Computational Thinking : Course Overview

Computational Thinking

1. Pertemuan #01: Course Overview & CT Concepts Catherine Olivia Sereati - FT Unika Atma Jaya FTA 101 BERPIKIR KOMPUTASIONAL Team Dosen : Dr. Ir. Catherine Olivia Sereati., ST., MT Gregorius Airlangga ., PhD Julius Bata., S.Kom ., M.Kom

2. Agenda RPS, Kontrak Kuliah Gambaran kuliah Konsep Berpikir Komputasional

3. RPS & Kontrak Kuliah

4. RPS Deskripsi Mata Kuliah Kemampuan menyelesaikan persoalan membutuhkan cara berpikir yang terstruktur. Berpikir komputasional merupakan cara berpikir terstruktur yang terdiri dari beberapa konsep seperti dekomposisi, abstraksi , pengenalan pola dan algoritma . Mata kuliah Berpikir Komputasional memberikan pengenalan terhadap konsep – konsep berpikir komputasional, menggunakan konsep – konsep tersebut untuk menyelesaikan persoalan serta menerapkan dalam komputer. Pada m ata kuliah ini mahasiswa akan mempelajari bagaimana menggunakan dekomposisi, abstraksi, pengenalan pola, dan algoritma untuk menyusun suatu solusi permasalahan . Selain itu Mahasiswa juga akan dikenalkan dengan bahasa pemrograman untuk mengimplementasi solusi yang dibuat. Computational Thinking

5. RPS Capaian Pembelajaran Mahasiswa mampu mengemukakan pendapat secara tertulis secara lisan dan bekerja secara individu atau berkelompok Mahasiswa mampu menyusun algoritma dalam menyelesaikan suatu permasalahan Mahasiswa mampu membuat program komputer sederhana menggunakan Bahasa Pemrograman Mahasiswa mampu menerapkan konsep berpikir komputasional pada suatu studi kasus sederhana Asesmen : Tes kinerja ( praktik ) dan Tugas ( proyek akhir ) Komposisi nilai : UTS : 20 % UAS : 25 % Tugas : 55 %

6. RPS Topik Course Overview, Programming Language Programming Language Basic program structure: control constructs and data types CT concepts – Abstraction CT concepts – Decomposition CT concepts – Pattern recognition CT concepts – Algorithm Program structure: Function Program structure: Testing & Debugging Case Study

7. Perkuliahan Kuliah tatap muka (teams atau gather.town ), elearning.atmajaya.ac.id Mahasiswa diharapkan mencoba latihan selama dikelas dan latihan mandiri Blended Gamified + game-based learning + active class-room

8. Konsep Berpikir Komputasional

9. Pillars of Computational Thinking 9 P r o pe r t y o f P e nn E n g i n e e r i n g

10. What is computational thinking? Computational thinking refers to the thought processes involved in expressing solutions as computational steps or algorithms that can be carried out by a computer. ( Cuny , Snyder, & Wing, 2010; Aho , 2011; Lee, 2016). From Digital Promise: https://digitalpromise.org/initiative/computational-thinking/computational-thinking-for-next-generation-science/what-is-computational-thinking/

11. Breaking a complex problem into more manageable sub-problems Putting the solutions to the sub- problems together gives a solution to the original, complex problem 11 P r o pe r t y o f P e nn E n g i n e e r i n g D e c omp o s i t i on

12. Decomposition: Outlining a Paper Introduction 12 P r o pe r t y o f P e nn E n g i n e e r i n g Conclusion Bo dy

13. Mapping the Earth 13 P r o pe r t y o f P e nn E n g i n e e r i n g

14. Collecting the Data 14 P r o pe r t y o f P e nn E n g i n e e r i n g

15. Stitching the Images 15 P r o pe r t y o f P e nn E n g i n e e r i n g

16. Functionality: Zoom and Search 16

17. Pattern Recognition

18. F i n di n g s i m il a riti e s o r s h a r e d characteristics within or between problems Makes the problem easier to solve since the same solution can be u s ed f o r eac h o cc u r r e n ce o f t h e pattern 18 Property of Penn Engineering Pattern Recognition

19. Pattern Recognition Drawing Dogs 19 Property of Penn Engineering

20. Pattern Recognition Drawing Different Dogs 20 Property of Penn Engineering

21. Social Media Site: Photo Albums M e t a d a ta Name(s) Date Location ... Photo A lbum C o m p r e s s 21 Property of Penn Engineering Data S e r v er 👤👤 👤👤 👥👥 👤👤 👤👤 👥👥 👥👥

