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: Completed
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
Along with the omnipresence of media and connectivity, students not only require essential skills for navigating and scrutinizing today’s data-rich landscape of modern society, they are also susceptible to mis- and disinformation in social media. Seeking to empower students, this project proposes the development and implementation of a course at the Bern University of Applied Sciences (BFH) and the University of Bern (UniBe), aimed at enhancing data literacy skills among students, such as spotting statistical traps, the confusion of causation and correlation, the exploitation of human biases and fake news. By building on the principles outlined in “Calling Bullshit: The Art of Skepticism in a Data-Driven World” by Carl T. Bergstrom and Jevin D (2020). West a solid basis is already established and initial course material can be obtained. In addition, Alex Edmans’ book “May Contain lies: How Stories, Statistics, and Studies exploit our Biases” (2024) will serve as a second source and mandatory reading for the students. For the course we will adapt existing materials to the Swiss context, incorporating locally relevant case studies and examples. Through a quantitative assessment conducted across both institutions and different study programs, the research team will evaluate the effectiveness of the course in improving students’ ability to critically analyze and interpret data and information.
The developed materials will first be used at BFH. Curated parts of the materials will be used for practitioner outreach in invited talks and keynotes as well as sessions in adjacent CAS programs.
Objectives
The primary objective of this project is to develop and implement a course that significantly enhances data literacy skills among students. This course will be based on the principles outlined in “Calling Bullshit: The Art of Skepticism in a Data-Driven World” by Carl T. Bergstrom and Jevin D. West, and Alex Edmans’ book “May Contain lies: How Stories, Statistics, and Studies exploit our Biases”. Recognizing the need for a localized approach, the existing materials and illustrations will be adapted to the Swiss context, incorporating relevant local examples and case studies that resonate more effectively with students at BFH, UniBe and other participating institutions. Both books serve as literature background for the course and, depending on student prior knowledge in statistics and math, materials need to be flexible to accommodate a broad range of recipients. To achieve this, we will undertake a systematic process of material adaptation and course development. This involves a thorough review of the existing curriculum and the identification of content that can be directly applied or modified to suit the Swiss academic and cultural environment. Collaboration with local experts will be essential in developing case studies that reflect the specific data challenges and institutional contexts of Switzerland.
A critical component of this project is the quantitative assessment of the course’s impact on students’ data literacy skills. By administering standardized data literacy tests before and after the course, we aim to measure the extent of skill improvement among participants. This pre- and post-course assessment will be conducted across multiple institutions, providing a robust dataset for evaluating the course’s effectiveness. Ultimately, this project seeks to foster a culture of critical thinking and informed decision-making among students. By equipping them with the skills to discern and challenge deceptive data practices, we aim to contribute to a more informed and sceptical public, better prepared to navigate the complexities of the modern information landscape. Through these objectives, we aspire to make a lasting impact on data literacy education and empower students to become vigilant and discerning consumers of information.
Method
The project will employ a quasi-experimental design with pre- and post-course assessments to measure changes in students’ data literacy skills. The course, which we use as the experimental condition will be developed by the project team and held at different institutions. The course will be developed through a series of systematic steps, beginning with the adaptation of existing materials from the “Calling Bullshit” course to incorporate Swiss-specific content and examples. This will ensure the relevance of the course to the local context. Additionally, collaboration with local experts will be integral in identifying and developing pertinent case studies that resonate with the target audience. Depending on the different course structure, resource availability, and complexities, materials will need to be adapted. The course will be first implemented at BFH, then potentially at the University of Bern and possibly at partnering institutions, broadening its reach and impact. To quantitatively measure improvements in data literacy, standardized tests will be administered to students both before and after the course.
Following the collection of assessment data, statistical methods will be utilized to analyze the results. Descriptive statistics will provide an overview of the general trends and average performance improvements. Inferential statistics, such as paired t-tests or ANOVA, will be applied to determine the significance of changes in data literacy skills between the pre-course and post-course assessments. This analysis will help in identifying the effectiveness of the course in enhancing students’ data literacy under different study programs and conditions. Furthermore, regression analysis may be conducted to explore potential factors influencing the observed changes, such as differences in course delivery methods, student backgrounds, or specific components of the curriculum. This comprehensive analysis will provide a robust evaluation of the course’s impact and identify key elements contributing to its success or areas needing improvement.
Results
Milestones
- Completed, modular course package
- Study setup
- Data analysis
Slide deck, syllabus, data literacy study structure, assessment tool, and questionnaire.
Implemented Translation
Courses are being implemented at the institutions of the project leads. We are currently also cooperating with additional educational institutions in an attempt to extend the project to provide teacher education institutions with a tested and adaptable course that directly addresses one of the most pressing educational needs of our time. Teachers trained in this program will be better equipped to critically engage with AI-driven media, identify manipulative data practices, and foster similar skills among their students. 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. 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.
