Subject: Transformative Learning

Dear Chris,

You raise an important issue, not only in design education but also in design theory. Significantly, you raise it by pointing to a core problem that most of the writing in this area avoids - the 'how' and 'why'.

You describe the phenomenon as designers having 'a truly changed way of thinking and a fundamental change in themselves'. Practitioners and educators in many fields also refer to this phenomenon. For example, mathematicians refer to the difference between being able to 'do' math and 'understanding' math. The same is found in engineering. I also came across the phenomena in Youth Work education where new practitioners often move quite suddenly from a state of trying to work out which of the many theories to apply in a given situation (often emotionally and physically quite difficult in streetwork) to being able to work within a variety of theory frames almost without cognitive effort. To give a name to the phenomena Marcia Salner (1986) in the field of Systems Education coined the beautiful term - 'epistemic shift'. The characteristics of an 'epistemic shift' are changes in the person that result in changes to their 'worldview' and consequent changes to their learning, critical analysis and agency.

You state that the problem is 'where and how to look to gain greater understanding'. Below is an appeal for clarity in avoiding the Research 101 error of assuming that correlatory theories are causal explanations.

There seem to me to be two sides to investigating transformational processes and the choice depends on the intended outcome. The first approach is aimed at gaining greater understanding of how transformations/epistemic shifts occur. This understanding has the potential to form a sound conceptual foundation for identifying and designing education programs to improve designers' competencies. The second approach is to look empirically at the situation and derive simple heuristic models connecting observed outcomes to factors that seem to cause transformations/ epistemic shifts in individuals.

This research/theory making double act is found in a wide variety of disciplines. The first approach is the search for causal explanations that will form the foundation for further theory making. The second approach is aimed at identifying correlatory relationships between variables that seem important in a specific situation. The purpose is to create a locally accurate model to support decision-making and action. The limits of the correlatory approach to theory making are that it doesn't have reliable predictive power except very close to situation about which one has data. There has been extensive debate about these theory-making issues in several professional disciplines, including those referred to in this email thread.

One example is the field of Environmental Research. In environmental research, practical situations involving theory making and modeling can involve a relatively large number of variables representing physical phenomena interacting in rather messy and often not very deterministic ways. In spite of the messiness, the behaviour is in the physical realm, and at a small enough scale can be accurately described by basic physical 'laws'. The problem is, how to make theory at bigger scales.

In research and theory making at these bigger scales are found the two approaches described above. The first approach (causal) is to build and test increasingly complex models using what is already well understood about simpler physical phenomena. The aim is to build theory that explains how and why something happens in terms of the primary factors that have direct effects. At the end of the day, these models are well grounded on theories about physical relationships. Experience shows they are powerfully predictive even at significant distance from the data.

The second approach, (correlation-based) relies on a raft of generic techniques to identify 'quick and dirty' relationship models that statistical analysis indicates closely represent the data. These general theory techniques include e.g. applying simple straight-line relationships between variables, modeling situations in terms of generic Fourier or Taylor series (of which most simple models are a subset), and Time series models. A key aspect this second approach is the theory model does not and cannot describe why or how something happens. It is limited to providing a heuristic indication of the likely value of individual variables, given the values of sufficient other variables.

The purpose of the two approaches is different. The first approach (causal) is aimed at providing understanding and knowledge that can either be used in accurate prediction in situations distant from the data, or for building higher-level theory. The purpose of the second approach (correlatory) is to provide a simplified model that heuristically supports thinking and decision making about the immediate situation, or has an exploratory research role in identifying concepts for undertaking the first approach. Correlation-based models are much weaker than causal models based directly on the physical understanding. This is widely accepted in environmental research centres where those that build theory on physical understanding rather than correlations are more highly regarded. An important issue is that correlatory models are not required to be similar to or provide causal explanations or theories. Thus, it is not possible to infer how or why something happens from correlation-based theories and models. I feel this distinction between 'causal' and 'correlation' theory making is important in the context of your original posting on 'Transformative Learning'.

Going back to the important issue you raise 'How does transformative learning occur'? There is a lot of literature that apparently addressed this and answers this question as other researchers have indicated. The problem is that the literature from Dewey to Friere, from Schön to Lave and Wenger, from Newell to Berger and Luckman consist of proposals for theories that are essentially based on correlatory evidence with all the conceptual weaknesses, and lack of prediction and generality. This means that this literature offers little in building sound explanatory theory. None of the proposals I have read explains, in any epistemologically satisfactory manner, HOW transformative learning occurs (or for that matter explains any other human activity such as designing that depends on affective cognition and motor processes).

