Navigating the complexity of education in universities – arguing for holiploigy

Introduction

In a number of previous posts I’ve tried to set out a loose framework for understanding how we might conceptualise the process of teaching, learning, etc in higher education. These posts were based on the idea that to argue for a discussion about ‘teaching and learning’ such as that in Scholarship of Teaching and Learning leads to a conceptual narrowing of the task at hand. Instead I proposed a simple diagram to outline a complex process:

ped2I argued that we should move away from ‘teaching and learning’, and back to a reformed notion of ‘pedagogy’ (1) which takes into account assessment (2),  curriculum (3) (4), learning (5) (6) and teaching (7). As such I was calling this a form of ‘complex pedagogy’ due to the idea that each of these processes was, in their own right, complex, with their interpenetration making them all the more complex. I still think that this premise is correct for work in higher education, but the use of ‘pedagogy’ still concerned me; I was quite rightly challenged by someone who argued that pedagogy, by definition, focuses on the education of children. So what are the alternatives?

If we think about the meaning of ‘pedagogy’ it is actually composed of ‘paidos’, male child in ancient greek, and ‘agogos’, meaning to lead, so pedagogy means to lead a child. Doesn’t seem quite the right conceptualisation for working with young adults in undergraduate and postgraduate environments.

Two other terms which are used to describe teaching situations are ‘andragogy’ and ‘heutagogy’. Andragogy, comes from ‘andras’ man, leading to the ideas of teaching adults, i.e. leading men, and heutagogy relating to self-determined, student-centred, or discovery learning. In all these cases there is the notion of people being led – even heutagogy still refers to this.

As a result of reflecting on these ideas, I have decided that we need to think differently about the relationships between teaching, learning, curriculum and assessment, and between lecturers and students together with the terms of the spaces (virtual and real) in which such activities and relationships take place.

Outlining holiploigy

The concept of ‘holiploigy’ attempts to capture two fundamental aspects of work in higher education. The ‘holi’ element relates to the idea that the process of higher education needs to be considered holistically, and as a series of interpenetrating complex adaptive systems. This philosophy acts at a number of scales, and across a series of ideas. Firstly, there is the idea of the complexity of knowledge and skills within a domain, and increasingly their links across domains (inter- and trans-disciplinarity). Secondly, as laid out above, it includes the idea of teaching, learning, assessment and curriculum being inextricably linked, and of a complex nature (with lecturers and students at the intersection of the four). However, around this is the complexity of learning environments and how these processes operate across them. Teaching, learning etc operate differently in a face-to-face context when compared to being online, and yet increasingly, such blends will occur within a single course. How are the complexities of this to be understood and navigated?

And this leads to the idea of ‘ploigy’, from ploigos – navigate. Agogos, as used in pedagogy, suggests a role for the lecturer as leader, being at the centre of the educative process. At higher education level, this should not be the case – all of the time. However, if we see the lecturer as merely a guide – we might begin to move towards a process of ‘learnification’ (Biesta, 2012) which is potentially damaging. Biesta (2015) suggests the need for the teacher to be more central to the process of teaching and learning, but in a way that offers an opening up rather than a narrow leading. Navigating can be thought of as a process which sometimes needs more direct action, especially when moving through complex, dangerous and difficult waters. But at other times, such navigation requires less direct intervention, and can allow for much greater freedom, whilst still being a journey with a purpose. In some cases a journey might allow for detours, extra investigations of interesting, new places, but all the time the crew and navigator are working together to chart a meaningful course. And all the time, the navigator is inculcating the crew into the art of navigation for themselves.

Therefore, over the next few posts, I’ll outline what I see as a conceptual framework for the idea of navigating the complexity of the educative process and the knowledge and skills which it is used to explore, the process or holiploigy.

