Previous Sessions

Session #01, Part 1: October 27, 2021, 4:00 p.m. (CEST, UTC + 2):

Data Detective Clubs in the Time of COVID-19
Jan Mokros and Bill Finzer (USA)

Abstract: (Click to view the full abstract)

The COVID-19 pandemic presents an opportunity to engage young people in exploring how data can be used to understand a public health crisis, make decisions, and save lives. In this session we will describe a multifaceted project involving an adventure story about COVID-19 that is connected to data challenges in which CODAP (Common Online Data Analysis Platform) is used to explore time series of pandemic data. This work takes place in out-of-school clubs around the US, comprising 20 hours over two to three months with students who are 10-14 years old.[]

We will focus on two aspects of this work: First, we’ll demonstrate and discuss the affordances of CODAP and accompanying datasets in understanding how a dynamic pandemic unfolds. For example, CODAP interacts well with NetLogo, which means students (and those attending our session) can set parameters for infectivity and run multiple simulations to see how many people get sick, how many recover, and how long the outbreak lasts. In addition, CODAP’s “Story Builder” feature enables youth to combine graphs, photos, and text to tell the story of what happens over time with COVID under different circumstances and in different places.

Second, we’ll discuss the challenges and opportunities of working with real-time, highly relevant data that transcend the boundaries of school curricula. Most students do not study epidemiology in secondary school, though the subject area is an ideal vehicle for learning about data. Students’ work with data from pandemics also integrates social science, public policy, and the science of viruses.

The session will conclude with a discussion of the social-emotional aspects of using sensitive data. COVID data, like most data that truly matter, elicit a range of social and emotional issues, and we believe it is part of our role as data science educators to address these concerns.


Bio Jan Mokros (Click to view the full bio)

Dr. Jan Mokros is a developmental psychologist who is currently directing National Science Foundation-funded projects focused on how youth learn about data in out-of-school clubs. []


Jan’s work with data science education introduces youth to topics including Lyme disease, teens’ use of time, sports injuries, and COVID-19. The COVID project centers on using a combination of CODAP, data activities, and a young adult adventure book, “The Case of the COVID Crisis”, by Pendred Noyce, to explore infectious disease epidemics. Afterschool programs around the US are using this program with youth who are underserved with respect to STEM. Jan is a Senior Research Scientist at Science Education Solutions. In prior positions, she has designed curriculum and conducted research at TERC and at the Maine Mathematics and Science Alliance. She has authored three books, including one for museum educators on incorporating math into exhibits and programs, and one for parents on exploring math in everyday life. She has been involved as a writer and researcher for the math curriculum Investigations in Number, Data and Space.

Bio Bill Finzer (Click to view the full bio)

Bill Finzer’s work has long centered on getting students using data in every subject they study. []


He led the Fathom Dynamic Data Software development team at KCP Technologies before joining the Concord Consortium in 2014 where he leads development of the Common Online Data Analysis Platform (CODAP). He has been a classroom teacher, curriculum developer, teacher professional development course designer and leader, and educational software developer. Bill works with staff of many projects both inside and outside Concord Consortium to help them make use of CODAP. He loves nothing better than fixing bugs and implementing new features.

Session #01, Part 2:  October 27, 2021, 5:10 p.m. (CEST, UTC + 2):

Teaching machine learning in school: Some emerging research trajectories
Matti Tedre and Henriikka Vartiainen (Finland)

Abstract: (Click to view the full abstract)

A major technological shift has recently triggered discussions about the need to amend computing education at all education levels. Traditional, rule-based automation has been joined by machine learning (ML), which, when provided with enough computing power and data, has enabled new classes of jobs to be automated, and thus expedited automation in the society, workplace, and in people’s everyday lives.[]

Although ML has become an integral part of our lives, communities, and societies, it has gained very little attention in K–12 (school) computing education which mainly focuses on rule-based programming and computational thinking. This talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. It will situate that research in the broader context of computing education and describe what changes ML necessitates in the classroom. The talk will outline the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula.


Bio Matti Tedre (Click to view the full bio)

Dr. Matti Tedre is a professor of computer science, especially computing education and the philosophy of computer science, at the University of Eastern Finland.[]

His 2019 book “Computational Thinking” (The MIT Press, with P.J. Denning) presented a rich picture of computing’s disciplinary ways of thinking and practicing, and his 2014 book “Science of Computing” (Taylor & Francis / CRC Press) portrayed the conceptual and technical history of computing as a discipline.

Bio Henriikka Vartiainen (Click to view the full bio)

Dr. Henriikka Vartiainen[]

is a senior researcher at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. Currently, her research focuses especially on learning Machine Learning through co-design as well as on the ways to support children’s data agency.

Session #02, Part 1: November 24, 2021, 4:00 p.m. (CET, UTC + 1):

Beyond Bias. Locating questions of injustice in Data Science and Artificial Intelligence
Tobias Matzner (Germany)

Abstract: (Click to view the full abstract)

Data Science and Artificial Intelligence have repeatedly come under scrutiny because of injustices they produce. AI based services only work for a certain part of the populace, scoring and ranking systems are skewed towards certain groups, errors concentrate on specific persons etc.[]

Thus, when teaching Data Science and Artificial Intelligence, such issues should be part of the curriculum. Most often in research and in educational concepts, injustice is discussed as bias outputs of information processing. In social studies of technology, recently some people have begun to challenge this idea of equating injustice with bias. The talk will shortly present why this is the case – and how this challenge from current research opens up new ways of discussing the social impact of Data Science and Artificial Intelligence in schools.

Bio Tobias Matzner (Click to view the full bio)

Dr. Tobias Matzner is professor for “Media, Algorithms, and Society” at Paderborn University in Germany. His work combines theories of (digital) media and technologies with approaches from political philosophy, cultural studies, and social theory.[]


He has studied philosophy and computer science in Karlsruhe, Rome, and Berlin. Prior to his appointment in Paderborn, he has been working at the International Centre for Ethics in the Sciences and Humanities at the University of Tübingen as well as the New School for Social Research in New York.

