1. From Algorithmic to. Computational Thinking on the Way to
From Algorithmic to Computational Thinking on the Way to Computing for All Students
Maciej M. Sysło
University of Toruń, University of Wrocław

2. Memories From „Elements of Informatics”
to Information and Communication Technology
Maciej M. Sysło

3. Innovations – a wheel?
Maciej M. Sysło

4. Overview School system in Poland and informatics education
Informatics education – shifts in approach
Computational thinking (CT)
The new curriculum
The role of programming
Introducing computer science concepts – examples
Supporting activities
Maciej M. Sysło

5. The School System in Poland (2008)
Informatics education
anything related to computers in schools
Tertiary education – University
Informatics for all students, 1h
Informatics adv. – elective, 6h
Upper – high school
Informatics introduced in our curriculum in 1985 for HS has never been removed !!!
Secondary education
Lower – gimnazjum,
middle school
ICT and Informatics for all
with elements of algorithmics
2014: ICT/Informatics as a stand-alone subject on all school levels !!!
Primary education
2nd stage
From :
Informatics for all students
with elements of programming
Computer lessons (ICT)
1st stage
integrated
7 - 9
Pre-school year 6
Computer Science education

6. Informatics (CS) versus ICT
Informatics (Computer Science) is concerned with designing and creating informatics ‘products’ and ‘tools’, such as: algorithms, programs, application software, systems, methods, theorems, computers, …
ICT – applications of CS (computing) – concentrates on how to use and apply informatics and other information technology tools in working with information; can be also creative
Now (from 2015):
computer science education (CS education) – education in computer science
informatics education – includes CS education, ICT in other subjects, anything in schools related to computers
computing – the term not used in Poland
Maciej M. Sysło

7. History: 1965 – … computers in education
1965 … 1985 …
Informatics curricula and teaching – computer science – there was no ICT – numerical methods, programming in Algol
in 90’
moves in education:
informatics (CS) → information technology
i.e.: constructing computer solutions → using ready-made tools
i.e.: good move?: computer science for some students → information technology for all (ITiCSE’99 Cracow)
informatics (CS) still for some students
2015/2016: informatics (CS) for all → based on computational thinking
Maciej M. Sysło

8. Computer science education in crisis some answers
students have tested enough ICT in their upbringing and they want something different, in particular at a university level
the traditional school and university curricula in computing are unattractive to present-day students
students (but not only students) do not distinguish between using and studying (computer tools)
opposed to a vocational qualification, the mission of schools and universities is to develop understanding, rather than skills only
The lack of adequate CS education in schools
On the other hand – there is still a demand for experts and specialists in computing and its applications
Maciej M. Sysło

9. UK: harmful ICT replaced by Comp Sci – 2012
September2014:
Computing at School
On all stages of K-12
Maciej M. Sysło

10. Informatics education – shifts in approach
60’ – 90’: algorithmic thinking: creating programs, algorithmics, programming – there was no ICT
90’ – ICT era: step back: basic computer literacy – the capability to use today’s technology
beginning of 2000: fluency with ICT – the capability to use new technology as it evolves
J. Wing, 2006: computational thinking – competencies built on the power and limits of computing:
3R (Reading + wRiting + aRithmetic) + computational thinking
ICT
for all
Shift: algorithmic thinking to computational thinking
informatics for informatics to informatics for all
Maciej M. Sysło

11. Computational thinking (J. Wing, 2006)
Includes a range of mental tools for problem solving originated in computer science:
reduction and decomposition of complex problems
approximation, when exact solution is impossible
recursion: inductive thinking
representation and modeling of data or phenomena
heuristic reasoning (thinking)
The influence on other disciplines – in mathematics:
the purpose of computing is insight not numbers [R.W. Hemming, 1959]
Applies to all other disciplines
1924
Maciej M. Sysło
Seymour Papert used „computational thinking” in his paper of 1996

12. Computational thinking
Text book for (2011):
1 hour/week of Informatics for all students in high schools
computational thinking approach
contains a module on programming
learning strategy proposed: Project Based Learning + Flipped Classroom
Proceedings of ISSEP: Informatics in Secondary Schools – Toruń 2008
Maciej M. Sysło