 

Biesta, G.J.J. (2012) ‘Giving Teaching Back to Education: Responding to the Disappearance of the Teacher.’ Phenomenology & Practice, 6 (2), 35-49.  http://www.ul.ie/eps/sites/default/files/Biesta%202012.pdf

Biesta, G.J.J. (2015) ‘The Rediscovery of Teaching: On Robot Vacuum Cleaners, Non-Egological Education, and the Limits of the Hermeneutical Worldview’. Educational Philosophy and Theory http://bura.brunel.ac.uk/handle/2438/10587

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Heuristics – making sense of the complexity of pedagogy

Over the course of a number of posts I have laid out a notion of pedagogy as the interpenetration of a number of complex adaptive systems (see here for the foundation of this expanded definition). Conceptually, what sits at the centre of this characterisation of pedagogy is the idea of complexity. Seminar rooms and the activities which develop within them, and the associated activities beyond (study, reading, discussion, etc) are such that we are not able to capture them in their entirety at any point in time – there are always elements which are beyond our perception. This leads to the idea that to research and understand seminar rooms and pedagogy is akin to looking onshore from a boat on a foggy day.

fog

When we observe or reflect on pedagogic activity we are only seeing some of the elements present, and rely on extrapolation, much as we might use elements of the landscape as a basis for extrapolating and imagining the detail of the whole landscape.

So if pedagogy is the interpenetration of teaching, learning, curriculum, assessment centred on the tutor and students, how are we able to make sense of the inherent complexity in these environments?

When individuals first begin to teach, they often find the process of teaching confusing, stressful, and even, on occasion, disorientating. I would argue that this is because they are faced with the complexity of the task, with little in the way of structures for sense making. However, as they begin to understand the tasks of teaching, they begin to see patterns in the activity, how various elements work together; this leads new teachers to build heuristics. Heuristics are strategies which emerge from experience, emerging out of groups of similar experiences and in this way creating sense making in complex environments. Individuals often problem-solve by using their experience of similar past events, or knowledge they have gained from elsewhere but which appear to have problem solving potential in the current situation. This allows a level of ‘patterning’ to pedagogic work, and as a result, networks of complex relationships are ‘chunked’ to simplify and make sense of practice. This is a form of ‘complexity reduction’ (Biesta, 2010) where the system is understood in simpler terms through the use of personal frameworks of understanding. However, heuristics are prone to approximation and error. This can be the result of biases and systematic errors in the frameworks which have been developed. Therefore, tutors will tend to identify elements of practice which appear not to work well, particularly through the use of reflective practice. In this framing reflective practice is a process by which the errors or approximations in heuristics are identified and developed to improve the heuristic models by which we operate.

The opening up of practice to reflection and to research is a process of reintroducing complexity, of allowing the many interpenetrating systems to become open and explicit once again – it is the conscious reintroduction of complexity as a way of trying to understand practice and process more deeply. The reflection or research is then enfolded into new heuristics which develop and allow complexity reduction to be introduced to practice once more. In this way, cycles of heuristics are developed to make sense of the complexity of pedagogy through reduction, before opening up the same complexity again in an attempt to engage in activity to change practice towards new heuristics and better practice. In this way, new practice is developed through the generation of new insights and knowledge through action (pragmatism), but in the context of interpenetrating complex systems which go to make up pedagogy. Hence, this is a complex pragmatic view of pedagogy and its emergence. In addition, the nature of heuristics is such that theory merely becomes a way of characterising those heuristics. Hence, as we go through cycles of emergent change, practice and theory become different elements or perspectives in relation to modelling and sense making sense of complexity through heuristics. Finally, going back to the image of the coastline, the emergent understanding of complexity and its enfolding into heuristics over time mean that we can blow some of the fog away – over a career, with the development of wise judgement (Biesta, 2014), i.e. the fostering of ever better and more deeply understood heuristics in practice, we extrapolate less and see more. However, we must accept that patches of fog will always remain, the complexity of pedagogic activity is such that we will never reach a clear and full understanding of it.