Session #02, Part 2: November 24, 2021, 5:10 p.m. (CET, UTC + 1):

Bringing together statistics and computer science education: Machine learning by decision trees grounded in students’ data exploration experiences 
Rolf Biehler and Yannik Fleischer (Germany) 

Abstract: (Click to view the full abstract)

Trees can be used to visualize decision rules for classifications, and students may have encountered trees for different purposes in the mathematics or computer science classroom already. Everyday decisions can be supported by using simple decision trees. A new idea for students is to use trees for predictive modeling (classification) in multivariate data sets. []

The human construction of trees has to be based on insights into the data and its context. Based on such experiences, algorithms for the automatic creation of trees can be developed and critically evaluated. Essential elements of predictive modeling such as the distinction between training and test data, overfitting, consequences of bias in the data (random or systematic sources), different evaluation criteria based on the confusion matrix can be discussed.

We have developed material and educational guidelines for their use for several educational levels (grade 5/6, 9/10, and 11/12). We use different computational tools: Codap (codap.concord.org) as a web-based, easy-to-use data exploration tool that has a plug-in for creating decision trees used for a start. Various types of Jupyter Notebooks, based on Python, require different levels of coding skills from the students. As a rule, we start with unplugged activities at all levels. For instance, we have developed a decision game with data cards for young kids. In their basic version, the Jupyter Notebooks appear menu-driven. In their advanced version, students get worked examples for computational essays that they can adapt for their own data and predictive modeling problems, including the adaptation and enhancement of code. All notebooks use libraries for data exploration and decision tree machine learning that we have adapted for educational purposes from professional sources.

Students encounter various multivariate data sets. These include data on nutrition values of food, data on (social) media use of adolescents, and data from medicine on heart diseases. In addition, parking lot occupancy data from their town were used, where predictive modeling is applied to help reducing parking search traffic and related emissions.

We will present some of our materials and the first results from studies in the classroom where we used the material.


Bio Rolf Biehler (Click to view the full bio)

Dr. Rolf Biehler is professor for didactics of mathematics at Paderborn University. His research interests include probability, statistics and data science education, university mathematics education and the professional development of mathematics teachers. []

He was a co-founder and co-director of the Centre for Research in University Mathematics Education. He is engaged in the International Association of Statistics Education (IASE) and has worked as an editor or editorial board member in several international journals and book series for mathematics education. He is currently co-directing the Project Data Science and Big Data at School.

Bio Yannik Fleischer (Click to view the full bio)

Yannik Fleischer Yannik Fleischer is a PhD student in mathematics education research at Paderborn University, Germany. []

His main research interest is developing a conception for teaching machine learning methods in school with a focus on decision trees, and to evaluate this by developing and examining teaching materials in practice. Since 2019, he has been teaching year-long project courses on data science in upper secondary and developing, implementing, and evaluating teaching modules for different levels in secondary school, mainly about machine learning with decision trees.

Session #03, Part 1: January 12, 2022, 4:00 p.m. (CET, UTC + 1)

Learning data science through civic engagement with open data
Graham Dove (USA)

Abstract: (Click to view the full abstract)

In this talk I will discuss work undertaken for the project “Learning Data Science Through Civic Engagement With Open Data”. This project, which is funded through the National Science Foundation’s Advancing Informal STEM Leaning (AISL) program, studies the informal data science learning that takes place within workshops, and other events and activities, that have been developed to support community engagement with civic open data in New York City (NYC). []

NYC is a leader in Open Data initiatives, which are centered around the NYC Open Data portal, and which have become enshrined in the City Charter. It also has a large and highly diverse population, including many traditionally underserved communities. As government service provision becomes increasingly digital, large amounts of data are generated and subsequently used to assess need, drive service delivery decisions, and evaluate effectiveness. Services producing these data include education, transport, and 311 service requests (a non-emergency municipal service available in many cities for reporting problems such as noise or public safety concerns), and the data they produce can be probed to ask many important questions such as: “How do City agencies respond to noise in my neighborhood?”, “How do waste and recycling services in my neighborhood compare with others?”, and “Are there more construction permits issued for my neighborhood than similar areas?”. To better understand how diverse communities might access and analyze these data to answer questions, share narratives about issues of concern, and respond to data driven policy and resource allocation, we are studying programs offered by the Mayor’s Office of Data Analytics (MODA) and BetaNYC. MODA is the NYC agency with overall responsibility for the City’s Open Data programs, while BetaNYC is a leading nonprofit organization working to improve the lives of NYC residents through civic design, technology, and engagement with government open data. We study the ecosystem that has emerged around the programs these organizations offer as a possible model for identifying, validating, and evaluating best practices; including questions of participation and potential barriers to entry.

Bio Graham Dove (Click to view the full bio)

Dr. Graham Dove is a human-computer interaction researcher, with experience in participatory approaches to design and citizen science. []


Based in NYU Tandon’s Dept. of Technology Management and Innovation (TMI), and the Center for Urban Science and Progress (CUSP), Graham investigates ways that people who are not experts in data science can use quantitative data and artificial intelligence to inform decision making, advocacy, and creativity in design. Current projects include investigating the informal learning that takes place around NYC Open Data, designing data rich interfaces to support future healthcare work practices. SONYC (Sounds of New York City), which investigates approaches to monitoring and mitigating noise pollution. He has previously worked in Denmark and the UK.

