Competencies, badges and authentic assessment; these approaches have been touted by some as the solution to issues associated with rising costs, cheating and the demand for more flexible learning. Are they really though?
A competency-based approach is what is commonly used in vocational education where students must demonstrate that they have a defined range of skills mapped against a set of predetermined criteria. Badges are micro-credentials that validate that students have met a specific learning outcome. These outcomes tend to be much smaller in scope than a formal qualification. Authentic assessment is assignments that resemble in some way real life work-related activities.
What all these approaches have in common is the assumption that a large body of knowledge and way of seeing the world can be reduced to focussed, skills-based modules of learning that align with the workplace.
A university education is not simply about the accumulation of a series of well defined packages of knowledge, nor is it just about learning how to function in a particular job by completing assessment tasks that simulate work students will do when they graduate.
These sorts of approaches have more in common with a trade apprenticeship than they do with the traditional aims of higher education. They also seem to have more in common with an industrial age approach to education than what is required for high-level knowledge work in the 21st century.
Ways of being
University learning is about changing who students are, to turn them into job ready graduates and into educated members of society who can analyse and synthesise knowledge in sophisticated ways. A university is supposed to not just change what students think and can do but to change who they are, to change their way of being in the world.
Before it is possible to undertake authentic tasks, there needs to be a base level of knowledge established. That knowledge needs to be situated and integrated with what students already know. It also needs to be synthesised into meta-level constructs that allow students to become self-sufficient learners, to transfer what they learn to novel settings and adapt to unforseen developments.
If a university degree is to be distilled into a set of competencies or micro-credentials, at what point are all the pieces put together? Does it remain up to students to figure out for themselves how to coalesce a series of modules into a systematic network of interrelated concepts and ideas?
By modularising higher education into smaller components that are supposedly authentic and badge-worthy risks not giving students an opportunity to develop the meta-level constructs required for them to adapt and transfer what they learn. In focussing more on the real world and authentic trees there is an inherent risk of developing a generation of graduates who can’t see the forest.
Remembering and applying
While competency-based authentic assessment and badges might help overcome some of the problems currently facing higher education, they are ultimately about demonstrating what students can do at a micro-level. By assessing what they can do, we make an inference about what they know.
Do they truly understand the broader meaning and context of what they are doing? Do they remember the core ideas and can they integrate and analyse concepts in order to function effectively as educated members of society?
Just because a student can provide evidence that they can do something, authentic to the workplace or not, it still provides little direct information about how well they understand the knowledge required to complete the task. More importantly, doing an authentic task provides no information about the student’s understanding of how that knowledge fits into the bigger picture.
Given the types of complex and unpredictable problems facing graduates in the years to come, being able to prove that they have met a series of loosely connected competencies isn’t going to be enough. They need a clear understanding of the forest and the trees.
Job ready or life ready?
The problem here is really about the balance between core knowledge and competencies required to function as competent professionals, scientists, scholars etc. and what is required for a career and a life contributing to the greater good.
At some point all the pieces need to be pulled together, not for the purpose of getting a job but for the purpose of thinking creatively and critically, of addressing old and new problems in different ways.
It has been argued many times that too strong a focus on employability and job prospects risks turning higher education into vocational education. Competencies, badges and authentic assessment could all be labelled as moves in that direction. What is not clear is what kinds of authentic tasks and competencies are required in the mid 2060s. That is after all when school leavers entering university in 2015 will still be in the workforce.
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.
Barnett, R. (2009). Knowing and becoming in the higher education curriculum. Studies in Higher Education, 34(4), 429-440.
Chase, W. G. & Simon, H. A. (1973). The mind’s eye in chess. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press.
Clark, R. C. (2008). Building expertise: Cognitive methods for training and performance improvement. San Francisco, California: John Wiley & Sons.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58.
Ericsson, K. A. (2009). Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments. Cambridge: Cambridge University Press.
Ericsson, K. A., Charness, M., Feltovich, P. J., & Hoffman, R. R. (2006), The Cambridge handbook of expertise and expert performance. New York: Cambridge University Press.
Ericsson, K. A., Prietula, M. J., & Cokely, E. T. (2007). The making of an expert. Harvard Business Review, 85(7/8), 114.
Ertmer, P. A., & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24(1), 1-24.
Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255-278.
Feldon, D. F. (2007). The implications of research on expertise for curriculum and pedagogy. Educational Psychology Review, 19(2), 91-110.
Feltovich, P.J., Prietula, M.J., & Ericsson, K.A. (2006). Studies of expertise from psychological perspectives. In K. A. Ericsson, M. Charness, P. J. Feltovich & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance. New York: Cambridge University Press.
Gladwell, G. (2005). Blink: The power of thinking without thinking. New York: Little, Brown and Company.
Haggis, T. (2009). What have we been thinking of? A critical overview of 40 years of student learning research in higher education. Studies in Higher Education, 34(4), 377–390. doi:10.1080/03075070902771903
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235-266.
Horvath, J. A. (1999). Tacit knowledge in professional practice: Researcher and practitioner perspectives. New York: Psychology Press.
Kahneman, D. (2011). Thinking, fast and slow. New York: Macmillan.
Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 80, 237–251.
Kinchin, I. M., & Cabot, L. B. (2010). Reconsidering the dimensions of expertise: from linear stages towards dual processing. London Review of Education, 8(2), 153- 166.
Koedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science, 342, 935–937.
Lodge, J. M. & Lewis, M. J. (forthcoming). Professional education, knowledge, inquiry and expertise in MOOCs. In L. McKay & J. Lenarcic (Eds.) Macro-level learning through Massive Open Online Courses (MOOCs): Strategies and predictions for the future. IGI Global.
Mosier, K. L. & Fischer, U. M. (2011). Informed by knowledge: Expert performance in complex situations. New York: Psychology Press.
Reigeluth, C. M. (1999). Instructional design theories and models: A new paradigm of instructional theory. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
Roediger, H. L. (2013). Applying cognitive psychology to education translational educational science. Psychological Science in the Public Interest, 14(1), 1-3.
Schachtman, T. R., & Reilly, S. (2011). Things you always wanted to know about conditioning but were afraid to ask. In T. R. Schachtman & S. Reilly (Eds.) Associative learning and conditioning theory: Human and non-human applications. Oxford, UK: Oxford University Press.
Schön, D. A. (1983). The reflective practitioner: How professionals think in action (Vol. 5126). New York: Basic books.
Sternberg, R. (2008). Cognitive psychology. Belmont, California: Cengage Learning.
Winn, W. (2004). Cognitive perspective in psychology. In D. H. Jonassen (Ed.), Handbook of research on educational communication and technology (2nd ed.). Mahwah, NJ: Lawrence Erlbaum