As we learn more about the workings of the brain there is increasing discussion about what neuroscience might be able to do to enhance education. The crux of the justification for the emerging field of educational neuroscience is that knowledge about how the brain learns can have a direct impact on teaching practice. This impact would be similar to the influence fundamental biomedical sciences and randomised control trials have had on medicine.
The consequences of this argument are many but amongst them are that teachers should have a working understanding of the brain and that the growing body of knowledge about basic neurological processes can ‘fix’ education by eliminating fads and myths. In other words, the implication would appear to be that scientific rigour should be privileged over the complexities of the context in which teaching occurs.
As has been discussed in articles in The Conversation by Max Coltheart, and my colleague Jared Horvath, the reality of using neuroscience to enhance education is a little more complicated than that.
Molecular gastronomy as a better model
Part of the problem with educational neuroscience is that medicine perhaps does not provide the best model for translation of neuroscience for enhancing education. A better analogy might be that of cookery (perhaps somewhat conveniently as I am a former chef).
Like teaching practices, cuisines and cooking techniques have existed loosely for millennia and more formally for centuries. The art of both have developed within complex social, political and cultural settings and through extensive theorising and trial and error. It takes skill in both cases to take the raw components, be they students or ingredients, and make the most of them in the unique context in which the practice occurs.
The seeds of change in cookery sprouted about fifty years ago when a small group of chemists and chefs started to take food science seriously as a method for rigorously testing established cooking practices. Sometime later, the molecular gastronomy movement was born and has become possibly the most striking example of the fusion of art and science.
It is not necessary for every chef to also be an expert in chemistry. The work being done in laboratory-like test kitchens by people such as chemist Hervé This and chef Heston Blumenthal is having a profound influence on the practice of cooking globally. Enhanced cooking practices filter down from test kitchens to every kitchen.
For example, the cook at the local fish and chips shop doesn’t need to be a chemist but they do know exactly what temperature the frying oil needs to be at and what consistency the batter needs to be for the best tasting result. The increasing impact of scientific evidence on traditional cookery is largely responsible for cooking techniques being significantly refined over the last few decades.
However, food science has not fundamentally changed established cuisines or taken away from the artistic flair required to be a good chef.
Similarly, the introduction of neuroscience could impact the education through careful translation to teaching practice. Neuroscientists are not required in classrooms to tell teachers that they are doing it all wrong and teachers need not become proficient in the workings of the brain. There are multiple layers of interpretation required as the widely used phrasing ‘from neuron to neighbourhood’ suggests. Cognitive scientists, psychologists and educational researchers are all important in the translation process.
The aim should instead be to take what is already known about the art of good teaching practice and work collaboratively towards using evidence to refine existing approaches.
As I’ve discussed previously, this collaboration is unlikely to produce prescriptive recipes for teachers. There is, however, potential for creating enhanced tools and approaches such as flexible lesson templates that teachers can then expertly adapt for use their specific context.
What path forward?
The main difficulty with multidisciplinary integration is that each researcher brings with them their own ways of seeing the world and conducting research based on their disciplinary background. These differences are difficult to reconcile and make translation of research to practice problematic due to a lack of shared understandings and confused terminology. For example, feedback means something very different to a neuroscientist than it does to a teacher or educational researcher.
These difficulties mean that simplified translation models adapted from medicine cannot be implemented uncritically. A more nuanced conversation needs to evolve about what neuroscience might be able to do for education and vice versa. The discussion should happen both ways as it has been between chemists and chefs.
Enhancing education through the use of evidence is also about what expert teaching practitioners and new sources of data about learning ‘in the wild’ might be able to contribute to the investigation of learning at biological and cognitive levels. For example, large scale collection and integration of data about student learning known as learning analytics is providing an increasingly sophisticated view of how students are learning with technology in the 21st century. What is gleaned from these real world data is useful for generating research questions to be tested in the laboratory.
As has been argued elsewhere by Peter Goodyear from the University of Sydney, to ignore the complexities and art of teaching practice and the work already being done in established fields such as the learning sciences is at the peril of anyone seeking to use neuroscience to enhance education. In other words, the aspiration should be to bring scientific rigour and the relevance of the context together, rather than privilege one over the other.
There remains some conjecture about the role of cognition in a comprehensive understanding of knowledge acquisition in educational settings. This is evident particularly when considering the development of expertise. An expert is defined as a person with an enhanced capacity to perform in a particular domain. The study of experts reveals much about the desired aims of knowledge acquisition and quality education (Feldon, 2007). The current essay is an attempt to compare and contrast two different discussions about the limitations of cognitive approaches to learning on the understanding of expertise development and the implications for instructional design. The aim of this comparison is to synthesise some lessons for knowledge acquisition towards accelerated development of expertise.