Session #03, Part 2: January 12, 2022, 5:10 p.m. (CET, UTC + 1)

Why should students take a data science course?
Rob Gould (USA)

Abstract: (Click to view the full abstract)

The Mobilize Intro to Data Science (IDS, www.introdatascience.org) course was first offered in 2017 and was, at that time, the only data science course designed for secondary students in the U.S., and possibly anywhere. IDS was developed through a partnership between the Los Angeles Unified School District (the second-larger district in the U.S.), the UCLA Department of Computer Science, the UCLA Graduate School of Education and Information Sciences, and the UCLA Department of Statistics. []

The IDS curriculum has several special features. First, it relies on a data collection paradigm, Participatory Sensing (Burke, et al 2006), in which students use mobile devices to collect multivariate data together. Second, the curriculum teaches students to use R, via the interface Rstudio and the mobilizR package, to organize, prepare, and analyze data. Finally, it relies on a student-centered, activity-based pedagogy. The primary goal of IDS is to develop in students the ability to synthesize statistical and computational thinking (DeVeaux, et al. 2017; Gould 2021) in order to work, and live, ethically and productively in a data-driven world.

Since 2017, interest in preK-12 data science and “data literacy” has blossomed in the U.S (and elsewhere), and varying visions and purposes for data science education have emerged. Data science education is many things to many people; it can be seen as an approach to developing “data literacy”, improving programming skills, increasing “college readiness”, improving equity in mathematics (Burdman,2015), and developing students’ appreciation for and skill in for mathematics. While we agree that many of these purposes are welcome consequences of a well-designed data science course, in our vision of data science education these are secondary to the primary goal, which is to teach students to analyze complex, multivariate data and to develop what has been called “data acumen”. Many in the U.S. see secondary-level Data Science as a sub-discipline of mathematics (Levitt 2019), which naturally affects the scope and intent of a course.

In this presentation, we’ll describe the IDS vision of data science and IDS’s role in developing data literacy for secondary students. We’ll also discuss some of the challenges that remain in implementing this vision, including teacher preparation and university acceptance.

References:


Bio Rob Gould (Click to view the full bio)

Dr. Rob Gould is a teaching professor and vice-chair of undergraduate studies in the Department of Statistics at UCLA, active in statistics and data science education since 1994. He is the founder of DataFest, a 48-hour undergraduate data analysis competition sponsored by the American Statistical Association and held at over 40 sites around the world.  []


He is co-author with Colleen Ryan and Rebecca Wong of an introductory statistics book. He was the lead Principal Investigator of the NSF-funded Mobilize project, which produced the Introduction to Data Science curriculum, the first high school data science curriculum in the U.S. Rob was elected Fellow of the American Statistical Association in 2012; in 2019 was awarded the CAUSE Lifetime Achievement Award for Statistics Education and the ASA Waller Distinguished Teaching Career Award. He received his B.S. from Harvey Mudd College in 1987 and PhD in Mathematics (concentration on Statistics) from the University of California, San Diego, in 1994. He is Vice-president of International Association of Statistics Education (IASE).

Session #04, Part 1: April 27, 2022, 4:00 p.m. (CEST, UTC + 2):

Why Computing Education, and Especially CT, Needs a Broader Perspective!
Arnold Pears (Sweden)

Abstract:

Computing education has focussed on introductory programming, nearly to the exclusion of all other CS content, a mistake that plagues the discipline. Computational Thinking (CT) runs the risk of making a parallel error by focussing on aspects of computation unique to the imperative programming paradigm and sequential execution. An inordinate focus on loops, sequences and alternation runs the risk of impoverishing the computing discipline, and by ignoring vital areas such as concurrency and data parallelism, runs the risk of educating future generations in an obsolete programming tradition.


Bio Arnold Pears (Click to view the full bio)

Arnold Pears is Professor and Chair of the Department of Learning in Engineering Sciences at the KTH Royal Institute of Technology, Sweden. He also holds a professorship in Computer Science at Uppsala University, Sweden. Professor Pears received his BSc(Hons) in 1986 and PhD in 1994, both from La Trobe University, Melbourne, Australia.[]


In the late 1990’s, Together with colleagues Dr. Berglund and Prof. Daniels, Prof. Pears established the UpCERG research group in computing and engineering education research at Uppsala University. As foundation professor at KTH he has lead research in all areas of technical and engineering education since 2017. His recent work includes several articles on computing in schools. He has published over 100 articles in leading Computing and Engineering education journals and conferences. He has delivered a number of keynote addresses, and is well known as a computing and engineering education researcher through his professional activities in the ACM, and IEEE.

Contributions to the academic and professional community include his roles as a member of the Board of Governors of the IEEE Computer Society 2012-2014, where he is active coordinating education conferences; serving on the steering committee of the Frontiers in Education Conference and as Chair of the Special Technical Community (STC) for Education. In addition, he is a Director of CeTUSS (The Swedish National Center for Pedagogical Development of Technology Education in a Societal and Student Oriented Context, www.cetuss.se) and the IEEE Education Society Nordic Chapter. He also serves as a reviewer for a number of major journals and conferences, including the Computer Science Education Journal (Taylor and Francis), the ACM SIGCSE and ITiCSE and Koli Calling International Computer Science Education conferences.

Recent publications include „Does Quality Assurance Enhance the Quality of Computing Education?“, in the Proceedings of the 12th Australasian Computer Science Education Conference, 2010, and models for research driven education in Computing, „Conveying Conceptions of Quality through Instruction“, in the 7th International Conference on the Quality of Information and Communications Technology, 2010.

Prior appointments include, lecturer and senior lecturer at La Trobe University between 1991 and 1998. Since 1999 senior lecturer at Uppsala University, Sweden where he was awarded the Uppsala University Pedagogy Prize in 2008, and promoted to Associate Professor of Computing Education Research in May 2011, and Professor in 2017. Roles at Uppsala University include appointment to the University Academic Senate, Programme Director for the IT Engineering programme, member of the selection committee for the Uppsala University Pedgogy prize and as member of the educational advisory board of the Faculty of Technology and Natural Sciences.

