1. A New Computer Science Curriculum for All School Levels in Poland Maciej M. Sysło University of Toruń, University of Wrocław syslo@mat.umk.plsyslo@mat.umk.pl, http://mmsyslo.pl/http://mmsyslo.pl/

2. Overview 2 Maciej M. Sysło  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

3. The School System in Poland (2008) 1st stage integrated Pre-school year 7 - 9 6 10 - 12 2nd stage 13 - 15 16 - 18 Primary education Secondary education 19 - 18 Upper – high school Lower – gimnazjum, middle school Tertiary education – University Computer lessons (ICT) ICT and Informatics for all with elements of algorithmics Informatics for all students, 1h Informatics adv. – elective, 6h Informatics education anything related to computers in schools 3 From 2015-2016: Informatics for all students with elements of programming Informatics introduced in our curriculum in 1985 for HS has never been removed !!! Computer Science education 2015: ICT/Informatics as a stand-alone subject on all school levels !!!

4. 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 4 Maciej M. Sysło

5. 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 constructing computer solutions → using ready-made tools computer science for some students → information technology for all informatics (CS) still for some students (advanced level) 2015/2016: informatics (CS) for all based on computational thinking 5 Maciej M. Sysło

6. 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 6 Maciej M. Sysło

7. 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 7 ICT for all Maciej M. Sysło Shift: algorithmic thinking to computational thinking informatics for informatics to informatics for all

8. 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 8 Maciej M. Sysło 1924 Seymour Papert used „computational thinking” in his paper of 1996

9. Computational thinking Proceedings of ISSEP: Informatics in Secondary Schools – Toruń 2008 9 Maciej M. Sysło Text book (2011) for: 1 hour/week of Informatics for all students in high schools computational thinking approach contains a module on programming learning strategy: Project Based Learning + Flipped Classroom

10. Computational (algorithmic) thinking (CT, CAT) 11 out of 54 papers in ITiCSE’15 (Vilnius) 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 10 Maciej M. Sysło

11. The new curriculum Introduction on the importance of computer science for our society in general and for our school students in particular 11 Maciej M. Sysło Unified aims – the same for all levels, define five knowledge areas in the form of general requirements Purpose of study, formulated adequately to the school level. 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 K, 1-3 Grades 4 - 6 Grades 7 – 9 Grades 10 -12 HS The Council for Computerization of Education, Ministry of National Education

12. 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. 12 Maciej M. Sysło ICT The Council for Computerization of Education, Ministry of National Education

13. Challenges 13 Maciej M. Sysło 1.How to motivate and engage students through K -12, for 12 years, e.g. learning programming requires constant practice 2.The role of coding (programming) 3.When and how to switch from visual to textual programming? 1.Visual – for beginners, non-professional – Scratch, Blokly in the Hour of Code, the Code Studio 2.Textual – for those who seriously think about CS – we don’t want to loose them – Python, C++, …

14. 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 when appropriate, we extend unplugged CS by adding … a computer 14 Maciej M. Sysło

15. 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 10-12 and vocational schools) – CS/ICT specializations 15 Maciej M. Sysło

16. The new curriculum – the role of programming Questions: learn to code or code to learn or maybe code to earn 16 Maciej M. Sysło to learn how to program? to learn CS concepts? to code? to program? CS concepts? CS? certificate, degree $, ¥, £ in HS in K-5?

17. 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 17 Maciej M. Sysło

18. Introducing CS concepts – to kids (1-3, 4-6) 18 Maciej M. Sysło Examples from the Childrens’ universities (with A.B. Kwiatkowska) We use all three forms of children 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, Python We also combine our hobbies with problem scenarios: collecting computing instruments and graph theory as my „research hobby” Bebras tasks – the source of problem situationsBebras tasks The Hour of Code – introduction to (visual) programming with puzzlesThe Hour of Code

19. Collection of mechanical instruments for computing 19 Maciej M. Sysło

20. School mechanical calculators 20 Maciej M. Sysło

21. School mechanical calculators 1920 Quipu, South America Soroban, Japan World vice-Champion in mental calculations 21 Maciej M. Sysło

22. Children playing with machines 6 years old student !!! Slide rules – 400 anniversary of inventing logarithm by John Napier 22 Maciej M. Sysło

23. Playing with machines – the Educated Monkey For 5 x 5 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 !!! 1916 23 Maciej M. Sysło

24. 1617 Made in 2007 Napier’s rods – 400 anniversary of inventing logarithm, in 2014 24 John Napier 1550-1617 Maciej M. Sysło

25. 25 x 25 125 + 50 625 2 5 2525 4 1 1 2 5 0 0 0 + 2056 Traditional multiplication: Using Napier’s rods 25 First calculator Concepts: the algorithm for multiplying two number using Napier’s rods and then with pencil and paper Maciej M. Sysło

26. Schickard’s calculator, 1624 From a letter of Schickard to Kepler Found in late 50’ XX C Replica of Schickard’s calculator, 2005 W. Schickard used round rods 26

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28. The Hanoi Towers 28 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 Maciej M. Sysło Concepts: game algorithm efficiency (complexity) recursion (MS, HS)

29. 29 Maciej M. Sysło 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

30. 30 Greedy algorithm – Dijksta’s Algorithm Shortest path – Beaver task Maciej M. Sysło

31. 31 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? Shortest path – introduction Maciej M. Sysło

32. 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 32 Shortest path – PISA task Concepts: graph models algorithm greedy approach shortest paths Dijkstra’s algorithm symmetry Maciej M. Sysło From Einstein to Diamond it takes 31 min – which way?

33. 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 33

34. 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. 34

35. 35 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 I st 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 Supporting activities Maciej M. Sysło The Council for Computerization of Education, Ministry of National Education

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