Computing Education at ITU

Title: Getting Students to Think While Learning to Program in the Age of Generative AI

Abstract: Instructors of programming classes are wrestling with the impact of generative AI. Allowing students to use generative AI can widen the performance gap between strong and weak students. Weak students are more likely to have generative AI just do their work for them, without any cognitive effort on their part. Active, social, and/or creative assignments can encourage students to still think in the age of generative AI. Dr. Ericson has been exploring free and interactive ebooks, mixed-up code (Parsons) problems, Peer Instruction, Process Oriented Guided Inquiry Learning (POGIL), and open-ended and creative projects.

Bio: Dr. Ericson got her PhD in Human Centered Computing in 2018 from Georgia Tech and is now an Associate Professor at the University of Michigan. She applies research results from educational psychology to help students learn to program. She creates free and interactive ebooks with new types of practice problems including some that leverage generative AI. Dr. Ericson is part of the Generative AI in CS Education Consortium and helped create materials for free CS1 and CS2 courses that leverage AI. She won the 2022 ACM SIGCSE Award for Outstanding Contributions to Computer Science Education. She is also a Distinguished Member of the ACM.

Title: Changing Computing To Make It “For All”

Abstract: The term “computer science” was invented as something that should be taught to everyone in order to facilitate learning other subjects and to reduce the danger of having this powerful new technology controlled by only a few. Computing education has not become the democratizing force imagined in the 1960’s. Today, computer science has a narrow definition, and only a privileged few understand and create a critical part of our world. In this talk, I review the history of “computer science” and its earlier purpose, consider some barriers to reaching universal computational literacy, then describe and demonstrate new approaches for learning and teaching computing for everyone.

Bio: Mark Guzdial is a Professor in Computer Science & Engineering and Director of the Program in Computing for the Arts and Sciences at the University of Michigan. He studies how people come to understand computing and how to make that more effective. He was one of the founders of the International Computing Education Research conference. With his wife and colleague, Barbara Ericson, he received the 2010 ACM Karl V. Karlstrom Outstanding Educator award. He is an ACM Distinguished Educator and a Fellow of the ACM. He has just completed the second edition of his book, Learner-Centered Design of Computing Education: Research on Computing for Everyone.

Title: Exploring Programming in Higher Education: Understanding Course Framings, Institutional Positionality, and Educator Values

Abstract: The thesis is an exploration of how programming knowledge is selected, framed, and institutionalized in higher education across disciplines. Nicolajsen applies a model for the translation of knowledge into teachable content, explores how we frame “programming education,” how institutions differ on these framings, and what values as educators serve to mediate the transition from knowledge selected for teaching into classroom decisions and practices. In broader terms, it is an exploration of how teachers in various computing education programs across disciplines decide what to teach.

Bio: Sebastian Mateos Nicolajsen is a PhD student at the IT University of Copenhagen. His work is centered around how novices learn to program. In connection with his PhD studies, he was awarded the Danish National Eliteforsk (Elite Research) Travel grant and spent six months at University of California, Berkeley, CA, USA as well as three months at University of Cambridge, UK. His PhD studies have been supervised by Samantha Breslin Dawn (KU), Marisa Cohn (ITU), Louise Meier Carlsen (ITU), and Claus Brabrand (ITU).