Every day we are all bombarded with information from social media, the news, and our social networks. All too often, what we hear is taken out of context, biased, incorrect, or just plain fake. How can you know when an argument for or against doing something is valid? How can you know what to conclude from some data? In a world in which sometimes everything seems uncertain, how can you learn to distinguish between more reliable and less reliable information? And how can you tell when you are being manipulated?
The Data & Critical Thinking Learning Community will help you answer these questions. You will gain skills and practice to confront this avalanche of data and argumentation, recognise biases in others and reduce your own, and ultimately become a better decision-maker and advocate. We live in an increasingly complex world where sometimes a position or policy that seems obviously good or bad, or likely to produce a certain desired result, ends up having the opposite effect from what its supporters wanted — learn how to avoid the trap of unintended consequences!
Learning Community Activities
Coming up
coming soon
Past activities
Cross-EUTOPIA student debate on Data and Critical Thinking: Topic Societies Global Issues Climate, Veganism and AI (LC Data and Critical Thinking - second iteration of this event) March 2nd 2022
Within the context of the “Data and Critical Thinking” learning unit, the LC proposed three climate-change related topics to foster students’ debating skills: “To what extent is climate change driven by human activity?”, “To what extent does going vegan help reduce our carbon footprint?”, “What is the potential of the development or artificial intelligence in terms of carbon footprint reduction?”. Students across the partner institutions had a chance to debate online. The first round was triggered by the question “To what extent is climate change driven by human activity?”, where a team from the VUB had to defend the point that human activity has a big impact on climate change against a team from UoW. The second round tackled the question “To what extent does going vegan help reduce our carbon footprint?” with two cross-university (mixed teams CY -VUB -Warwick). During debates (45 min each), teams of students voiced their arguments using supportive data (figures, graphs, charts...), taken from various empirical studies to support and argue their claims. At the end of each debate, tailored feedback was provided by Matthieu Cisel (the co-lead at CY) to students regarding the quality of proposed arguments, supporting data, and strategies that could have been taken to refute opposing arguments.
More information about the callhere, and short summary of the event here.
Cross-EUTOPIA student debate on Data and Critical Thinking – When? March 3rd 2021 – Where? online – more information here.
Cross-EUTOPIA participation in CY’s ‘Data and Critical Thinking’ MOOC – When? Autumn 2021 – Where? online - more information here.
How to get involved?
(Students and educators)
Contact the Learning Community lead: Matthieu Cisel (matthieu.cisel@cyu.fr) or Tomy Quenet (tomy.quenet@cyu.fr).
Learning Community Members
Click on names below to see picture and short biography
Valerie Nachef Valérie Nachef has a PhD and an HDR. Her research domain is cryptography. She is the Director of the International Bachelor of Data Science by Design of CY Cergy Paris Université.
Matthieu Cisel Dr. Matthieu Cisel has a PhD in Education Data Mining. He teaches Statistics and Argumentation at CY Cergy Paris Université, and notably the Data and Critical Thinking Learning Unit.
Partner: Louise McNally (UPF). Email: louise.mcnally@upf.edu
Louise McNally Louise McNally is a professor of Linguistics at Universitat Pompeu Fabra. Her research and teaching focus on natural language meaning.
Partner: Jan De Beule (VUB). Email: Jan.De.Beule@vub.be
Jan De Beule Jan De Beule is an assistant professor at the VUB. His research and teaching duties are in discrete mathematics, with applications in data science.
Lyudmila Grigoryeva is an associate professor in the Department of Statistics at the University of Warwick. Her research interests are in the field of data-driven learning of dynamic processes, statistical learning theory, machine learning with dynamical systems, reservoir computing, time series analysis and financial econometrics. She is also interested in a wide range of computationally intensive applications including those appearing in Data Science.
Partner: Robert MacKay (UW). Email: R.S.MacKay@warwick.ac.uk
Robert MacKay Robert MacKay is a Professor of Mathematics and Director of Mathematical Interdisciplinary Research at Warwick. His main research interests are in the theory and application of dynamical systems.
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Lyudmila Grigoryeva is an associate professor in the Department of Statistics at the University of Warwick. Her research interests are in the field of data-driven learning of dynamic processes, statistical learning theory, machine learning with dynamical systems, reservoir computing, time series analysis and financial econometrics. She is also interested in a wide range of computationally intensive applications including those appearing in Data Science.
