The Teaching Computational Thinking Workshop took place in EPFL 21st March 2018
Shriram Krishnamurthi: Curriculum Design as an Engineering Problem (video)
Shriram Krishnamurthi is professor of computer science at Brown University. He is a member of the core development group for the Racket programming language and is responsible for the creation of software packages including the Debugger and the FrTime package. He is the inaugural winner of the Robin Milner Young Researcher Award (2012), co-author of the best-selling book How to Design Programs (MIT Press) and is centrally involved in the computer science outreach programme Bootstrap.
He has argued that computer science education is the hardest problem he has worked on because “it requires substantial work on both technical and human-factors fronts; the audience is often unsophisticated and vulnerable; and if you screw up, you can do real damage to not only individuals but also the field and society.”
Mark Guzdial, Improving Computing Education with Learning Sciences: Methods for Teaching Computing Across Disciplines (video)
Mark Guzdial is a professor in the College of Computing at Georgia Institute of Technology. He is the inventor of the Media Computation approach to learning introductory computing, which uses contextualized computing education to attract and retain students. He was vice-chair of the ACM Education Board, still serves on the ACM Education Council, and his blog on Computing Education is widely used.
He has won numerous computer science education awards including the ACM Karl V. Karlstrom Outstanding Educator Award (with Barbara Ericson), the IEEE Computer Society Undergraduate Teaching Award and in 2014 he was named an ACM Distinguished Educator and a Fellow of the ACM.
Computational thinking is increasingly being seen by educators, policymakers and researchers as at the core of all modern science, technology, engineering, and mathematics (STEM) disciplines. At its most fundamental, computational thinking’s essence is thinking like a computational scientist when confronted with a problem from any discipline.
EPFL has embarked on a major curriculum reform which will see computational thinking introduced as a foundational course across science and engineering disciplines, as well as the development of disciplinary-specific computational thinking content in courses across different programmes. But questions remain about how computational thinking can and should be integrated into scientific and engineering programmes:
- What does “computational thinking” look like in practice, either as a stand-alone course or as integrated into other disciplines? What are the links and differences between learning programming, computer science and computational thinking across engineering and science disciplines?
- There is clear evidence that social and active-learning approaches have a more positive impact on student learning than traditional approaches both in STEM disciplines and more widely. What are the implications of this for initiatives in teaching and learning computational thinking?
- What would “success” look like? How should any new initiative be evaluated and against what criteria?
These themes were addressed in the “Teaching Computational Thinking” workshop, March 21st 2018, at EPFL.
The event was organised by the EPFL Centre for Learning Sciences (LEARN) in conjunction with the Teaching Support Centre, and with the assistance of EPFL Application-Centered Computational Engineering Science (ACCES).
Images courtesy of Laboratory for Multiscale Mechanics Modelling and the Computational Molecular Design Lab