Paderborn Colloquium on Data Science and Artificial Intelligence in School

Please register here for the colloquium

Information on the colloquium

Data science, artificial intelligence, machine learning, data literacy, and statistical literacy concerning secondary education are currently discussed in the communities of scientists and educators in statistics, mathematics, computer science, social and natural sciences, and media education. Our colloquium intends to bring together these perspectives and communities to create an interdisciplinary community for scientific exchange.  

Since data science and artificial intelligence have become more and more relevant in industrial and economical automation processes, marketing processes, and monitoring in politics, both topics permeate nearly all areas of life. These influences raise questions about future possibilities for social participation, self-determination, and self-realization in the professional and private sector, resulting in the need for educational processes that address these issues in school. For the teaching of mathematics and computer science completely new challenges have emerged, as well as for the subjects of the socio-scientific field and cross-curricular media education. 

In our colloquium, we want to take up these issues and discuss state of the art and future trends of education in data science and artificial intelligence that can inspire ideas for teaching data science in secondary schools. We also want to discuss fundamental ideas of data science as they are conceptualized by experts in this field since a broad perspective of data science as a scientific discipline is needed to inform curriculum development. Contributions to the colloquium will also present practice-oriented research as well as research on teachers’ professional development. 

ProDaBi (Project Data Science and Big Data at School) develops research-based teaching material and professional development courses for teaching data science and artificial intelligence for grades 5 to 12. It was initiated and is funded by the Deutsche Telekom Stiftung since 2018. 


The colloquium is open and free for everyone and will be held via Zoom. To register, please fill out the form, which you can access via the link below.
After the registration, you’ll be emailed the information for the sessions including the Zoom-Access-Data (which is the same for all three sessions). If you have any questions, please do not hesitate to contact us at the following mail address:
Please distribute the information to interested colleagues so that they can also register.

Please register here for the colloquium

Upcoming Sessions

  • 29th November 2023, 4 p.m. – 6.30 p.m. (CET, UTC+1):
    • Henning Wachsmuth (Germany) and Travis Weiland (USA)
  • 24th January 2024, 4 p.m. – 6.30 p.m. (CET, UTC+1):
    • Vincent Geiger (Australia) and Devin W. Silvia (USA)

Each presentation will be followed by an intensive discussion aiming at community building. 

Information about presentations and speakers

Session #12, Part 1: November 29, 2023, 4 p.m. – (CET, UTC+1)

NLP Research in the Age of Large Language Models – Henning Wachsmuth (Germany)

Abstract: (Click to view the full abstract)

Natural language processing (NLP) has recently got into the center of public attention to AI, due to the impressive capabilities of large language models (LLM) such as the one underlying ChatGPT.[]

LLMs cannot only generate text that is barely distinguishable from human-written anymore, but the most recent LLMs can even tackle problems successfully that they have never seen before. In this talk, I start from the general functionalities of LLMs, before I presented insights from selected research of my group involving LLMs to reconstruct, optimize, and create natural language text for specific NLP tasks. On this basis, I look at the recent breakthroughs that caused the success of ChatGPT-like technologies and the paradigm shift that comes with it.

Bio Henning Wachsmuth (Click to view the full bio)

Henning Wachsmuth leads the Natural Language Processing Group at the Institute of Artificial Intelligence of Leibniz University Hannover since 2022.[]

After receiving his PhD from Paderborn University in 2015, he worked as a PostDoc at Bauhaus-Universität Weimar, before he returned to Paderborn as a junior professor from 2018 to 2022. His group studies how intentions and views of people are reflected in language and how machines can understand and imitate this with large language models. Henning’s main research interests include computational argumentation, the mitigation of social bias and media bias, and the construction of human-like explanations for educational and explainable NLP.

Session #12, Part 2: November 29, 2023, 5.30 p.m. – (CET, UTC+1)

Reading and Writing the World with Data – Travis Weiland (USA)

Abstract: (Click to view the full abstract)

The interplay between data and society has grown more and more powerful with the rise of our current information age. Data is constantly being collected in unimaginable quantities and used to make decisions and shape the very reality we experience.[]

Some highlight the amazing potential of data’s power to create a better world and others see portents of how such use of data will only exacerbate inequities and lead to the very downfall of democracy. Regardless of what stance you take, being data literate is crucial, but it is unclear what kind of data literacy is important and for whom. In this talk I will lay the groundwork for taking a critical literacy perspective on data literacy to focus on reading and writing the word and the world with data. I will also spend time connecting such work to practice by discussing an ongoing research to practice partnership with teachers and share a framework for thinking about practices of reading the word and the world with data visualizations.

