Amber Settle (De Paul University )
The Chair of ACM SIGCSE

Title: Refocusing Academic Integrity to Enhance Learning

Abstract: Academic integrity in computing education is a source of confusion and disagreement, both on the part of students and faculty. Institutional academic integrity policies are often not appropriate for computing assessments, and polices designed by computing faculty tend to be reactive rather than proactive. This can produce an environment in which the focus of computing educators is on detection and punishment rather than on prevention, turning students and faculty into adversaries. Further complicating the matter is the gap between academic and industry policies and beliefs regarding collaboration and code reuse. In this talk I introduce a new approach to the development of academic integrity policies, one that aligns academic integrity with learning goals. Several sample policies for courses at various levels in the undergraduate curriculum are shown to demonstrate the flexibility and generalizability of the approach.

Bio: Amber Settle is an Associate Professor in the School of Computing at DePaul University and has been on the fulltime faculty since 1996. She earned a B.S. in mathematics and a B.A. in German from the University of Arizona, and a M.S. and Ph.D. in computer science from the University of Chicago. Dr. Settle’s research interests include computer science and information technology education and theoretical computer science. Dr. Settle was named as a Vincent DePaul Professor in 2004. She has served on the Advisory Board for the ACM Special Interest Group for Computer Science Education (SIGCSE) since 2010 and is currently the Chair of SIGCSE. Dr. Settle has also served on the program and/or conference committees for RESPECT 2016, SIGITE/RIIT 2013, 2014, and 2015, and ITiCSE 2013. She has been a Senior Member of the ACM since 2011.

Mark Guzdial (Georgia Institute of Technology)
ACM Fellow
The winner of ACM Karl V. Karlstrom Outstanding Educator Award

Title: Improving Success in Computer Science Education Using Lessons from Learning Sciences

Abstract: Learning computer science is difficult, as multiple international studies have demonstrated. We still understand too little about the cognitive difficulties of learning programming, but we do know that we can improve success by drawing on lessons from across learning sciences. In this talk, I will describe three examples, where we improve success in learning computer science through application of lessons and models from the learning sciences. We help non-technical students learn programming by using models of student motivation. We increase the value of on-topic discussions in a collaborative forum by drawing on lessons from anchored instruction. We have improved learning and transfer of knowledge about programming by using subgoal labeling to promote self-explanations.

Bio: Mark Guzdial is a Professor in the School of Interactive Computing in the College of Computing at Georgia Institute of Technology. He studies how people come to understand computing and how to make that more effective. He leads the CSLearning4U project to create ebooks to help high school teachers learn CS. He is one of the leaders on the NSF alliance “Expanding Computing Education Pathways" which helps US states improve and broaden their computing education. He invented Media Computation which uses media as a context for learning computing. 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.

Dan Garcia (University of California, Berkeley )

Title: Bringing “Trustworthy Network Big Data” Ideas to High School Students

Abstract: In an effort to broaden participation in computing to traditionally underrepresented students in the United States, a new course was developed by the College Board called Advanced Placement Computer Science Principles (AP CSP). This is a high school course modeled after introductory "computing for non-majors" University courses. The curriculum framework for this groundbreaking class contains seven "big ideas": Creativity, Abstraction, Data, Abstraction, Programming, The Internet, and Global Impact. Our Beauty and Joy of Computing (BJC) course is one of the endorsed curricula for AP CSP, and we have offered professional development to over two hundred high school teachers over the past six years. Using the blocks-based visual programming language "Snap!" (based on Scratch), we have brought the powerful ideas behind trustworthy network big data to more than ten thousand learners.

Bio: Dan Garcia received dual B.S. degrees in Computer Science and Electrical Engineering from MIT in 1990; and an M.S. and Ph.D. in Computer Science from UC Berkeley in 1995 and 2000, respectively. He joined the CS faculty at UC Berkeley in 2000, won the departmental Diane S. McEntyre Award for Excellence in Teaching in 2002, the departmental Information Technology Faculty Award for Excellence in Undergraduate Teaching in 2004 and was chosen as a UC Berkeley "Unsung Hero" in 2005. He is active in SIGCSE, is recipient of the ACM Distinguished Educator Award, is a member of the ACM Education Board, and is the faculty co-advisor for BFOIT, a wonderful Berkeley outreach effort.