Informatics
A Fundamental Discipline for the 21st Century
Michael E. Caspersen
Director, It-vest – networking universities
Honorary Professor, Department of Computer Science, Aarhus University
Special adviser for Executive Vice President Margrethe Vestager of the European Commission
mec@it-vest.dk
eMadrid Network
Madrid, 16th December 2022

April, 2019

• Ongoing work since 2012
• Formally founded in 2018 by
• Association for Computing Machinery (ACM) Europe Council
• Council of European Professional Informatics Societies (CEPIS)
– GI and 28 other national IT societies
• Informatics Europe – 160+ universities and research centres
• The International Federation of Information Processing (IFIP) joined
in 2020
• To advocate for the inclusion of informatics as a
foundational discipline in schools across Europe
• Chair: Wendy Hall
• ACM Europe Council: Judith Gal-Ezer and Andrew McGettrick
• Informatics Europe: Michael E. Caspersen and Enrico Nardelli
• CEPIS: Gerald Futschek and Luis Fernandes-Sanz
• IFIP (TC3): Don Passey and Mary Webb
• Advisor: Bobby Schnabel
• Activities described on the site http://informaticsforall.org
Informatics for All – a joint European endeavour

Brussels, March 2018
CECE, 2013-2017
(Committee on European Computing Education)
2017
2013
Presented to the European Commission
Brussels, April 2022

The top-three priorities for 2020-2030
Digital education
Green transition
Resilience

Digital Education Action Plan (DEAP)
2021-2027
Update of DEAP
• A top priority in Ursula von der Leyens Mission Letter to
Mariya Gabriel in 2019 when the new EC was formed.
Action 10
A focus on inclusive high-quality computing education
(informatics) at all levels of education.
2018
Proposal for
A coherent vision
and shared terminology
related to providing
informatics to all students in Europe.
as requested in action 10 of DEAP.

Michael E. Caspersen (Chair)
It-vest – networking universities, Denmark
Ira Diethelm
University of Oldenburg, Germany
Judith Gal-Ezer
The Open University of Israel
Andrew McGettrick
Strathclyde University, Scotland
Enrico Nardelli
University of Roma, “Tor Vergata”
Don Passey
Lancaster University, UK
Branislav Rovan
Comenius University, Slovakia
Mary Webb
King’s College London, UK
Informatics Reference Framework for School
Developed by “Group of 8”

Broad consultation
June – September 2021
Feedback from the networks of the four organisations
behind the Informatics for All coalition
and from 14 countries across Europe.
Integration of feedback and
preparation of current version
September 2021 – February 2022
Informatics Reference Framework for School
Key activity 2020-2022
Preparation of interim version
October 2020 – June 2021

Informatics Reference Framework for School
Characteristics
• Synthetic and short
• 17 pages
• A minimum set of high-level common requirements
• Room for national communities to derive curricula tuned to local culture and
needs while coherent with a common European vision of Informatics in School
• An enduring foundation of 11 core topics
• Foundational and invariant terms possessing temporal robustness
• Contemporary context and implications
• A brief contemporary interpretation of the core topics illustrating richness,
relevance for all (potential specialisation topics)
• Indicators of outcomes (annex)
• These are not intended to be prescriptive and are provided for illustrative
purposes only to stimulate thinking and action of cirricula designers.
• Future work
• Guidelines for using the framework (ready)
• Further elaboration on specialisation topics (in progress)
11 Core topics
Data and information
Algorithms
Programming
Computing Systems
Networks and communication
Human-computer interaction
Design and development
Digital creativity
Modelling and simulation
Privacy, safety and security
Responsibility and empowerment

Inclusion, diversity and gender
remain important issues in informatics
education.
Inclusion is a fundamental principle.
Diversity is a feature of inclusion.
Gender concern is an issue of diversity.
The gender issue is a particular concern;
engagement with informatics at an
early age can promote self-efficacy
and tackle gender stereotyping before
prevailing views become entrenched.
Compulsory informatics education
counteracts a tendency for girls to opt out
and puts the onus on curriculum
developers and teachers to create a
curriculum that engages girls as well as
boys.

4.2 Aims and objectives
At the end of upper secondary education, pupils will skilfully be able to:
1.Use digital tools in a conscious, responsible, confident, competent, and creative
way.
2.Understand the principles and practices of informatics and their multifaceted
applications.
3.Analyse, design, frame and solve problems "informatically" .
4.Creatively develop computational models to investigate and communicate
about phenomena and systems.
5.Identify and discuss ethical and social issues associated with computational
systems and their use, potential benefits, and risks.
More elaborate version on page 5 of the document.

4.3 Core topics
Core topic areas Description
Data and
information
Understand how data are collected, organised, analysed and used to
model, represent and visualise information about real-world artefacts and
scenarios.
Human-computer
interaction
Evaluate, specify, develop and understand interaction between people
and computing artefacts.
Responsibility and
empowerment
Critically and constructively analyse concrete computing artefacts as well
as advanced and potentially controversial techniques and applications of
informatics, particularly from an ethical and social perspective.
All 11 core topics are described using succinct descriptors (see table 1, page 6).

