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. Computational mathematics. Basic data mining.

1. Exemplify and distinguish between explicit and tacit knowledge and knowledge conversion processes.
2. Appraise and use human- and machine-centric knowledge generation techniques.
3. Choose and apply knowledge codification techniques.
4. Distinguish between and recognize the challenges of knowledge sharing and organizational learning methods.
5. Discuss how to measure the impacts of knowledge management activities on an organisation.
6. Examine and illustrate various human and machine collaboration models.

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 the uploaded online materials.

    Please see learning outcomes. 

The module will be assessed using an open book online class test.

Students will be provided with reassessment opportunities in line with University regulations.

Explicit and tacit knowledge. Nonaka’s knowledge conversion model. Knowledge maps. Information acquisition. Data mining and machine learning techniques. Microdata. Semantic Web. Knowledge impact metrics. Organizational learning. Knowledge sharing. Human-machine collaboration models.