 

References

Biesta, G. (2010) ‘Five theses on complexity reduction and its politics’ in D. Osberg & G. Biesta (eds) Complexity Theory and the Politics of Education. Amsterdam: Sense Publishers, 5-14.

Biesta, G.J.J. (2014) The Beautiful Risk of Education. Boulder, CO: Paradigm Publishers

 

What do we Mean by Pedagogy?Characterising learning (part 2)

‘One cannot begin to understand the true nature of human learning without embracing its interactional complexity.’ (Alexander et al, 2009: 176)

Alexander et al (2009) attempt to create a series of criteria which together characterise learning. From these principles, they then try to outline the dimensions of learning within which they argue the principles operate. One crucial part of their argument is the notion that no single theory of learning has created a fully adequate representation of learning. They identify nine foundational principles of learning:

1 Learning is change – this is inherent in learning, but has a number of possible characteristics, so can occur at group or social level as well as individual, and can be from the obvious to the imperceptible and can occur over a number of different time scales.

2 Learning is inevitable, essential and ubiquitous – to be alive is to learn as it is an inevitable process, and is also essential if we are to survive as individuals. We learn wherever we are, ‘the processes of learning are in operation whenever and wherever humans are situated.’ (178)

3 Learning can be resisted – Humans can resist learning, perhaps due to a lack of effort/interest, or from a fear of failure. If learning might lead to cognitive, social or cultural dissonance there can be also be a resistance to learning.

4 Learning may be disadvantageous – Learning can be a negative process, for example, learning how to disrupt the efforts of others, or how to cheat. Also, disadvantageous learning can take different forms, for example the learner might wish they hadn’t learned something (e.g. how veal is reared and produced), whilst in other cases, the learner might see their learning as positive even though it might have negative impacts (e.g. excessive use of social media).

5 Learning can be tacit and incidental – Much of our learning falls into the category, particularly outside of formal educational settings. This can include much of first language learning, especially in the early stages, and contextual learning.

6 Learning is framed by our humanness – learning is framed by our neurobiology, but varied between individuals leading to variation in our learning capabilities.

7 Learning is both process and product – As a process, learning is something which happens over time, whilst product is the durable change which occurs as a result of the process. Formal assessment and much research tends to focus on the product. ‘Indeed, much research in which the focus is only on learning as a product may oversimplify our conception of the learning process.’ (180) However, the same bias can occur if we only focus on the process.

8 Learning is different at different points in time – Change occurs over time, and learning is affected by where the learner is in the process. For example, the what and how of learning by young children is different to adults in part as the level or iteration and recursivity is different.

9 Learning is interactional – Learning is shaped by biological, social and cultural factors which interact in a dynamic environment. ‘Learners are influenced by, and at the same time push back, take from, change, control, and create the environment in which learning is situated.’ (180)

These principles interact to give the basis for learning, and show a complex mixture of biological, cognitive, social and cultural dynamics to the process and product of learning. However, in addition to these principles, Alexander et al argue that four dimensions of learning set the context for the principles, the ‘what’, ‘where’, ‘who’ and ‘when’ of learning.

The what of learning – Learning always focuses on something. It can be a simple focus, or a more complex one, and generally speaking as the what is developed over time, it becomes more complex. Take as an example, any academic subject, which becomes increasingly detailed, complex and abstract as expertise develops. The what includes both the process and product of learning and the increasing intricacy involved is defined as interactive complexification.

The where of learning – this is the ecological context of learning. It includes the physical, social and cultural contexts of learning and can be critical to the process of learning. In some cases, the where can lead to problems of learning becoming ‘contextually restricted

The who of learning – This covers the characteristics of the learner, the biological, cognitive, experiential and affective (including motivational) factors which are important in both the process and product of learning.

The when of learning – This emphasises the importance of time and flow of experiences in learning. Different elements of learning are most pertinently explained over different time frames, from the evolutionary to short term individual.