Session #04, Part 2:  April 27, 2022, 5:10 p.m. (CEST, UTC + 2):

Education for a fast-changing world: Conceptions of Statistical Literacy and Data Science
Jim Ridgway (England)

Abstract:

The data landscape is in a continued state of flux. New sorts and sources of data emerge from new creators; new ways to interact with data are created; we ill-understand the ways new information shapes beliefs and actions. Here, I will map out some of the elements in the evidence ecosystem – producers and consumers, both well- and ill- intentioned. What do students need to learn, if they are to navigate this brave new world? We will explore the needs of future citizens in their roles as spectators, referees and players in this ecology, and consider how well these needs map onto conceptual frameworks describing statistical literacy, and data science.


Bio Jim Ridgway (Click to view the full bio)

Jim Ridgway is an emeritus professor at Durham University, with a background in cognitive psychology. Past work has included the creation of materials to develop mathematical thinking on undergraduate courses in the USA, creation of computer-based materials to identify students in poorly-supported communities who have a flare for STEM (subsequently used in 20+ countries), work with the House of Commons Library to provide (huge amounts of) data accessible to citizens via their phones (along with some gamification), design and delivery of the first OECD workshop for politicians and policy makers on evidence-informed decision making, EU-funded projects on girls and STEM; and ProCivicStat, an Erasmus-funded collaboration between 5 countries which has developed materials to engage students with issues such as poverty, migration, gender inequality and racism.[]

A current project entitled firing up the epistemological engine plans to use AI (and conventional methods) to challenge some current research practices and conclusions in science and medicine.
Latest book: Teaching Data Science and Statistics  (eds. MacGillivray, Gould, Ridgway Special Edition of Teaching Statistics (vol 43, Summer 2021)
Forthcoming: J. Ridgway (Ed.),  Statistics for Empowerment and Social Engagement: teaching civic statistics to develop informed citizens. Springer. 

Session #05, Part 1: May 18, 2022, 4:00 p.m. (CEST, UTC + 2):

Data Awareness: Be aware of the data!
Lukas Höper & Carsten Schulte (Germany) 

Abstract: (Click to view the full abstract)

In data and in digital literacy one core issue is to enable students to cope with the datafication of their everyday lives, and hence to become data literate. Based on this line of reasoning the debate then focusses on what skills to teach. We add to this discussion by trying a slightly different angle: to make students aware of the data flows they create and probably can influence when interacting with digital artefacts like social messengers, recommendation system of video streaming portals, or simply when using a mobile phone. []

In order to enable students to understand these processes of data collection and processing when using data-driven technologies, we developed the framework data awareness. The goal is to enable students to become aware of and understand the collection and processing of data about them during interaction with digital artefacts. It also aims to provide students with appropriate skills and adequate knowledge to apply this to their own daily lives, and to enable them to evaluate data-driven systems and their impact. This is intended to create the basis for them to be able to shape the data-driven world (in the sense of agency).

In this talk we will first introduce this framework data awareness. We will then present two exemplary teaching units for fostering data awareness. The first is about exploring the mobile phone system and location data traces of one user; the second is about a recommender system for movies and how it works (e.g., using the k-nearest-neighbour method).


Bio Lukas Höper (Click to view the full bio)

Lukas Höper is PhD student for computing education research at Paderborn University, Germany. []

The main research interest is to develop the concept data awareness for computing education and evaluate this within design-based research by developing and empirically examining teaching materials in practice. Since 2020, he has been working on data awareness in the ProDaBi project, in which curriculum ideas, teaching materials and teacher education approaches regarding data awareness, artificial intelligence and data science in schools are developed.

Bio Carsten Schulte (Click to view the full bio)

Carsten Schulte is professor for computing education research at Paderborn University, Germany. []

Work and research interests are: Philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.

Session #05, Part 2: May 18, 2022, 5:10 p.m. (CEST, UTC + 2):

Teaching Core Principles of Machine Learning with a Simple Machine Learning Algorithm: The Case of the KNN Algorithm 
Orit Hazzan & Koby Mike (Israel) 

Abstract: (Click to view the full abstract)

Data science is a new interdisciplinary science that focuses on extracting insights and value from data. Upon scanning introductory data science courses, one usually finds that they include several machine learning algorithms of different kinds.[]

In this talk, we propose that only one simple algorithm may be sufficient for such courses, illustrating our approach using the KNN algorithm. The main reason we propose the KNN algorithm is that it is simple to understand both from a mathematical perspective and from an algorithmic perspective. This approach is implemented in the basic level of the data science unit of the Israeli high school computer science curriculum. We highlight our approach from three perspectives: Computational, cognitive and pedagogical. We show that despite the simplicity of the KNN algorithm, it enables to expose novice data science learners to the main ideas of machine learning and to pose interesting questions that address its core concepts. We also discuss how such an approach may eliminate barriers, which new teachers may encounter, to both learning the topic and teaching it.  In the discussion, we invite the audience to suggest other algorithms that may serve as the sole algorithm taught in introductory data science courses.

Bio Orit Hazzan (Click to view the full bio)

Professor Orit Hazzan  is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education.[]


Within this framework she researches cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. She has published about 130 papers in professional refereed journals and conference proceedings, and seven books.
In 2006–2008 she served as the Technion’s Associate Dean of Undergraduate Studies. In 2007-2010 she chaired the High School Computer Science Curriculum Committee assigned by the Israeli Ministry of Education. In 2011-2015 Hazzan was the faculty Dean. From 2017 to 2019, Hazzan served the Technion Dean of Undergraduate Studies.

Additional details can be found on her personal homepage.

Bio Koby Mike (Click to view the full bio)

Koby Mike  is a Ph.D. student in the Technion’s Department of Education in Science and Technology under the supervision of Professor Orit Hazzan.[]


He holds a B.Sc. and a M.Sc. in electrical engineering. Koby’s doctoral research focuses on data science education. As part of his research, he teaches data science in high school and at Tel Aviv University. Prior to his doctoral studies, Koby has gained an extensive experience in the Israeli hi-tech industry.

