CM3107: Knowledge Management
| School | Cardiff School of Computer Science and Informatics |
| Department Code | COMSC |
| Module Code | CM3107 |
| External Subject Code | 100963 |
| Number of Credits | 20 |
| Level | L6 |
| Language of Delivery | English |
| Module Leader | Dr Sylwia Polberg-Riener |
| Semester | Autumn Semester |
| Academic Year | 2025/6 |
Outline Description of Module
This module looks at how individuals and organisations generate, capture, transfer and leverage knowledge. We examine how the application of knowledge can make a significant difference to the success or failure of an enterprise, whether it be a business, service, or community. The course covers key questions such as, how can different kinds of knowledge be transferred? How can knowledge be generated by people and by machines? What kinds of codification techniques one can use to capture knowledge, and how these can impact knowledge sharing? What kinds of models are out there to capture collaboration between humans and machines?
This module requires a basic understanding of:
Web technologies. Data mining techniques.
On completion of the module a student should be able to
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Exemplify and distinguish between explicit and tacit knowledge and knowledge conversion processes.
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Contrast different knowledge management lifecycle models.
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Appraise and use human- and machine-centric knowledge generation techniques.
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Choose and apply knowledge codification techniques.
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Distinguish between and recognize the challenges of knowledge sharing and organizational learning methods.
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Discuss how to measure the impacts of knowledge management activities on an organisation.
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Examine and illustrate various human and machine collaboration models.
How the module will be delivered
The module will be delivered through a combination of lectures, supervised lab sessions and tutorials as appropriate. You will be expected to attend all timetabled sessions and engage with online material.
Skills that will be practised and developed
Please see learning outcomes.
How the module will be assessed
The module will be assessed using a portfolio composed of a blend of mini online tests, written or oral assessments, activities, as well as mix of individual and group tasks.
Students will be provided with reassessment opportunities in line with University regulations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Portfolio | 100 | Knowledge Management Portfolio | N/A |
Syllabus content
Explicit and tacit knowledge. Nonaka’s knowledge conversion model. Knowledge maps. Information acquisition. Data mining and machine learning techniques. Microdata. Semantic Web. Data preprocessing techniques. Knowledge impact metrics. Organizational learning. Human-machine collaboration models.