What the principles and dimensions of learning discussed by Alexander et al demonstrate is that learning is multi-faceted, and cannot be collapsed into a simple process, such as seeing it as synonymous with memory. As they state:

‘objects of learning become increasingly more complex and….the processes and products of learning mirror that growing complexity.’ (185-186)

This leads to the following definition of learning:

‘Learning is a multidimensional process that results in a relatively enduring change in a person or persons, and consequently how that person or persons will perceive the world and reciprocally respond to its affordances physically, psychologically, and socially. The process of learning has as its foundation the systemic, dynamic, and interactive relation between the nature of the learner and the object of the learning as ecologically situated in a given time and place as well as over time.’ (186)

Geary (2009) agrees that the what, where, who and when are important, but adds that there also needs to be the ‘why’. In the short term the why is important in relation to motivation. At a simple level, he distinguishes between the learning which takes/took place in traditional societies and cultures where the why is related to survival and reproduction. Therefore, much of learning was inherently practical and required for a quality of life. This has shifted rapidly in modern societies so that the why is increasingly related to a where, what, and when which is not in alignment with these basic needs. This shift and the complexity it brings with it needs to be considered in relation to enhancing motivation and focus on learning in educational contexts.

The characterisation of learning presented by Alexander et al, and augmented by Geary is one of complexity and interaction. They stress that these various elements should not be seen as ‘independent contributors’ to learning, in other words they cannot be isolated and measured separately – the whole is greater than the sum of the parts. They describe them as facets of an intricate and fluid system. I would take this further and state that their work very clearly demonstrates learning as a complex adaptive system, one of the four interpenetrating systems which make up the concept of pedagogy.

References

Alexander, P.A.; Schallert, D.L. & Reynolds, R.E. (2009) What is learning anyway? A topographical perspective considered. Educational Psychologist, 44:3, 176-192.

Geary, D.C. (2009) The why of learning. Educational Psychologist, 44:3, 198-201.

Designing a Complex Curriculum – The Case of Innovation and Reform 3 – emergent pedagogy and learning.

In my last post I outlined some of the main characteristics of complexity, and in particular, of complex adaptive systems (CAS). Hardman (2011) stresses that it is not a sustainable position to assert that something is a CAS without any particular evidence other than an impression of complexity. In the case of higher education pedagogy and contexts is seems reasonable to suggest that a characterisation as a CAS does work. Why? The argument here is based on the underlying characteristics of post-graduate pedagogic contexts. Taking Cilliers’ (1998) notion of a CAS the table below outlines an argument for seeing pedagogy in post-graduate study as indeed complex.

