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COMP47340

Academic Year 2025/2026

Computational Thinking (Conversion) (COMP47340)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Claudette Pretorius
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This is a core module on the MSc. Computer Science (Conversion) and Higher Diploma in Computer Science programmes. The principles of computational thinking are presented through a combination of lectures, discussion sessions and workshops. Focus is on communicating concepts fundamental to modern Computer Science (e.g. algorithmic thinking, knowledge representation, logic and statistics, computability and complexity) by drawing on some “Big Ideas” and a variety of topical examples from everyday, real-world applications. You will complete a research report documenting your reflections and further research on how the elements discussed relate to computational thinking. You will research several of the topics in groups and present the results of this research at the workshops.

About this Module

Learning Outcomes:

This module will enable you to:

1. Appreciate the principles and technologies that underpin Apps and Websites we use everyday.
2. Explain the role of computational thinking in a number of different areas whcih underpin modern Computer Science.
3. Recognise and reflect on the power and limitations of computational processes.
4. Research and synthesise a range of "Big Ideas" in Computer Science and critically reflect on how they relate to current applications and state-of-the-art research within and beyond the field.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

4
Autonomous Student Learning

76
Lectures

10
Small Group

10
Total

100

Approaches to Teaching and Learning:
This module will be a combination of lectures, guest talks, discussion sessions with peer and group work and workshops with active task-based learning.

If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification. Where indicated, assignments will be designed to support responsible AI use and therefore the use of generative AI tools must be clearly documented. Misrepresentation of AI use will be considered a breach of academic integrity.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: A 2,000-word critical reflection on a topic covered by the seminars, completed with or without Generative AI, assessing understanding and critical engagement. Week 14 Graded Yes
70
 Yes
Participation in Learning Activities: Active participation in workshop and group presentation on selected topic Week 11, Week 12 Graded No
10
 No
Participation in Learning Activities: Active participation in group tasks, completing a joint report on outcome with individual reflection section during the scheduled session. Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 Graded No
20
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Detailed written feedback will be provided for the examination when the results are released. Oral group/class feedback will provided on the discussion session reports and the presentations.

Name Role
Mr Hrishikesh Dilip Mulay Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - Autumn: All Weeks Thurs 11:00 - 11:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 11:00 - 11:50
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