Course Catalogue

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This course develops core academic and technical skills in Computing Science through a combination of technical and informal writing and presentation, programming, and structured group work. Programming activities focus on strengthening foundational skills in Python, while group-based work develops collaborative problem-solving, and group-based communication. Students engage with disciplinary writing to develop precise technical language and the ability to explain computing concepts clearly in written form.

The course includes regular formative individual programming exercises that reinforce core concepts such as control flow, data structures, debugging, and code comprehension, alongside assessments that test technical understanding and appropriate use of Computing Science terminology. Two summative in-class written examinations assesses students' ability to reason about code, algorithms, and computational problems under time constraints and communicate their ideas.

Students are taught how to communicate both in natural language to work with other students and staff and using academic language to present high-level computing concepts and write with appropriate evidence. This is assessed through credit bearing journalling exercises and presentations, reflecting on meetings with mentors, and a formative academic writing exercises and presentation in class on a range of topics on a contemporary computing science research problem.

A group project requires students to plan, implement, and reflect on a collaborative technical task, supporting the development of teamwork skills, version control awareness, and collective problem solving while also reenforcing the core Python skills developed. Together, these components provide structured practice in the technical, written, and collaborative work required for successful study in Computing Science.

2 hours lectures per week:

2 hours of labs per week (8 on python, 2 on academic writing)

2 hours of group work labs for 5 weeks (supervised)

1 hour of Tutorials per week with a mentor reflecting on academic progress

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Semester 1
E-portfolio video presentations and reflections on meeting mentors (portfolio):10%

■ ILO1,2

Written essay and in-person presentation, 1,500 words on an advanced computer science topic (essay): 20%

■ ILO3

Weekly Labs (set exercises) 20%

■ ILO4,5,6

2 In-class invigilated exams on Python (written exams) (10% then 20%) 30%

■ ILO5,6

Group project on Python project and video presentation (project output): 20%

■ ILO7,8

The primary aim of this course is to facilitate a smooth transition for students entering Year 2 of their computing science degree at university from Glasgow International College by addressing well understood discrepancies in skillsets and expectations. It seeks to:

■ Develop technical and disciplinary language skills relevant to Computing Science.

■ Strengthen students' group working and collaborative learning abilities.

■ Reinforce foundational programming skills, particularly in Python.

The delivery of these learning outcomes within a credit-bearing structure is expected to significantly improve engagement, participation, and overall effectiveness.

By the end of this course students will be able to: 

1. Demonstrate a clear understanding of the academic expectations and requirements in computing science at a university level.

2. Utilise mentoring and reflective practice to identify areas for academic and personal growth within the university environment.

3. Identify English-language academic sources of information, critically read them and combine sources to produce effective, evidenced written and presented academic arguments.

4. Design, implement, and evaluate Python programs using fundamental programming constructs, including functions, iteration, recursion, types, strings, files, sequences, maps, sets, sorting, searching, exceptions, classes and arrays;

5. Recognise and apply procedural, functional, event-driven, and object-oriented programming to solve a given problem;

6. Identify, select, and apply appropriate techniques, libraries, and algorithms to solve a given problem.

7. Communicate their understanding of a problem and solution approach in English clearly, design top-level plans for a problem, and translate these plans into a working program.

8. Effectively collaborate in computer science group projects, communicating technical requirements and coordinate software development with teammates to produce functional Python projects.