Bio Travis Weiland (Click to view the full bio)

Travis Weiland’s career in education began with teaching high school mathematics in the mountains of Western North Carolina.[]

While working on a master’s degree, I realized not only how little I understood statistics, but that I had been doing a terrible job of teaching those concepts to students for years. It was this realization that motivated me to pursue a PhD in Mathematics Education to investigate the question of how can we improve the statistical education of students in schools? My work stems from two main principles: that education is transformative, and the goal of public education is democratic equality. More specifically, my research is focused on issues of equity, including the consideration of issues of access, achievement, identity and power, at their intersection with statistics and data science education.

Session #13, Part 1: January 24, 2024, 4 p.m. – (CET, UTC+1)

Evaluating media claims about sustainability through the use of large data sets Vince Geiger (Australia)

Abstract: (Click to view the full abstract)

In an era marked by disruption, both global and digital, the application of mathematics and statistics is crucial for understanding, making predictions about, and addressing challenges associated with public health, the environment, and national and global social cohesion. []

The COVID-19 pandemic highlighted how mathematics and statistics can be used to present sometimes contradictory and misleading claims by various groups – amplifying the need for citizens be capable of critically evaluating claims made by both expert and non-expert commentators, and the decisions of government. This means that informed and responsible citizens must be capable of understanding the role of mathematics and statistics in underpinning evidence and have the capacity to employ evidentiary practices in forming relevant judgements. In this presentation I report on the aims and approaches that frame an international collaborative project being conducted by Australian Catholic University and Wurzburg University, entitled Strengthening Teachers’ Instructional Capabilities with Big Data. The project is designing and implementing tasks that require middle years students to evaluate differing claims in the media about issues related to sustainability by making use of relevant publicly available large data sets. This includes students’ selection of databases, approaches to modelling data, and their decision-making processes. Participant teachers will engage with professional learning activities based on students’ approaches to resolving differences in media reports.

Bio Vince Geiger (Click to view the full bio)

Vince Geiger is a Professor of Mathematics Education within the Institute for Learning Sciences and Teacher Education (ILSTE), at the Australian Catholic University.[]

He is the Research Director for the STEM Education Program – an interdisciplinary research space focused on the enabling and transformative role of mathematics within the STEM disciplines. His research interests span across the areas of critical mathematical thinking, numeracy, mathematical modelling, the use of digital resources in mathematics teaching and learning, and the mathematical/statistical capabilities required for informed and responsible citizenship. This work is driven by awareness that the capacity to know and use mathematics confidently is important for an individual’s career prospects and their empowerment as informed citizens.

Session #13, Part 2: January 24, 2024, 5.30 p.m. – (CET, UTC+1)

A learner-centered approach to teaching computational modeling, data analysis, and programming Devin W. Silvia (USA)

Abstract: (Click to view the full abstract)

One of the core missions of Michigan State University’s Department of Computational Mathematics, Science and Engineering (CMSE) is to provide education in computational modeling and data science to MSU’s undergraduate and graduate students. []

In this presentation, I will describe our creation of CMSE 201, „Introduction to Computational Modeling and Data Analysis,“ which is intended to be a standalone course teaching undergraduate students (both STEM and non-STEM) core concepts in data analysis, data visualization, and computational modeling. I will discuss the rationale behind the „flipped classroom“ instructional model that we have been using and explain the course’s design principles and implementation. The concepts and skills students learn in this course can be used by other disciplines as the foundation for integrating computing across the curricula in undergraduate degree programs.

Bio Devin W. Silvia (Click to view the full bio)

Devin is the Director of Undergraduate Studies and a teaching specialist in the Department of Computational Mathematics, Science and Engineering at Michigan State University (MSU). []

He received his PhD in Astrophysical and Planetary Sciences from the University of Colorado where he worked as a computational astrophysicist running simulations aimed at understanding various facets of chemical evolution in the universe. He then worked as a National Science Foundation Astronomy and Astrophysics postdoctoral fellow at MSU before joining the ranks of the CMSE faculty in 2017. Most recently, Devin has been pursuing computing education research in an effort to better understand how students learn to do computational and data science and help inform pedagogical approaches in these disciplines. As part of this effort, Devin co-leads the Computing Education Research Lab at MSU.