Research approach

New ways of looking at problem-solving

The increasing complexity in 21st century industries means engineering practitioners at ALL levels are expected to cope with a far broader range of challenges when solving problems.

One of the analytical tools used on this research project is based on the Legitimation Code Theory concept of 'Epistemic Relations' (Maton, 2014). Quite simply, it shows the relationship between the 'what' and the 'how' of the problem being addressed. 

When the 'what' is a 'strongly bounded' phenomenon, this means it is understood by all as having a defined set of properties. A 'weakly bounded' phenomenon would require more contextual information or is more open to interpretation.

When the 'how' of the problem is fairly fixed - in other words, there are set ways to address or refer to the problem - this means it has stronger 'discursive relations'. Weaker 'discursive relations' means there are more ways or approaches to the problem. There are more choices, and these are not necessarily dependent on the rules of 'science' and its accepted methods.

These principles were translated into a useful and accessible schematic called the '5P model'*.

The 5P model was used as a basis to 'map' how different practitioners solved controlled electro-mechanical problems in different industrial contexts, under different conditions. Some of the key findings indicated that the environment plays a significant role in determining how practitioners solve engineering problems. Successful practitioners navigate the 'plane' in an iterative and responsive fashion: they recognise what kind of thinking is needed at different stages of the problem solving process.

Linking theory and practice in engineering education

Successful problem-solving practitioners are able to recognise the 'why' behind a particular problem ('what'), and select appropriate methods ('how') to address the problem. These include methods to address features of the problem-solving environments - the problem-solver him/herself, people, processes, principles and possibilities.

Understanding how successful engineering practitioners navigate a problem-solving process can help educators to develop better theory-practice linking strategies. Using another LCT tool, the following graphic captures the challenge for lecturers in enabling 'cumulative learning':

Theory-practice and complexity

Linking Engineering Problem-Solving Research to Education

Building on these instruments to demonstrate theory-practice linking, I have combined multiple educational principles into an overarching framework through which to address holistic education. The Cognitive-Affective-Systemic (CAS) model combines Tait (2000), Barnett (2000), and Bloom (1956), who all describe the three-fold 'head-heart-hand' dimensions of student support, curriculum design and learning objectives. The CAS model, together with the LCT tools have been invaluable in working with engineering educators to better prepare our future graduates for the development of competencies required in our increasingly complex world today.

For more detailed information on the study findings, see Research Outputs

References

Barnett, R. (2000). University knowledge in an age of supercomplexity. Higher education, 40, 409-422. 

Bloom, B. & Krathwohl, D. (1956). Taxonomy of educational objectives: The classification of educational goals, by a committee of college and university examiners. Handbook 1: Cognitive domain. New York , Longmans. 

Maton, K. (2009). Cumulative and segmented learning: exploring the role of curriculum structures in knowledge building. British Journal of Sociology of Education, 30(1), 43-57

Maton, K. (2014). Knowledge and Knowers: Towards a realist sociology of education. London and New York: Routledge.

Tait, A. (2000). Planning student support for open and distance learning. Open learning: The Journal of open, distance and e-learning, 15(3), 287-299.

See the LCT website for further papers using Legitimation Code Theory