Science and Social Science to Build a Smart Campus.”

Cummings says that Ram’s concept of building an “Amazon for education” “was challenging, but I agree that predicting a student’s needs, gaps in education, and areas of interest for further study could be greatly enhanced with the use of machine learning. In fact, institutions of higher education like ours would be smart to study and deploy some form of this.”

During afternoon breakout sessions, UI faculty and staff considered an array of topics, including descriptions of how big data is being collected and heavily utilized to facilitate decision-making in public administration and business operations and a UI course that introduces undergraduate students to the use of information and big data in the social sciences and humanities.

To end the day, Associate Professor of Philosophy Jovana Davidovic provided her perspective and facilitated a lively discussion about the impact of machine learning and artificial intelligence on higher education and teaching methods.

“While social work includes a contingency of practitioners and researches who are doing the work studying the impact technology has on practice,” Cummings says, “it’s still considered something of a niche area. Like many areas of research and practice, we need to be as aware and clear-eyed about the impact of automation and machine learning.”