Debunking a Data-Driven World
Learn to navigate our bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument.
Duration: January 2025 – December 2025
Status: Ongoing
Educational Level: Tertiary Level
Topic: Artificial Intelligence AI, Data Science for Education, Digital Skills & Literacy
Keywords: Problem Solving, Critical Thinking, Digital Literacy, Media Literacy
Initial Situation
The project addresses the proliferation of what it terms “bullshit”: communication crafted with an indifference to truth. In today’s world, this is a pervasive issue where political discourse is often untethered from facts, scientific communication can be driven by press releases rather than rigor, and even higher education can sometimes prioritize superficiality over deep analytical thought. Students, constantly connected through digital media, are particularly vulnerable. They are not only confronted with an overwhelming amount of data but are also targeted by misinformation and disinformation, especially on social media platforms. Without the necessary skills to critically evaluate sources, distinguish correlation from causation, identify statistical fallacies, and recognize the manipulation of cognitive biases, they are ill-equipped to navigate this complex information environment. This lack of data and media literacy poses a significant risk to their ability to make informed decisions and participate meaningfully in a data-driven society.
Objectives
The primary objective is to empower students by enhancing their data and media literacy. The project aims to develop and implement a course at two partner institutions, the Bern University of Applied Sciences (BFH) and the University of Bern (UB). This course will equip students with the practical skills needed to critically assess data-based claims. Key learning goals include identifying common statistical fallacies, understanding the crucial difference between correlation and causation, recognizing how human biases are exploited, and detecting fake news and other forms of misinformation.
Method
The project’s methodology centers on adapting and contextualizing established academic work. The course curriculum will be fundamentally based on the principles outlined in Carl T. Bergstrom and Jevin West’s book, “Calling Bullshit: The Art of Scepticism in a Data-Driven World.” This will be supplemented with required readings from Alex Edmans’ “May Contain Lies” and Philipp Hübl’s “Bullshit-Resistenz” to cover cognitive biases and philosophical aspects. Existing course materials will be specifically adapted for the Swiss context by incorporating local case studies and examples. The effectiveness and learning outcomes of the course will be systematically evaluated through a quantitative assessment conducted at both partner universities and across different fields of study.
Planned Translation
The project has a clear, phased plan for translating its results into educational practice. Initially, the developed course will be implemented and tested at the Bern University of Applied Sciences. Following this pilot phase, the course is planned to be offered at the University of Bern, specifically within the “Digital Transformation and Applied Data Science” minor program. The long-term goal is to make the course materials widely accessible; after the initial run and refinement, all materials will be published as an Open Educational Resource (OER), allowing other educators and institutions to freely use and adapt them. The primary partners from educational practice are the BFH and UB.
The project is expected to have a significant impact by directly equipping students with the essential skills to identify and challenge deceptive data and misinformation. This fosters a more informed, skeptical, and resilient student body capable of navigating the modern information landscape. The impact will be measured via the quantitative assessments of student learning outcomes before and after the course. By making the curriculum available as an Open Educational Resource, the project’s impact is designed to be scalable, extending beyond the partner institutions to empower a broader audience of students and educators, thereby contributing to a more data-literate society.