22. Data Compression 5 3 1 2 1 2 2 22 Property of Penn Engineering

23. Data Compression : problem example

24. Data Representation & Abstraction

25. Determining what characteristics of the problem are important and filtering out those that are not Use these to create a representation of what we are trying to solve 25 P r o pe r t y o f P e nn E n g i n e e r i n g Data Representation & Abstraction

26. Important: name and billing address student id on-campus address phone number ... 26 P r o pe r t y o f P e nn E n g i n e e r i n g Not Important: favorite color shoe size food preferences ... Data Representation: Students

27. Important: author list title ISBN publication date edition category ratings summary ... 27 P r o pe r t y o f P e nn E n g i n e e r i n g Not Important: color of the cover birthplace of authors complete contents of the book … Data Representation: Books

28. ALGORITHMA

29. Fill electric tea kettle Bring it to a boil Pour hot water in cup Put teabag in cup Steep for 4 minutes Remove teabag 29 Property of Penn Engineering Making a Cup of Tea

30. Step- by- step instructions of how to solve a problem Identifies what is to be done (the instructions), and the order in which they should be done. 30 Property of Penn Engineering Algorithms

31. Often expressed as something humans understand Eventually translated into sequences of computer instructions For example, we will discuss “coding” algorithms using Python 31 Property of Penn Engineering What is an Instruction?

32. Simple Flowchart Instruction1 Instruction2 32 Property of Penn Engineering

33. Flowchart: Making a Cup of Tea Start Stop Fill electric kettle with water Boil water Fill cup with hot water Put teabag in cup Let steep for 4 minutes Remove teabag 33 Property of Penn Engineering

34. Flowchart: Making a Cup of Herbal or Black Tea type = “black”? Stop Start Input: “black” or “herbal” tea? Fill electric kettle with water yes Store input in variable type Temperature = 212 Steep = 4 Temperature = 180 Steep = 5 Heat water to value of temperature °F Let steep for value of steep minutes Remove teabag no Put type teabag in cup 34 Property of Penn Engineering

35. Flowchart: Making a Cup of Tea, Revisited Start Stop Fill electric kettle with water Boil water Fill cup with hot water Put teabag in cup Let steep for 4 minutes Remove teabag 35 Property of Penn Engineering

36. Flowchart: Making a Cup of Tea Efficiently Fill electric kettle with water Boil water Fill cup with hot water Stop Put teabag in cup Start 36 Property of Penn Engineering

37. Case Studi : Writing Paper

38. Dekomposisi Abstraksi Pengenalan pola Algoritma Problem : Menulis artikel / makalah Step 1 st

39. Dekomposisi Abstraksi Pengenalan pola Algoritma Problem : Menulis artikel / makalah Makalah Introduction Body Conclusion

40. Dekomposisi Abstraksi Pengenalan pola Algoritma Problem : Menulis artikel / makalah Makalah

41. Dekomposisi Abstraksi Pengenalan pola Algoritma Problem : Menulis artikel / makalah Makalah referensi

42. Dekomposisi Abstraksi Pengenalan pola Algoritma Problem : Menulis artikel / makalah Algoritma menulis makalah Menentukan ide pokok makalah Mencari dan membaca makalah referensi Menulis paragraf pembuka Menulis beberapa paragraf isi pembahasan Menulis kesimpulan Membaca seluruh makalah Jika ada kesalahan , kembali ke Langkah 3, jika tidak lanjut langkah 8 selesai

43. Case studi 2: https://www.coursera.org/learn/compthinking/lecture/u1EfA/introduction-to-the-graphic-organizer

44. Assignment : Make algorithma for case study 2

45. De c o m p o s i t i on : b r ea k in g d o w n a co mpl e x p r o bl e m in t o sm a ll e r p a r t s Pattern recognition: finding the similarities among smaller problems Data representation and abstraction: describing data in a structured manner and generalizing details A l gor i th m s : s tep b y s t e p ins t r uc t i o ns f o r sol v in g t h e p r o b l em 45 P r o pe r t y o f P e nn E n g i n e e r i n g Conclusion Pillars of Computational Thinking

46. Next Intro to Programming Language

47. References : Computational Thinking for Problem Solving University of Pennsylvania https://www.coursera.org/learn/compthinking/lecture/ Problem Solving Using Computational Thinking University of Michigan https://www.coursera.org/learn/compthinking/home/module/1