Expertise and the development of expertise have received increased attention over the last decade. This is exemplified by the popularity of Malcolm Gladwell’s pop psychology book Blink (Gladwell, 2005) and the publication of several edited books devoted to the academic study of expertise (e.g. Ericsson, 2009; Ericsson, Charness, Feltovich & Hoffman, 2006; Mosier, & Fischer, 2011). Within this context, two publications from the growing literature on expertise and, more broadly, on cognitive notions of learning will be discussed. These two papers will be compared and contrasted on several dimensions before presenting some possibilities for progressing the design of instruction for developing expertise.
The psychological underpinnings of expertise are the main topic of discussion in a book chapter by Feltovich, Prietula and Ericsson (2006). This chapter focuses on the different schools of psychological thought about expertise and the development required to attain high-level performance characteristic of an expert. The second article, forming the point of comparison in this essay, is that of Winn (2004). This article discusses the role of cognitive psychology in current understanding of learning in the context of educational technology. Although the article by Winn does not focus solely on the development of expertise, the implications of the main arguments made in this article provide substantial grounds for the ongoing discourse about theories of knowledge acquisition and educational practice associated with the development of expertise.
While the scope of the Feltovich et al. (2006) and Winn (2004) articles differs somewhat, it is in the areas of overlap in the discussion of expertise that some interesting comparisons can be made. The articles both discuss the paradigm shift in psychology from behaviourism to more cognitive-based approaches. This shift has been well documented (e.g. Sternberg, 2008) as alluded to in both papers and, according to Winn, is still yet to be completely realised in some instructional methods. Aside from the discussion of this transition, the interesting comparison comes from contrasting the implications of this for expertise as argued in each of the papers. Winn suggests that the combination of knowledge representation ideas such as schema, that are a mainstay of cognitive theories of knowledge, and legacy ideas from the behaviourist tradition potentially interfere with the evolution of effective instructional design. Feltovich and colleagues would seemingly disagree. They explicitly support the notion that behaviourism still has a place in understanding the development of expertise, particularly when considered in the context of the practical experience required to become an expert. Thus, even though the scope of these articles differs, there are some areas in which they directly speak to each other on the matter of expertise and thus warrant consideration.
A broader look at the tone of each article gives some suggestions as to why the views around the role of behaviourism in expertise differ and can shed light on where the divergent arguments come from. Winn (2004) more broadly argues that a disconnect has formed between psychological notions of learning and recent views about instructional design (e.g. Reigeluth, 1999). This is an idea that has also received attention in the educational research literature elsewhere (e.g. Haggis, 2009; Roediger, 2013). The implication of this disconnect according to Winn is that a hole has been left at the centre of research and understanding of knowledge acquisition. The assumption is that behaviourist notions should no longer impact on thinking about knowledge development, a notion that some would disagree with (e.g. Schachtman & Reilly, 2011). This is also at odds with the central thesis of the Feltovich et al. (2006) paper, which is about the critical role of cognitive science and cognitive psychology in understanding expertise and the development of expertise. Taken together this suggests two implications, firstly that the laboratory-based cognitive science research on expertise as discussed by Feltovich et al. has so far failed to make sufficient impact on theories of knowledge development post-cognitive revolution (see also Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013). This may explain the hole in the centre of conceptions of knowledge acquisition described by Winn. Secondly it suggests that research on expertise could have a significant impact on filling the hole. These two implications thus provide a thread to tie together the arguments made in the two articles in question.
In terms of the experimental research addressed, both Winn (2004) and Feltovich et al. (2006) discuss a range of research on experts in laboratory and controlled settings. In both cases, the discussion focuses in part on studies that examined people highly proficient at very specific tasks such as chess champions. Looking at the ways in which these experts go about these highly specific tasks has given researchers ideas about how experts process information and develop strategies (Chase & Simon, 1973). Winn situates this firmly within the realm of schema-based theories of knowledge whereas Feltovich and colleagues devoted more discussion to the specificity of the knowledge domain in which expertise develops and how such expertise does not translate very well beyond the domain. While on the surface these discussions may not appear to provide a lot of common ground for further elaboration, there is substantial overlap in terms of the notion of automaticity. Both papers devoted significant discussion to the tacit, implicit or automated side of expertise. Tacit and implicit knowledge have been considered within educational contexts and are important in the development of competent professionals (Horvath, 1999). The overlap between the apparent importance of tacit knowledge in education and the role of automaticity in expertise gives credence to the impact of laboratory research on automaticity for both informing the ongoing development of knowledge acquisition theories and instructional design strategies. Automaticity has received much attention as part of the ongoing understanding of expertise (Clark, 2008) and therefore deserves a central role in any understanding about what a subject matter expert is and how to educate them.