Session #06, Part 1: June 22, 2022, 4:00 p.m. (CEST, UTC + 2)

My AI discriminates? How could this happen and who is to blame?
Marc Hauer (Germany)

Abstract: (Click to view the full abstract)

For some years now, artificial intelligence methods have been used in many areas of daily life. Many applications have been criticized for being discriminatory. There are several ways to deal with such cases: Training datasets can be improved to reduce discriminatory behavior, discriminatory model outputs can be modified post hoc, processes can be established to make discriminatory results usable. In any case, the preliminary assumption is that discrimination can be measured. []

The development process of an AI consists of several steps, collection and processing of data is only one of them. Errors can occur at all steps, which have an effect on later steps. This means control processes are needed at all steps and the transitions between them. In addition, responsibilities must be assigned at all these points so that it is clear who must react to errors and problems. At this point, the concept of the „Long Chain of Responsibilities“ is introduced, which helps to clarify these responsibilities.

In this talk, we will talk about how discrimination can enter AI, who is responsible for it, and how discrimination can be operationalized.

Bio Marc Hauer (Click to view the full bio)

Marc Hauer is a PhD candidate on the question of how to make software development processes and AI products accountable. []

Additionally, he works as media education consultant for the Landesmedienzentrum Baden-Württemberg in the education of students, parents and teachers on the topic of computer science and society and for TrustedAI GmbH in AI consulting for companies.


Session #06, Part 2: June 22, 2022, 5:10 p.m. (CEST, UTC + 2)

A Framework for Exploring the Purposes and Processes of Data Wrangling in Complex Self-Directed Analysis Tasks
Michelle Hoda Wilkerson (USA)

Abstract: (Click to view the full abstract)

The data science education community at large is advocating to include open-ended exploration of large datasets in curriculum and instruction. Introducing this level of flexibility, however, requires teachers and students to wrangle data — that is, to transform complex datasets so that they can be used to particular lines of inquiry. []

In this talk, I will draw empirical examples from two data science education projects that engaged teens and young adults in analyzing complex public datasets about socioscientific issues (e.g. public transit, genetics, environmental justice): Data Science Games and Writing Data Stories. Through these examples, I will present a framework for understanding the process of data wrangling as novice analysts determine
(a)  whether  a particular question can be explored using a given dataset;
(b)  what  transformation needs to be applied to the dataset in order to pursue that question, and
(c)  how  to execute that transformation using the tools at hand.

The framework and examples lend insight into how actions by learners that may initially seem inappropriate (such as rejecting a dataset or applying unexpected transformations) can be understood as sensible from the perspective of learners‘ goals and contexts. More generally, they highlight how the interplay of learners‘ investigative goals, the data context, and the available tools all shape the ways in which a complex data investigation unfolds. I will discuss how considering these elements of data exploration in interaction can lead to the more thoughtful development of educational data science tools, activities, and assessment.

Bio Michelle Hora Wilkerson (Click to view the full bio)

Michelle Hoda Wilkerson is an Associate Professor in the Graduate School of Education and the Graduate Group in Science and Mathematics Education at the University of California, Berkeley. Her research broadly explores the question: How is computing changing what is important to teach and learn in middle and high school science and mathematics classes?  []


This has led her to study how young people learn with and about scientific computing artifacts such as simulations, data analysis tools, and interactive visualizations. Recently, she has explored how learners‘ relationships with data — for instance as consumers, subjects, and creators of data — shapes how they understand and engage in data analysis. Michelle’s research has been supported by the United States National Science Foundation (NSF), the George Lucas Education Foundation, and Google Education Research. Her work has appeared in general and STEM-specific venues including Educational Researcher, Journal of the Learning Sciences, Science Education, and the Journal of Science Teacher Education and in 2020, her work was recognized with the American Educational Research Association’s Jan Hawkins Award for Humanistic Research and Scholarship in Learning Technologies.  

Session #07, Part 1: November 2, 2022, 5:00 p.m. (CET, UTC+1)  

Roadmap to Computational Thinking for the AI age: A challenge for Mathematics and Computer Science Education – Conrad Wolfram (England)  

Abstract: (Click to view the full abstract)

The importance of computational thinking for jobs, management and society has exploded over the last few decades. Meanwhile, the mainstay of preparation at school–mathematics education–increasingly struggles to match up. []

The importance of computational thinking for jobs, management and society has exploded over the last few decades. Meanwhile, the mainstay of preparation at school–mathematics education–increasingly struggles to match up.


Conrad Wolfram (Click to view the full bio)

Conrad Wolfram, physicist, mathematician and technologist, is Strategic Director and European Co-Founder/CEO of Wolfram–the world’s „computation company“ for over 30 years and makers of Mathematica, Wolfram Language and Wolfram|Alpha (which powers knowledge answers for Apple’s Siri, Amazon’s Alexa and Microsoft’s Excel).[]


Wolfram pioneers new approaches to data science and computation-based development, with technology and consulting solutions that drive innovation in analytics, software development and modelling. Working with start-ups to Fortune 500 companies, governments, most universities and school systems worldwide, Wolfram’s expertise spans all fields where computation is applied including medicine, finance and telecoms for R&D, deployment and education. Conrad is recognised as a thought leader in AI, data science and computation, pioneering a multiparadigm data science approach. Conrad is also a leading advocate for a fundamental shift of maths education to become computer-based or alternatively introduce a new core subject of computational thinking. He founded computerbasedmath.org and computationalthinking.org to fundamentally fix math education for the AI age – rebuilding the curriculum assuming computers exist. The movement is now a worldwide force in re-engineering the STEM curriculum. His groundbreaking book ‚The Math(s) Fix- an education blueprint for the AI age‘ www.themathsfix.org, released in 2020, for the first time lays out a roadmap to this fundamental reform. He attended Eton College and holds degrees in Natural Sciences and Mathematics from the University of Cambridge.

Session #07, Part 2: November 2, 2022, 6:10 p.m. (CET, UTC+1)  

Epistemic Programming and Creative Coding: Programming as an Empowering Means for Self-Expression and Communication – Sven Hüsing, Carsten Schulte and Dan Verständig (Germany)

Abstract: (Click to view the full abstract)

Programming and Computer Science are closely linked in the educational context – in computer science classes students are to be taught on how to program. However, there is little discussion about the educational role and relevance of learning programming at school. In our talk, we want to present ideas and approaches to use (data-driven) programming within one’s own „life“ and surroundings as an empowering means for self-expression and for stimulating social and political discourse and exchange. With these perspectives, we want to make the case for programming as a tool for thinking and communicating; or as we call it: programming as epistemic programming.[]

Here, the key idea is that programming should be reconsidered from being an external tool to being an integral part of the thinking and learning process itself that makes proper use of computing technology as cognitive tools.

Regarding data science and especially when performing data analyses, in particular, programming can be a means to gain own relevant insights, communicate them, and express oneself in this way. Tinkering with (personal) data, looking deeper and deeper into it and creating meaningful data analyses through programming can lead to insights about personally relevant topics from the own environment, which can then be used to draw attention to possible problems and develop ideas for solutions.

Implementing this approach in school could therefore be a useful way to provide students with an interest-centered and insight-driven perspective on programming, which could give this programming approach an increased educational relevance – especially regarding the everyday life.


Bio Sven Hüsing (Click to view the full bio)

Sven Hüsing is a PhD student for Computer Science Education at Paderborn University, Germany.[]

His current research focuses on the development and refinement of the Epistemic Programming concept as well as on its implementation in schools in order to empower young people to use programming as part of their cognitive processes and thereby as a means for self-expression and the exploration of own fields of interests.

Sven Hüsing is part of the Project on Data Science and Big Data at School (ProDaBi, www.prodabi.de), where he is developing and evaluating teaching modules, teacher trainings and curriculum ideas regarding the topics of Data Science, Big Data and Artificial Intelligence. Within this project, he is interested in connecting the Epistemic Programming approach to projects on data exploration. Here, students get the opportunity and freedom to explore their environment regarding their own ideas and interests through data analyses and present their insights together with the intertwined knowledge-acquisition and programming process in a comprehensible and reproducible way.
In this way, the role of data analyses and Epistemic Programming, in general, is supposed to be emphasized, so that students can perceive and self-determinedly use this means for knowledge acquisition and self-expression.


Bio Carsten Schulte (Click to view the full bio)

Carsten Schulte is professor for computing education research at Paderborn University, Germany. []

Work and research interests are: Philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.


Bio Dan Verständig (Click to view the full bio)

Dan Verständig is a professor for educational science with a focus on general media education at Bielefeld University, Germany. []

Interests and Fields of Work: Dan’s work focuses on the intersection of educational and media theory. In the context of extracurricular media education, Dan is concerned with the design of learning and educational occasions and the promotion of digital competencies. Besides, Dan’s research focuses on the empirical investigation of creative practices in dealing with digital technologies.

Session #08, Part 1: December 7, 2022, 5:00 p.m. (CET, UTC+1)  

Learning About and Learning with Artificial Intelligence in School: From Understanding of Basic AI Concepts to Trustworthy and Human-centric AI Tools – Ute Schmid  (Germany)

Abstract: (Click to view the full abstract)

Artificial intelligence (AI) is currently a much discussed topic evoking high expectations as well as fears. For a realistic assessment of the opportunities and risks of AI, basic understanding of what makes AI applications different from other computer programs is necessary. How and what AI concepts can be introduced in primary and secondary education depends on the age and the computer science background of the students. []

In the talk, a sample of topics and methods for introducing AI in school is presented – covering machine learning as well as automated reasoning, offering unplugged material for students without background in computer science as well as possibilities for more formal introduction of AI algorithms and AI programming. In the second part of the talk, learning analytics and intelligent tutor systems are introduced as applications of AI methods to support students and teachers. While learning analytics is mainly behavioristic with focus on applying machine learning to prediction of students’ performance, intelligent tutor systems are based on cognitive and constructivist principles with focus on AI methods for individualized diagnosis of misconceptions and feedback generation.


Bio Ute Schmid (Click to view the full bio)

Ute Schmid is a full professor of Cognitive Systems at University of Bamberg. She has master degrees both in computer science and in psychology. []


For more than 20 years she has been teaching and researching artificial intelligence with special focus on machine learning and cognitive modeling. Ute Schmid dedicates a significant amount of her time to measures supporting women in computer science and to promote computer science as a topic in elementary, primary, and secondary education. She won the Minerva Informatics Equality Award 2018 of Informatics Europe for her university. Since many years, Ute Schmid is engaged in educating the public about artificial intelligence and she gives workshops for teachers as well as high-school students about AI and machine learning. Ute Schmid is speaker of the task force AI in Education of the Artificial Intelligence Section of the German Informatics Society (FBKI of GI). She is a member of the Bavarian AI council and a EurAI fellow.

Session #08, Part 2: December 7, 2022, 6:10 p.m. (CET, UTC+1)  

A hands-on workshop to develop a set of potential goals for learning about AI – Jane Waite (England)  

Abstract: (Click to view the full abstract)

In this 70-minute workshop, we ask participants to contribute to the process of developing a set of potential goals for learning about AI. We will ask you to join a group that will consider students of a particular age and what they might know about AI. The age groups will be 7, 11, 14, and 18. []

In this collaborative process, we hope to glean insights, share ideas and reflect on the progression of knowledge and mental models students might develop about AI. To support our group activity, we will introduce the SEAME framework, a simple way to group goals as social and ethical, application, model, or engine focused. We will also provide examples of learning goals and candidate concepts to start the process. It would be great to start to tease out what potential threshold concepts are critical to the progression of AI learning, and we hope this session will get us thinking deeply about this.


