COURSE OUTLINE: PS0001

Course Title

Introduction to Computational Thinking

Course Code

PS0001

Offered Study Year 1, Semester 1
Course Coordinator Thomas Peyrin (Assoc Prof) thomas.peyrin@ntu.edu.sg 6513 2027
Pre-requisites None
Mutually exclusive CE1003, CZ1003
AU 3
Contact hours Tutorials: 39, Technology-enhanced Learning: 13
Approved for delivery from AY 2020/21 semester 1
Last revised 23 Apr 2020, 15:08

Course Aims

Computational thinking (CT) is a problem solving process with the aid of computer; i.e. formulating a problem and expressing its solution in such a way that a computer can effectively carry it out. It includes a number of characteristics, such as breaking a problem into small and repetitive ordered steps, logically ordering and analyzing data and creating solutions that can be effectively implemented as algorithms running on computer. As such, computational thinking is essential not only to the Computer Science discipline, it can also be used to support problem solving across all disciplines, including math, science, engineering, business, finance and humanities.

The aim of this course is hence to take you from having no prior experience of thinking in a computational manner to a point where you can derive simple algorithms and code the programs to solve some basic problems in mathematics and science in general. In addition, the course will include topics to appreciate the internal operations of a processor, and raise awareness of the socio-ethical issues arising from the pervasiveness of computing technology.

Intended Learning Outcomes

Upon successfully completing this course, you should be able to:

  1. Describe the internal operation of a basic processor, how a program is executed by a computer and computing trends.
  2. Code basic programs based on Python programming language.
  3. Formulate a problem and express its solution in such a way that a computer can effectively carry it out.
  4. Apply the CT concepts to case studies/problem-based scenarios.

Course Content

Computational Thinking Concepts, Programming languages

Basic internal operation of computer

Basic program structure: Case Study, Pseudo code and flowchart

Basic program structure: Data type, Variable, sequence, logic and comparison operation

Basic program structure: Selection and Iteration

Procedural abstraction: function and library

Data abstraction: Data structure

Decomposition Case study

Pattern recognition Case study

Algorithms Sorting algorithm

Algorithm design Searching algorithm

Algorithm Complexity Analysis Big-0 concept

Computing trends and Ethical considerations

Assessment

Component Course ILOs tested SPMS-MAS Graduate Attributes tested Weighting Team / Individual Assessment Rubrics
Continuous Assessment
Seminar
Project 2, 3, 4 2. a, b, c
3. a, b
30 both See Appendix for rubric
Technology-enhanced Learning
TEL participations and TEL MCQs 1, 2, 3, 4 1. a, b, c, d
20 individual See Appendix for rubric
Multiple Choice Questions 1 1, 2, 3, 4 1. a, b, c, d
4. a
5. a
20 individual See Appendix for rubric
Multiple Choice Questions 2 2, 3, 4 1. a, b, c, d
4. a
5. a
30 individual See Appendix for rubric
Total 100%

These are the relevant SPMS-MAS Graduate Attributes.

1. Competence

a. Independently process and interpret mathematical theories and methodologies, and apply them to solve problems

b. Formulate mathematical statements precisely using rigorous mathematical language

c. Discover patterns by abstraction from examples

d. Use computer technology to solve problems, and to communicate mathematical ideas

2. Creativity

a. Critically assess the applicability of mathematical tools in the workplace

b. Build on the connection between subfields of mathematics to tackle new problems

c. Develop new applications of existing techniques

3. Communication

a. Present mathematics ideas logically and coherently at the appropriate level for the intended audience

b. Work in teams on complicated projects that require applications of mathematics, and communicate the results verbally and in written form

4. Civic-mindedness

a. Develop and communicate mathematical ideas and concepts relevant in everyday life for the benefits of society

5. Character

a. Act in socially responsible and ethical ways in line with the societal expectations of a mathematics professional, particularly in relation to analysis of data, computer security, numerical computations and algorithms

Formative Feedback

For online tasks, immediately after you submitted the answers, you will see your scores, your answers, the correct answers, feedback on your incorrect answers, and explanations for the correct answers. For online quizzes and laboratory quizzes, individual feedback will be provided to you through evaluation of your submissions. Quiz answers will be discussed in the example class. You will also see the average scores of the other students in the same cohort.

For project assessment, you will be given verbal feedbacks during your demonstrations of the program.

Learning and Teaching Approach

Tutorials
(39 hours)

The Example class will be used as seminar sessions for you to clarify the contents of the online topic, as tutorial sessions to work on algorithm exercises, as well as hands-on sessions to equip you with practical knowledge on coding, and on the design and implementation of a mini project to achieve LO 1 to LO 4.