13. Computational (algorithmic) thinking (CT, CAT)
11 out of 54 papers in ITiCSE’15 refer to CT or CAT:
extension od algorithmic thinking (P. Denning)
provides a framework for reasoning about solving problem
thinking with many levels of abstraction
mental activities required to deal with a model of computation
a mode of thought that goes well beyond computing
a collection of key mental tools and practices originated in CS
involves concepts, skills, competencies that are at the heart of CS
connected to CS and addressed to all students
a fundamental competency of youngsters in various domains
Important and useful mode of thinking in almost all disciplines and school subjects as an insight into can and cannot be computed
CAT is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems
a computational mode of thought, valuable to all members of society
Maciej M. Sysło

14. The new curriculum
Introduction on the importance of computer science for our society in general and for our school students in particular
Purpose of study, formulated adequately to the school level.
Grades
K, 1-3
Unified aims – the same for all levels, define five knowledge areas in the form of general requirements
Grades
4 - 6
Detailed Attainment targets. The targets, grouped according to their aims, define the content of each aim adequately to the school level. Thus learning objectives are defined that identify the specific computer science concepts and skills students should learn and achieve in a spiral fashion through the four levels of their education.
Grades
7 – 9
Grades HS
Maciej M. Sysło
The Council for Computerization of Education, Ministry of National Education

15. The new curriculum: Unified Aims for each Level
1. Understanding and analysis of problems based on logical and abstract thinking, algorithmic thinking, algorithms and representations of information.
2. Programing and problem solving by using computers and other digital devices – designing and programming algorithms; organizing, searching and sharing information; utilizing computer applications;
3. Using computers, digital devices, and computer networks – principles of functioning of computers, digital devices, and computer networks; performing calculations and executing programs;
4. Developing social competences – communication and cooperation, in particular in virtual environments; project based learning; taking various roles in group projects; equity.
5. Observing law and security principles and regulations – respecting privacy of personal information, intellectual property, data security, netiquette, and social norms; positive and negative impact of technology on culture, social live and security.
ICT
Maciej M. Sysło
The Council for Computerization of Education, Ministry of National Education

16. Challenges
How to motivate and engage students through K -12, for 12 years, e.g. learning programming requires constant practice
The role of coding (programming)
When and how to switch from visual to textual programming?
Visual – for beginners, non-professional
Textual – for those who seriously think about CS – we don’t want to loose them
Maciej M. Sysło

17. The new curriculum – general comments
remember: computer science ≠ programming
concepts before tools, before programming:
problem (situation)
concepts
algorithms
programs (-ming)
there are plenty of ways to introduce/teach computer science concepts … without computers: computer science unplugged, for instance Bebras tasks (panel)
when appropriate, we extend unplugged CS by adding … a computer
Maciej M. Sysło

18. The new curriculum – motivation
personalization for students
on all levels
included in the Attainment targets (levels: 7-9, 10-12)
personalization (freedom) for teachers – they know their students very well
from level 7-9: focus on real world problems and applications which are meaningful for students
HS (level and vocational schools) – CS/ICT specializations
Maciej M. Sysło

19. The new curriculum – the role of programming
Questions:
learn to code or
code to learn
or maybe
code to earn
to learn how to program?
to learn CS concepts?
to code?
to program?
CS concepts?
CS?
certificate, degree
$, ¥, £ in HS
in K-5?
Maciej M. Sysło

20. The new curriculum – the role of programming
remember: computer science ≠ programming
how to use extra curricular coding activities (the Hour of Code) in the classroom?
Programming
programming is a tool, not a goal
which programming language? – there are 3000
any, which can be used to introduce and illustrate concepts
introduce new constructs when needed
a program is a message for a computer and also to other people
different languages different programming methods
visual versus textual languages and programming
Maciej M. Sysło

21. Introducing CS concepts – to kids (1-3, 4-6)
Examples from the Childrens’ universities (with A.B. Kwiatkowska)
We use all three forms of activities:
visual learning (pictures, objects, abstract and physical models, …)
auditory learning (exchange ideas, discussions, group work, …)
kinesthetic learning (physical activities)
We work in environments consisting of three stages:
cooperative games and puzzles that use concrete meaningful objects – discovering concepts (Bebras tasks, The Hour of Code)
computational thinking about the objects and concepts – algorithms, solutions
programming – Scratch, The Hour of Code, Logo
We also combine our hobbies with problem scenarios: collecting computing instruments and graph theory as my „research hobby”
Bebras tasks – the source of problem situations
The Hour of Code – introduction to (visual) programming with puzzles
Maciej M. Sysło