Bio Jane Waite(Click to view the full bio)

Jane Waite is a senior research scientist at the Raspberry Pi Computing Education Research Centre. The center is a collaboration between the University of Cambridge and the Raspberry Pi Foundation. []

Jane researches the teaching and learning of computer science in schools, and one of the current focuses of her research group is what and how we might teach AI. Jane has researched how we teach programming to students and teachers, specifically design, computational thinking, PRIMM, semantic waves, and culturally relevant pedagogy. Jane has been an IT developer, a school teacher, and a teacher trainer.

Session #09, Part 1: January 18, 2023, 5:00 p.m. (CET, UTC+1)

Teaching reproducibility and responsible workflows – Nick Horton (USA) 

Abstract: (Click to view the full abstract)

Modern statistics and data science utilizes an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner. []

The National Academies of Science, Engineering, and Medicine’s (NASEM) 2018 „Data Science for Undergraduates“ consensus study identified the importance of workflow and reproducibility as a component of data acumen. But data science is increasingly important in primary and secondary education. How can we help students scaffold their analyses and foster responsible workflows as they begin to develop data fluency? In this talk, I will explore data tools and approaches that are intended to help students develop these important capacities.


Bio Nick Horton(Click to view the full bio)

Dr. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is passionate about improving quantitative and computational literacy for students with a variety of backgrounds and has worked to deepen engagement and mastery of higher-level concepts and data acumen. []


He is the editor of the Journal of Statistics and Data Science Education and is co-chair of the NASEM Committee on Applied and Theoretical Statistics. Nick served as the co-chair of the Planning Committee for the NASEM Workshop on K-12 Data Science and as chair of the Committee of Presidents of Statistical Societies. Dr. Horton has published more than 190 papers in statistics and biomedical research and four books on statistical computing and data science. He has been the recipient of a number of teaching awards and the American Statistical Association Founders Award. Dr. Horton is a fellow of the American Statistical Association and the American Association for the Advancement of Science.

Session #09, Part 2: January 18, 2023, 6:10 p.m. (CET, UTC+1)

Teaching Social Responsibility for a Tech-Powered World – Francine Berman (USA)

Abstract: (Click to view the full abstract)

Today’s world is complex and tech-driven. How do we use the tools of information technology to solve problems in a socially responsible way, i.e. in a way that both empowers us and promotes the well-being of the communities in which we live? []

The Public Interest Technology Initiative at UMass Amherst (PIT@UMass) engages students in learning and thinking about the impacts of technology in today’s world. Many of our courses explore the questions:  What is the public interest in a socio-technical world?  What strategies can we use to promote social responsibility in the public sector, private sector and for the general public?  What can each of us do to make the world a better place?  

This talk describes the evolution and current status of the PIT@UMass Initiative and discusses the creation of a signature course in Public Interest Technology as well as other efforts and projects whose purpose is to develop and inform socially responsible students, professionals, and citizens.


Bio Francine Berman (Click to view the full bio)

Dr. Francine Berman is the Director of Public Interest Technology and Stuart Rice Honorary Research Professor at the University of Massachusetts, Amherst. She is a data scientist whose work has focused on the social impacts of the Internet of Things as well as data cyberinfrastructure, stewardship and preservation. []


Dr. Berman is the former Director of the San Diego Supercomputer Center and co-founder of the international Research Data Alliance. She currently serves as a Trustee of the Alfred P. Sloan Foundation, and is on the Board of the Directors of the Marconi Society. Dr. Berman is the inaugural recipient of the ACM/IEEE-CS Ken Kennedy Award for „influential leadership in the design, development, and deployment of national-scale cyberinfrastructure“ (2009), recipient of the 2020 Paul Evan Peters award (given by Educause and the Association of Research Libraries), an elected member of the American Academy of Arts and Sciences, and a Fellow of the National Academy of Public Administration. In 2015, she was nominated by President Obama and confirmed by the U.S. Senate to become a member of the National Council on the Humanities.

Session #10, Part 1: April 19, 2023, 4:00 p.m. (CEST, UTC+2)  

Using Computational Essays to Support Student Creativity and Agency in Science – Tor Ole Odden (Norway)

Abstract: (Click to view the full abstract)

Computation holds great potential for enabling students to engage in creative, exploratory, and investigative scientific coursework. At the University of Oslo, we have been exploring this potential through the development and testing of a teaching tool known as a computational essay.[]

After using computational essays across several semesters of a large-enrollment physics course have found that they can serve a key role in both supporting and assessing students as they engage in open-ended, inquiry-based, disciplinarily-authentic coursework. In this talk, I will describe how we are conceptualizing student creativity and agency in science, how we use computational essays to support these qualities in our teaching, and possibilities for using computational essays as an alternative mode of assessment in science education.


Bio Tor Ole Odden (Click to view the full bio)

Tor Ole Odden is an associate professor of physics education research at the Center for Computing in Science Education, University of Oslo, Norway.[]

His research focuses on how computation can be leveraged to support student creativity and epistemic agency in science learning, how students acquire disciplinary computational literacy, and applications of natural language processing techniques to analyze science education research literature and data. Tor holds a PhD in physics education research from the University of Wisconsin-Madison, where his dissertation work focused on how undergraduate physics students make sense of abstract or tricky physics concepts, the specific strategies they use, and how instructors can help them in that process. Tor also holds a masters in physics from UW-Madison and a bachelors in physics from St. Olaf College in Northfield, Minnesota.