Epistemologically, it appears that any satisfactory explanation of how transformative learning or design occurs can only be derived from analyses whose foci are the processes by which humans do these things. These are human internal processes that are not singularly represented by external phenomena such as behaviour and emotions, or by subjective conscious experiences.

Theories that attempt to infer or model the processes going on inside a human by observing behaviour, interpreting subjective experiences or extending theories about e.g. objects, culture, knowledge, or emotion are on epistemologically very weak ground. There are many analogies: 'trying to understand the mechanical, kinematic and thermodynamic processes in a car engine by pressing the brake pedal'; or 'trying to identify the exact electronic circuits inside a calculator by observing the numbers on its display'; or 'trying to infer the software code from a word processor program by reading documents produced by an author using the program'. The situation is ridiculous - yet the literature is extensive. In each case, there are a potentially infinite number of correlation-based theories that can align with the data. Deriving causal theories that explain HOW and WHY the phenomena happen require a different sort of data and a different focus (the actuality and physicality of human internal activities).

A reasonable question is to ask what are the forces that have led to this extensive correlatory literature that problematically attempts to infer internal human processes on the basis of external observations of behaviour or from subjective reports of experience? I suspect the answer is simply that any insight into human internal processes is valuable and, until recently, there has been no other way. Until recently, regardless of its lack of validity it is the best we have been able to do. In practical terms, however, it has also provided heuristics (unfortunately also with misleading causal explanation) that have proven to be successful for individual managers, consultants and educators in a limited range of circumstances.

The situation is changing rapidly. Over the last decade there is increased insight into human internal feeling and thinking processes that are directly based on the physicality of what happens inside humans. These new insights offer direct information about how and why humans think and act in particular ways. . I'm suddenly aware it is important at this stage to distinguish between biological representation and biological determinism. These new insights about the human physiological processes that are the 'how' and 'why' of individuals' feeling, thinking, creativity, action, agency and emotions are biological but not deterministic (in a similar way that understanding how a car functions does not define where it is driven to except in a very general way - like cars don't usually go over water or fly)

An example, of the utility of these new causal approaches is in the area Paul Murty in an earlier post pointed to with a question about whether creative thinking is different from ordinary thinking. Research into the neuro-physio-psychological aspects of human functioning is the area from which this question can be addressed directly. It contrasts with indirect approaches where trying to infer an answer from Gestalt, insight theory, creative theory, or the properties of designed objects is simply using theory frames that are epistemologically inappropriate - category confusion.

There is already an extensive body of knowledge and research findings in relation to causal explanations of human processes (Cerebral Cortex is a good source). The time is ripe for including this material into design theory and design research. Two books that offer accessible models of how this might be done are: Damasio, A. (1994). Descartes' Error: Emotion, Reason and the Human Brain. New York: Grosset, and Damasio, A. (1999). The Feeling of What Happens. The earlier book retains echoes of the rationalist tradition. The second makes an increased break from that tradition and, for me points to many new areas of research and theory making for design researchers and design educators. Neither is technically difficult reading. Any difficulty is because the issues are complex because the ways human feelings shape human behaviour are complex.

To conclude, I make an appeal to research good sense in three areas:
 
· To a differentiation between causal and correlatory theories associated with designing, with an awareness of the substantial weaknesses of correlatory theories
· To an awareness that most of the literature on learning, cognition, creativity and behaviour is correlatory in fields such as Education, Management, Sociology, Psychology and Design (with all the research weaknesses that implies)
· That there are new causal explanations from new fields that offer benfits for design theory making and design education. Causal explanations from research into the neurology and physiology of humans' internal thinking, feeling, creative, decision making and motivational activities are replacing or clarifying much of the correlatory theory in the traditional domains listed above.


Finally, the two approaches (correlatory and causal) echo the different states on either side of an epistemic/ transformational shift. Perhaps, it is time for design researchers to make the transformation.

Best wishes,

Terry

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Dr. Terence Love
Curtin University
Perth, WA

Home office
PO Box 226
Quinns Rocks
Western Australia 6030
Tel/Fax +61 (0)8 9305 7629
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References:

Damasio, A. (1994). Descartes' Error: Emotion, Reason and the Human Brain. New York: Grosset.
Damasio, A. (1999). The Feeling of What Happens. London: Random House.
Salnar, M. (1986). Cognitive and Epistemological Development in Systems Education. Systems Research, 3(4), 225-232.

(c) Copyright Terence Love 2003. Free to use according to the Australian Copyright Agency Ltd conditions of fair use for criticism and review.