Previous Sessions

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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:

• P. Burdman, Degrees of freedom: Probing math placement policies at California colleges and universities (Report 3 of a 3-part series), Report issued by PACE Foundation and Learning Works, 2015, available at https://edpolicyinca.org/sites/default/files/PACE%20Report%203%2005-2015%20v7.pdf.
• J. Burke et al., Participatory sensing, Center for Embedded Network Sensing, 2006, available at https://escholarship.org/uc/item/19h777qd
• R. D. De Veaux et al., Curriculum Guidelines for Undergraduate Programs in Data Science, Annual Review of Statistics and Its Application, March 2017, 4:15-30.  https://doi.org/10.1146/annurev-statistics-060116-053930
• R. Gould, Toward Data-Scientific Thinking, Teaching Statistics, May 2021. DOI:10.1111/test.12267
• S. D. Levitt, America’s math curriculum doesn’t add up (Ep. 391). Freakonomics podcast, 2019, available at  https://freakonomics.com/podcast/math-curriculum

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).

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.

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. 

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.

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.

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.

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.

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.

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.

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.

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.

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.  

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.