That both papers include discussion of the importance of automated processes is not coincidental in this context. In the broader literature on automaticity, much of the theoretical discussion has focussed on an area that is not discussed at any length in either article. Dual processing theories (e.g. Evans, 2008) suggest that there are two distinct ‘systems’ for thinking. The first of these systems is fast, intuitive and requires
little cognitive effort. The second is slower, more deliberate and is where the difficult and more taxing thinking tasks occur (see Kahneman, 2011). Winn (2004) does address information processing as important in our psychological understandings of knowledge acquisition and both papers discuss the important role of converting ‘system 2’ thinking to ‘system 1’ thinking (although neither explicitly conceptualised these ‘systems’ this way). Expertise in relation to dual processing is a common theme in the more recent literature (e.g. Kahneman & Klein, 2009; Kinchin & Cabot, 2010). Helping students to develop the tacit knowledge required for expert performance is thus an implied aim of higher education in particular (see also Barnett, 2009) but, as suggested by both Winn and Feltovich et al. (2006), it is a difficult prospect. What can be taken away from this is that although extensive laboratory research on the two information processing systems hypothesised as part of the dual processing theory can help to provide models of how we develop knowledge and become experts but do not lend themselves easily to improved instructional strategies.
One area that does provide a possible way forward for the development of enhanced instructional strategies is discussed by Feltovich et al. (2006). Moving away from explaining and describing the nature of expertise, they focus in the latter part of their article on the role of reflection and metacognition in the development of expertise. Winn (2004) did not explicitly discuss either of these notions. It was interesting to note that these mental processes would appear to reside in exactly the knowledge domain Winn claims is beyond the computational models that have become prevalent in cognitive psychology. It would seem that the incorporation of reflection and metacognition would be a minimum requirement for the development of expertise in professional contexts and might provide a resolution to the instructional design issues raised by Winn. He argues that current conceptualisations of instructional design merely ‘provide training wheels’ for the development of expertise rather than specifically target this development as a learning outcome. It has been demonstrated that specifically developing reflection and metacognition is a basic prerequisite for lifelong learning (Hmelo-Silver, 2004). If the estimates of ten years (Chase & Simon, 1973) or 10,000 hours (Ericsson, Prietula, & Cokely, 2007) of practice to become an expert were even remotely accurate, this will require ongoing monitoring of progress in knowledge acquisition (Ertmer & Newby, 1996). Therefore it would follow that metacognition and reflection would also be required in order for sufficient ‘deliberate practice’ (c.f. Ericsson et al. 2007) to occur to become an expert at all.
A synthesis of the arguments made in the Winn (2004) and Feltovich et al. (2006) would thus lead to the conclusion that while laboratory experiments and cognitive notions of knowledge acquisition are useful in understanding expertise development, traditional approaches based on behaviourism and reflection still have a place in instructional design. Contrasting these two papers, it would appear that Winn poses some serious challenges for thinking in knowledge acquisition, both in relation to expertise and more broadly. Feltovich et al., on the other hand, would appear to be posing some ideas within the context of expertise, which when extended through dual processing theory as an example, may provide some solutions. Cognitive modelling of psychological processes, as with the behaviourist conceptions they superseded, have been useful in providing base knowledge for instructional design. As with the older conceptions, schemas and similar cognitive notions of knowledge acquisition have limits but remain useful. Drawing on both articles, we can see that not just older theories but both laboratory work on expertise development and practical appreciation of knowledge acquisition in areas like reflective practice (e.g. Schön, 1983) give us a more comprehensive understanding of learning and teaching with technology than any single model or set of observations from highly controlled studies can provide.
Although it is fair to point out that Winn (2004) was not explicitly discussing the development of expertise in his article, the criticisms of cognitive notions of learning are relevant to the examples provided by Feltovich et al. (2006). The study of expertise affords a reasonable proving ground for ongoing development of thinking about knowledge acquisition. The types of complexity in instructional design that Winn talks about are prominent when considering what is required to create expertise. This is an especially difficult prospect for higher education, in particular, to come to terms with. As massive open online courses (MOOCs) and other innovations impact on higher education, questions about whether or not students can develop real expertise in a knowledge domain without sufficient exposure to established subject matter experts will become more pressing (Lodge & Lewis, forthcoming). Given the complexity that is inherent in any learning and teaching situation (e.g. see Koedinger, Booth, & Klahr, 2013), it makes sense that we draw on the widest possible range of theories, practices and data to make informed decisions about all elements of the design of educational resources, courses and programs.
It would appear that there is something to Winn’s (2004) argument that cognitive conceptions of knowledge alone are limited in helping to support the development of instruction for creating the experts we will need in the future. Considering the nature of expertise in a broad way, as have Feltovich and colleagues (2006), provides some clues as to how we might progress. Metacognition and reflective practice are critical to the development of expertise despite not fitting neatly into cognitive or computational mental models nor easily observed in a laboratory or fMRI study. What will be critical in this evolution in our fundamental psychological understanding of knowledge acquisition will be an ongoing collaboration between researchers, educational designers, subject matter experts and teachers. Models and algorithms, no matter how good, are unlikely to exactly emulate the messy reality required to develop tomorrow’s experts and in this both Winn and Feltovich et al. would seem to be in agreement.
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