The report provides insights into
how informatics can be integrated
as a fundamental and scientific discipline
in school education in Europe.
The status quo of informatics in school
is analysed according to the
Informatics Reference Framework for School
published earlier this year by the
Informatics for All coalition.
Published 21th September 2022

- Reference year: 2020/2021
- Primary, general lower and upper
secondary education.
- Informatics as a distinctive discipline
(separate subject or integrated into other
subjects).
- 39 education systems in 27 EU member
states and 10 other Erasmus+ countries.
Scope and methodology

The second tier
Integration of informatics in other disciplines
e.g., natural science education

Mathematics
Informatics
The Vision
Informatics is a new aspect of ‘bildung’
– a new fundamental competence for all
Mathematics is (primarily) the language of the natural sciences
Informatics is a language of all disciplines
Informatics
Chinese
History Physics
Chemistry
Social science
Classical history
Music
Biology
Geology
Art
English
Literature
Marketing
Biotechnology
Psychology
German ... Philosophy
Spanish
Design Geography
Economics
Language
becoming

Danish Broadcasting Corporation’s
Rosenkjær Lectures
Peter Naur (1928-2016)
Turing Laureate (2005)
... da kan man ikke være i tvivl om at datalogien må have en plads i almenuddannelsen.
For at nå
̊ til en rimelig forestilling om hvordan denne placering bør være er det naturligt at
sammenligne med fag af lignende karakter. Man vil da nå frem til sproglære og matematik,
som er de nærmeste analoge. Fælles for de tre emner er også deres karakter af redskaber for
mange andre fag.
To conceive the proper place of ‘datalogi’ in the curriculum, it is
natural to compare with subjects of similar character. One will then
realise, that languages and mathematics are the closest analogies.
Common for the three is also their character as tools for many other
subjects.
1966-67

Spoken language Written language Mathematical language
Computational
language
Informatics as a fundamental discipline
A new language – a new cultural technique

It is indeed
too odd
for words
that half's
three quarters
of two thirds.
- Piet Hein
Natural vs. mathematical language

Central
Stochastic methods, differential equations
and lots of other “good stuff”
40 pages of non-trivial mathematics
Decentral
Simple local rule: (a+b) / 2
Emergence and dynamics "for free"
Agent-based modelling (ABM)
Mathematical vs. computational language
Wave model

Decentral
Simple local rule
Emergence and dynamics "for free"
Agent-based modelleling (ABM)
March 14, 2020
Mathematical vs. computational language (2)
Central
Stochastic methods, differential equations
and lots of other “good stuff”
25 pages of non-trivial mathematics
Epidemic model

1st ABM example : Spread of infection (2)
Description of the phenomenon at agent level
– a local model
Agent-based modelling
Transparent and accessible for all
Description of the phenomenon at
systems level – a centralised model
Mathematical analysis vs. agent-based modelling
Accessible for very, very few people
Coupled differential equations

1st ABM example: Spread of infection (1)
Infected
Immune
Receptive
Spread of infection
Time
Persons

One of the most difficult things in science (and any other discipline)
is to model the dynamics of processes
of system components.
Modelling the dynamics
of (complex) systems
But it is also a highly motivating way to approach the material,
to experiment and experience the subject matter
to understand details and large contexts.

Beyond the Centralized Mindset
Seymour Papert and one turtle Mitch Resnick and 1,000 turtles
Logo, 1967 StarLogo
(PhD, 1992)
Uri Wilensky (and a dolphin)
NetLogo
(PhD, 1993)

Agent-based modelling (ABM)
Agents have autonomy based on
• properties
(e.g., appearance, size, position, direction, speed)
• behaviour
(e.g., search south, avoid trees, follow neighbours)
• Agents’ properties and behaviour define not
only how they look and behave, but also
how they interact with each other and their
environment.

A White Paper from 2013
Future Directions in Computing Education Summit Part One:
Important Computing Education Research Questions
Palle Nowack

2017 2018-2020 2018-2022 2019-
Pilot project
Informatics in high school subjects

2nd ABM example: Tipping point – forest fire (1)
Trees are the agents
Green trees
One rule: 1) Do nothing
Red (burning) trees
Two rules:
1) Ignite neighbouring trees
2) Burn out
Density: 57 %

Density: 57 % Density: 61 %
2nd ABM example: Tipping point – forest fire (2)
Repeated simulations

Density: 57 %: ~ 8,3 %
Density: 61 %: ~ 83 %
2nd ABM example: Tipping point – forest fire (3)
[ 12.7 ; 9.1 ; 8.0 ; 7.2 ; 4.4 ; 8.0 ; ... ]
[ 81.5 ; 75.5 ; 84.5 ; 86.0 ; 85.8 ; ... ]

Systems analysis and comprehension
for discovery, expression and problem solving
Systemic comprehension is to see
– processes and interaction rather than static pictures
– connections and relations rather than individual parts
Recurring systemic properties across disciplines and domains
– feedback mechanisms
– exponential growth
– tipping points
– self organisation
– ...

Some resources
for inspiration and perspective

Keynote
Computational Literacy
as a Driver for
Disciplinary Renewal

Agent-based modeling in education
by Assistant Professor Arthur Hjorth, Aarhus University

The CMC Approach
Content – Modelling – Coding