Session #10, Part 2: April 19, 2023, 5:10 p.m. (CEST, UTC+2)  

Design decisions in creating short data science courses for pre-university students – Tom Button and Ian Dickerson (United Kingdom)

Abstract: (Click to view the full abstract)

Over the past three years MEI has designed and implemented two optional data science courses for pre-university Mathematics students (aged 16-18). One of these courses is an open-access self-study course and the other is a taught programme, delivered via online lessons.[]

In this session we will describe the major decisions taken when designing these courses:

  • How to develop students’ ability to make context-based decisions when working with data.
  • How to use a programming language without the course feeling like a coding course.
  • How to introduce machine learning to students working at this level.

There will be an opportunity to view some of the resources and also discuss the implications for introducing data science to learners at this level.


Bio Tom Button (Click to view the full bio)

Tom Button is the Mathematics Technology Specialist for MEI (an independent UK mathematics education charity). []

He is leading on MEI’s Data Science courses for students. He has almost 20 years’ experience in the integration of technology into resources, professional development and assessment for mathematics. Prior to this he taught mathematics in colleges for 10 years.


Bio Ian Dickerson (Click to view the full bio)

Ian Dickerson taught in London secondary schools for nearly two decades before joining the founding team at Ada, the National College for Digital Skills in 2016.[]

In his five years as Head of Maths, he developed a pioneering approach to the level 3 curriculum that places coding front and centre. He now spends half of his time developing Data Science courses with MEI (an independent UK mathematics education charity), and the other half supporting Ada as their Data Specialist.

Session #11, Part 1: May 17, 2023, 4:00 p.m. (CEST, UTC+2)  

How much mathematical modeling is in AI? – Martin Frank and Sarah Schönbrodt (Germany)

Abstract: (Click to view the full abstract)

We will try to argue that mathematical modeling, in the sense of a certain kind of mathematical thinking in an interdisciplinary context, is becoming ever more important in the age of digitalization, the data deluge, and AI. It should therefore play a key role in education.[]

We will try to outline how this could be achieved. We will also raise questions: How mechanical does mathematics education have to be? How much room is there for creativity? How to balance disciplinarity and interdisciplinarity? How to create awareness of the key role of mathematical modeling?
In addition, we present tested, computer-based teaching material for mathematical modeling projects with high-school students. The material was developed within the CAMMP project (www.camp.online) and focuses on the problem-oriented development of the mathematical foundations of AI methods. We try to show that it is indeed possible to embed AI into mathematics education in a meaningful way.


Bio Martin Frank (Click to view the full bio)

Martin Frank is professor for Computational Science & Mathematical Methods at the Karlsruhe Institute of Technology (KIT) and director of the Steinbuch Centre for Computing. []

After his studies of mathematics (Diploma 2001) and physics (diploma 2004) at Darmstadt, Martin Frank received his doctorate in 2005. After a junior professorship at TU Kaiserslautern he was Professor of Mathematics at RWTH Aachen University (2009-2017). There he founded the CAMMP (Computational And Mathematical Modeling Program) education lab. Since 2017, he has been a professor at KIT, where he is an active researcher in both mathematical methods (including AI) and mathematics education.


Bio Sarah Schönbrodt (Click to view the full bio)

Sarah Schönbrodt is a postdoctoral researcher and team leader at KIT. She completed her PhD in the field of subject-matter didactics at KIT after her teacher training studies at RWTH Aachen University (2018-2022). []

Since 2022, she is a postdoctoral researcher and head of CAMMP and Simulierte Welten (Simulated Worlds) at KIT. Her research focuses on the mathematical background of AI methods – from a mathematics education perspective. She is also involved in “KI macht Schule”, a non-profit initiative that designs and conducts courses for school classes and advanced teacher trainings on AI combining 3 dimensions: technical foundations, applications, and ethical considerations.

Session #11, Part 2: May 17, 2023, 5:10 p.m. (CEST, UTC+2)  

Reasoning with Data in School-Based Citizen Science – Dani Ben-Zvi (Israel)

Abstract: (Click to view the full abstract)

The United Nation’s fourth sustainable development goal calls for inclusive and equitable quality education for all. This includes broad educational goals such as supporting students’ agency to navigate a complex and uncertain world with broad sets of knowledge, skills, attitudes, and values.[]

Based on our work at the Taking Citizen Science to School (TCSS) research center, we find an opportunity to achieve such goals in school participation in a social phenomenon, known as citizen science, where scientists partner with the public to advance scientific research. Specifically, as citizen science often involves exploration of large and messy sets of data, school-based citizen science is a highly fertile ground for nurturing both scientific and data literacies among students.

For instance, in the TCSS Radon project, implemented in dozens of school in Israel, students learned from scientists about Radon and some unsolved scientific issues regarding its nature and measurement. They collected data by measuring Radon levels in their homes, analyzed a collective dataset from all schools, made informal inferences, and communicated them to their community and to the scientists. Their learning was scaffolded by a sequence of learning activities co-designed by the TCSS community and unique technology-enhanced tools to support data analysis and modeling. Findings indicate mutual benefits for students and scientists. Students developed competences for making sense of large and messy datasets, and for making informed informal inferences based on their explorations. The scientists, based on the data collected by students, developed a technique for identifying buildings with high Radon concentrations.


Bio Dani Ben-Zvi (Click to view the full bio)

Dani Ben-Zvi is a professor in statistics education and educational technologies in the Faculty of Education at the University of Haifa, Israel. []

He is the head of the Department of Learning and Instructional Sciences. His research interests draw upon two central aspects of human life: Statistical thinking and technology. He is the co-founder and a co-chair of the International Collaboration for Research on Statistical Reasoning, Thinking, and Literacy, a leading research group in the area of statistics education. He is the President-Elect of the International Association for Statistical Education.

Session #12, Part 1: November 29, 2023, 4:00 p.m. (CEST, UTC+2) 

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. (CEST, UTC+2) 

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.

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. 

Registration

The colloquium is open and free for everyone and will be held via Zoom. To register for the sessions #01-#03 of the colloquium, 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: prodabi@mail.upb.de
Please distribute the information to interested colleagues so that they can also register.

Please register here for the colloquium