Undergraduate Programme and Module Handbook 2013-2014 (archived)
Module COMP1051: COMPUTATIONAL THINKING
Department: Computer Science
COMP1051: COMPUTATIONAL THINKING
| Type | Open | Level | 1 | Credits | 20 | Availability | Available in 2013/14 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce students to fundamental concepts from Computer Science.
- To give an awareness of the importance of computation and computational thinking in the modern world and the impact it has on areas not immediately associated with Computer Science.
- To introduce students to the application of computational thinking in a wide range of settings.
Content
- The nature of computational problems.
- Solution methods for computational problems and the notion of an algorithm.
- The notion of a computer and its formalism.
- The measurement and efficiency of solutions, and intrinsic complexity barriers.
- Examples of computational thinking in a variety of settings.
Learning Outcomes
Subject-specific Knowledge:
- On completion of the module, students will be able to demonstrate:
- an understanding of the fundamental notions relating to problems and their solution in Computer Science
- an appreciation of the role of Computer Science and computational thinking in the modern world
- an understanding of several approaches to solving computational problems.
Subject-specific Skills:
- On completion of the module, students will be able to demonstrate:
- an ability to recognise and analyse computational problems in a variety of settings
- an ability to apply methods and techniques relating to algorithms and computation in order to solve problems
- an ability to reason about the quality of a solution or an algorithm.
- an ability to construct basic algorithms in a general-purpose high-level programming language.
Key Skills:
- On completion of the module, students will be able to demonstrate:
- an ability to reason about the solution of general problems.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable the students to learn new material relevant to computational thinking.
- Practical classes enable the students to put into practice learning from lectures and strengthen their understanding through application (by implementing and applying algorithms in a general-purpose, high-level programming language).
- Students are assessed by formative and summative assessment and examinations.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| lectures | 44 | 2 per week | 1 hour | 44 | |
| praxctical classes | 22 | 1 per week | 2 hours | 44 | |
| preparation and reading | 112 | ||||
| total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 50% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | Yes |
| Component: Coursework | Component Weighting: 50% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Coursework | 100% | Yes | |
Formative Assessment:
Examples and exercises are given throughout the course, to be undertaken and then discussed in practical sessions. Additional revison lectures may be arranged in the module's lecture slots in the 3rd term.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University