22. Collection of mechanical instruments for computing
Maciej M. Sysło

23. School mechanical calculators
Maciej M. Sysło

24. School mechanical calculators
Soroban, Japan
1920
World vice-Champion in mental calculations
Quipu, South America
Maciej M. Sysło

25. Children playing with machines
6 years old student !!!
Slide rules – 400 anniversary of inventing logarithm by John Napier
Maciej M. Sysło

26. Playing with machines – the Educated Monkey
How to:
multiply two numbers
divide two numbers
factor a number
With another table, can be used for additions
Concepts:
math basic operations,
the use of a calculating instrument,
own calculating methods
algorithms
Children: where we can buy these instruments !!!
For 5 x 5
1916
Maciej M. Sysło

27. Napier’s rods – 400 anniversary of inventing logarithm, in 2014
John Napier
Made in 2007
1617
Maciej M. Sysło

28. First calculator
Traditional multiplication:
Using Napier’s rods + 25
x 25
125
+ 50
625 1 2 5 4 1 2 5 6 2 5
Concepts:
the algorithm for multiplying two number using Napier’s rods and then with pencil and paper
Maciej M. Sysło

29. Schickard’s calculator, 1624
W. Schickard used round rods
From a letter of Schickard to Kepler
Found in late 50’ XX C
Replica of Schickard’s calculator, 2005

31. The Hanoi Towers The Hanoi Towers story
In the beginning: we ask kids to play and try to find „an algorithm” and calculate the number of moves for different numbers of rings
Expected: algorithms and tables with the number of moves
Then: kids play with (against) a computer program
Finally: they verify initial findings
Extra (MS, HS): recursive solution, minimum number of moves
Concepts:
game
algorithm
efficiency (complexity)
recursion (MS, HS)
Maciej M. Sysło

32. Recursion, recursive thinking – CS Unplugged
Ershov, 1988:
eat porridge;
if the plate is empty then STOP
else
eat a spoonful of porridge;
eat porridge
Syslo, 2009:
dance;
if the music is not played then STOP
else
make a step;
dance
Maciej M. Sysło

33. Shortest path – Beaver task
Greedy algorithm – Dijksta’s Algorithm
Maciej M. Sysło

34. Shortest path – introduction
Kids are working with real situation – motivates them:
Computer: Find your house and your school on the Google map
Find your way to/from school
Find shortest paths (distance and time) to/from school by different transportation means: on foot, by bicycle, by car, public transportation
Paper and pencil: Table to compare which is the shortest path (time/distance) to school?
Maciej M. Sysło

35. Shortest path – PISA task
From Einstein to Diamond it takes 31 min – which way?
Concepts:
graph models
algorithm
greedy approach
shortest paths
Dijkstra’s algorithm
symmetry
Typical approach, a greedy type: the nearest neighbor method.
It doesn’t work !
However it works when you go from Diamond to Einstein !!!
Remember: Dijkstra’s algorithm which a greedy method is optimal
Maciej M. Sysło

36. Problem situations at the Children’s University
Problem situations – concepts
map coloring – independence
sorting and preserving order – transpositions, min/max, binary search
matching – assignments, stable pairs (marriages)
Fibonacci numbers – in the nature, golden ratio
Eulerian graphs – how to draw a picture
Euclid algorithm – divide and conquer, logarithm
factorial – TSP problem
chess puzzles – graph models
RAM model – Turing machine
robot programming

37. Conclusions With the new curriculum:
students acquire a broad overview of the field of informatics and applications
informatics instruction focuses on problem solving and CT;
informatics is taught independently of specific application software, languages, environments – students are free to make their own choice;
informatics is taught using problem situations coming from school subjects and real-world applications;
informatics education provides a background for the professional use of computers in other disciplines.
students experience a solid foundation in CT through problem solving with computers;
students experience that programming is a creative process;
students learn how to collaborate on projects, which are mostly a group task;
students witness that computing enables innovation also in other fields;
PBL and flipped learning style contribute to a better personalization of learning.

38. Supporting activities
Teacher preparation: a teacher is the most important „technology”!
standards, evaluation and support in the classroom
in-service training at universities – based on standards
Web service – materials, MOOCs
Our goal: a computer science teacher should be prepared as a Ist degree computer science graduate (3 years of study)
Comments to the curricula of the other subjects how to use computational thinking in solving problems in other areas
PBL and flipped learning – after school activities of students – extra hours of school learning
Computer science oriented tasks in national school tests
Maciej M. Sysło
The Council for Computerization of Education, Ministry of National Education

39. Thank you for your attention
and don’t forget to: