Technological progress has raised exponentially the need for digital skills and competences at all levels across the economy and society. Yet the level of digital skills of different segments of the population - be it professionals, adults or young people - remains unsatisfactory low. Georgi Dimitrov (European Commission, DG Education and Culture) will provide an overview of the priorities and objectives of the Commission’s Digital Education Action Plan 2021-2027, with particular attention to the initiatives aiming at strengthening digital skills development. By looking at recent available data, he will focus on the advantages and the role that an effective provision of informatics in school education can play to promote active, responsible and safe use of technologies from a young age.
Georgi Dimitrov is responsible for the Digital Education unit in the European Commission, Directorate General for Education and Culture. He joined the European Commission in 2008 and was first involved in various roles in setting up the European Institute of Innovation and Technology (EIT). He then helped to develop and launch HEInnovate, an initiative by the European Commission and the OECD aimed at supporting universities to become more entrepreneurial. He led the development of the first Digital Education Action Plan adopted in January 2018 and also of the new Digital Education Action Plan 2021-2027 that was adopted in September 2020. Before joining the Commission, Georgi worked for a leading multinational telecommunication company and in a software start-up in Germany. Georgi studied at the University of Bonn (M.A.), the University of Erlangen-Nürnberg (PhD) and the Open University UK (MBA in Technology Management).
Computers, apps and programming languages are still commonly referred to as tools that help us accomplish tasks by amplifying particular skills such as calculating and remembering. Yet as computers and their apps have evolved into channels of communication among us and our appliances, programming languages are becoming a medium letting us interface with the world and express our ideas. I will present the Snap! visual programming language and discuss its design principles from the perspective of encouraging learners to approach programming not just as a tool for production but as a medium for exploration.
Snap! Is a Scratch-like programming language that treats code-blocks as first class citizens instead of confining them to an editing modality. Embracing nested data structures and higher order functions Snap! lets learners create arbitrary control structures and even custom programming languages with just blocks. Snap! has been developed for UC Berkeley’s introductory computer science course named “The Beauty and Joy of Computing”.
Jens Mönig is a researcher at SAP and makes interactive programming environments. He is fanatical about visual coding blocks. Jens is the architect and lead programmer, together with Brian Harvey, of UC Berkeley’s "Snap! Build Your Own Blocks" programming language, used in the introductory “Beauty and Joy of Computing” curriculum. Previously Jens has worked under Alan Kay on the GP programming language together with John Maloney and Yoshiki Ohshima, helped develop Scratch for the MIT Media Lab and written enterprise software at MioSoft. Jens is a fully qualified lawyer in Germany and has been an attorney, corporate counsel and lecturer for many years before rediscovering his love for programming through Scratch and Squeak. For leisure Jens likes guitar picking and strumming his mandolin.
The popular approaches to K-12 computing education today are based on analyzing and describing problems in a way that enables their solutions to be formulated as series of computational steps. Rule-based "classical" programming paradigms have come to dominate K-12 programming education, with some of their relevant key concepts and skills described under the title computational thinking (CT).
In the 2000s a number of data-driven technologies, most prominently machine learning (ML), have become commonplace in apps, tools, and services. Understanding some key ideas related to ML is becoming crucial for understanding how many key elements of our digital environment work. The power of traditional, rule-based computational thinking (CT1.0) to explain ML-driven systems is, however, limited, and new approaches to computing education are needed. A body of literature on how to teach some principles of ML and data-driven computing in K-12 education is emerging, but that body of literature relies on a set of concepts and skills very different from traditional CT1.0. This talk outlines the key changes in the conceptual landscape, educational practice, and technology for ML-enhanced CT (CT2.0) and compares it to the dominant computing education paradigm.
Applications of artificial intelligence (AI) are set to transform society, including how people work and learn. This growing ubiquity of AI in society poses significant challenges for educational systems: what will citizens in the 21st century need to know about, and do with, AI? Currently, there is very little research and experience on how schools and teachers adopt AI into the classroom and how our students work and learn together with AI.
In this keynote I will present some current work at our Centre for Change and Complexity in Learning to help address this issue. I will showcase some initiatives where students will work together with AI to solve complex problems. Our mission is to offer an AI learning environment where students can take ownership over AI, experiment with it and develop AI to follow their imagination. The environment is a social space for exploration and critical evaluation, it’s safe and inspiring. This is how we want students to treat AI. Rather than that AI is done to you, students should be able to play with AI, and through play, shape it so that AI starts to work for you and help you to go beyond your own capability.