Technology-enhanced Learning
(13 hours)

Topics will be delivered as a series of online videos lectures, and you will also be provided reference reading materials for self-study to achieve LO 1 to LO 4

Reading and References

The course will not use any specific text book. The following books and websites will be used as reference materials.
1. The Practice of Computing using Python; William Punch and Richard Enbody, Pearson, 2017. ISBN: 9780134380315
2. Introduction to Computation and Programming Using Python : With Application to Understanding Data; (2nd Ed) John V. Guttag, MIT Press Ltd, 2016. ISBN: 0262529629
3. Learning Python (5th Ed), Mark Lutz, O’Reilly Media, 2013. ISBN: 1449355730
4. https://edu.google.com/resources/programs/exploring-computational-thinking/

Course Policies and Student Responsibilities

As a student of the course, you are required to abide by both the University Code of Conduct and the Student Code of Conduct. The Codes provide information on the responsibilities of all NTU students, as well as examples of misconduct and details about how you can report suspected misconduct. The university also has the Student Mental Health Policy. The Policy states the University’s commitment to providing a supportive environment for the holistic development of students, including the improvement of mental health and wellbeing. These policies and codes concerning students can be found in the following link.
http://www.ntu.edu.sg/SAO/Pages/Policies-concerning-students.aspx

Academic Integrity

Good academic work depends on honesty and ethical behaviour. The quality of your work as a student relies on adhering to the principles of academic integrity and to the NTU Honour Code, a set of values shared by the whole university community. Truth, Trust and Justice are at the core of NTU’s shared values.

As a student, it is important that you recognize your responsibilities in understanding and applying the principles of academic integrity in all the work you do at NTU. Not knowing what is involved in maintaining academic integrity does not excuse academic dishonesty. You need to actively equip yourself with strategies to avoid all forms of academic dishonesty, including plagiarism, academic fraud, collusion and cheating. If you are uncertain of the definitions of any of these terms, you should go to the Academic Integrity website for more information. Consult your instructor(s) if you need any clarification about the requirements of academic integrity in the course.

Course Instructors

Instructor Office Location Phone Email
Thomas Peyrin (Assoc Prof) SPMS-MAS-05-14 6513 2027 thomas.peyrin@ntu.edu.sg

Planned Weekly Schedule

Week Topic Course ILO Readings/ Activities
1

Computational Thinking Concepts, Programming languages

3, 4

On-line Video

Familiarization with Raspberry Pi (RPi) board, and Scratch Programming

2

Basic internal operation of computer

1

On-line Video

IDE for Python on RPi

3

Basic program structure: Case Study, Pseudo code and flowchart,

2

On-line Video

Python programming exercises

4

Basic program structure: Data type, Variable, sequence, logic and comparison operation

2

On-line Video

Python programming exercises

5

Basic program structure: Selection and Iteration

2

On-line Video

Python programming exercises

6

Procedural abstraction: function and library

2, 3

On-line Video

Python programming exercises (Function)

7

Data abstraction: Data structure

2, 3

On-line Video

Python programming exercises (Function)

8

Decomposition Case study

2, 3, 4

On-line Video

Python Programming
Exercises + Mini project –
Flow Chart Design

9

Pattern recognition Case study

2, 3, 4

On-line Video

Python Programming
Exercises + Mini project –
Coding

10

Algorithms Sorting algorithm

2, 3, 4

On-line Video

Python Programming
Exercises + Mini project –
Coding and debugging

11

Algorithm design Searching algorithm

2, 3, 4

On-line Video

Python Programming
Exercises + Mini project –
Coding and debugging

12

Algorithm Complexity Analysis Big-0 concept

2, 3, 4

On-line Video

Python Programming
Exercises + Mini project –
Testing and assessment

13

Computing trends and Ethical considerations

1

On-line Video

Mini project –
assessment

Appendix 1: Assessment Rubrics

Rubric for Seminar: Project (30%)

You will demonstrate 1 working program in the form of a mini project. The maximum score is 30.

Criteria

Standards

Fail standard

(0-39%)

Pass standard

(40-80 %)

High standard

(81-100 %)

Demonstrate (including explanation) the use of CT concepts in the implementation of the project.

(LO 2,3,4)

Demonstrated less than 40% of the functionalities according to the specifications.

Demonstrated 40% to 80% of the functionalities according to the specifications.

Demonstrated more than 80% of the functionalities according to the specifications.

Rubric for Technology-enhanced Learning: TEL participations and TEL MCQs (20%)

You will complete online MCQ directly testing your understanding of the course content

Rubric for Technology-enhanced Learning: Multiple Choice Questions 1 (20%)

You will complete 2 hands-on exercises assessments, based on MCQs quizzes (each will count 10% of the final grade). The maximum score is 30.

Rubric for Technology-enhanced Learning: Multiple Choice Questions 2 (30%)

You will complete 2 MCQs based quizzes on the course contents (each will count 15% of the final grade). The maximum score is 30.