CAS element Reflection
  1. A large number of elements with many interactions
By considering the number of students, the technology they use and the multitude of spaces inhabited together with tutors and resources, it becomes clear that there are a large number of elements within a postgraduate seminar group. Any attempt to observe a seminar session demonstrates a large number of interactions which also extend spatially and temporally beyond the face-to-face ‘event’ as students continue to engage with learning in different ways and in different locations, both pre-and post-session.
  1. Non-linear interactions
The interactions which occur during the process of learning are not predictable and ‘linear’. Discussion and learning will not follow a strictly predetermined form or path. Different interactional media will occur both between participants and between them and the various resources, media and spaces they use. As a consequence, for any given individual, elements of work which are expected to have a ‘core’ role in learning may actually have little impact, whilst a brief informal chat may be crucial in opening up the understanding of the concept or area of knowledge. As such, the process of learning needs to be seen as non-linear.
  1. Interactions leading to feedback loops
As the students attempt to engage and learn there may be the emergence of positive and/or negative feedback loops. Discussion, for example, may lead a student to begin to make connections between elements of a sub-topic, and even between topics, leading to a positive feedback loop which brings rapid development of understanding as a consequence of synthetic insight. Alternatively, a resource or activity may actually confuse a student leading to a more general questioning of their understanding of the topic, in turn generating anxiety and a lack of learning. Predicting such fluctuations in the learning process are often not possible to predict bringing a level of uncertainty to the learning process.
  1. Interactions with the environment, making the identification of system boundaries difficult
The fluidity of student use of space within a typical postgraduate course leads to difficulties in deciding the unique characteristics and boundaries of particular systems. Due to the often intertwined nature of systems the nature and permeability of boundaries between each of them and the environment become blurred and hard to detect with any certainty. For example, in any given week, a student may engage with academic learning in a number of spaces, such as libraries, cafes, lecture theatres, seminar rooms and study-bedrooms within which they may make greater or lesser use of technology, reference to physical materials and/or discussion and completion of given planned activities. How a system within such fluid contexts is identified and characterised within this network of processes and where the environment begins is difficult to determine. In addition systems may be flexible both spatially and temporally as a result of this interplay of elements.
  1. Open to interactions with the environment
As suggested in the point above, the multisite nature of learning spaces and the flexibility in content development in postgraduate learning leads to constant interaction with the ‘environment’.
  1. System far from equilibrium, needing constant energy flow
As a system, teaching and learning requires constant energy input. In this case energy can be characterised as taking the form of information. If this energy flow is suppressed, or does not exist in the system, it would begin to break down, stagnating as a result of a lack of constant information input for use in learning. Energy can be found from within the system and in interaction with the environment but must be present as a flow to maintain the open nature of the system.
  1. Importance of history and past processes on the form of the present
The history of the system is important as past processes such as prior teaching activities, prior learning and experiences of individuals and the use of resources, etc, all play a part in informing and producing the current system, sometimes in unexpected and surprising ways.
  1. Each element acting only on local information rather than information of the whole system
The elements of the system, and particularly people, predominantly act on local information rather than through an understanding of the whole system at any point in time.

If learning and teaching systems are accepted as demonstrating the characteristics of a CAS, certain processes and features will be present. Firstly, the history of the system provides a foundation for the emergent form of the system. Reflection, experience, etc informs the learning and innovation of the present. A simple example of this is the impact that the differential prior learning of students has on interaction within the present and from here to the emergence of new knowledge, skills and conceptual understanding within and beyond the seminar room.

Secondly, Biesta (2010) considers the nature of complexity reduction within learning and teaching contexts which in simple terms is the differential impact of the imposition of various structures on the pedagogic process. For example, the imposition of a given curriculum or pedagogic approach within a formalised teaching and learning context is an example of the reduction of complexity as coherence becomes dominant over freedom, leading to a diminishing of emergence. Alternatively, an approach where students are left to make individual decisions concerning these important features may lead to freedom over coherence, which in its own right might be detrimental to learning. Consequentially, the degree of complexity reduction for any given context needs to be considered carefully so as to maximise the emergence of student learning. Sullivan (2010) considers the idea of emergent learning in relation to three small scale case studies and emphasises that the level of complexity within each context was dependent upon the degree to which the teacher controlled or encouraged independent approaches to learning.

Where complexity reduction is excessive. It may have a negative impact as there is a tendency to simplify through an exclusive focus on ‘knowledge’ and the use of single pedagogic approaches regardless of appropriacy to stated intentions, etc. However, complexity reduction can also be positive as all postgraduate courses require some form of structure, the use of supporting online materials which focus on stated aims, questioning frameworks and timetabled sessions. However, these are only positive if they do not destroy the complexity, instead making energy transfer into the system structured and efficient. In this way single agents may be aided in moving away from potential chaos (i.e. a surfeit of unstructured information) and into more productive states of engagement. Such teaching and learning becomes centred to a degree on creating environments and systems which allow enough flexibility to steer away from stagnation whilst not allowing for unstructured, overwhelming and therefore chaotic exposure to information.

Within post-graduate contexts, teaching and learning occur across a number of spaces, both formal and informal but also in individual and group situations, and within virtual, physical, personal, social and academic spaces. With such a variety of contexts and free access to large volumes of information descent into chaos is a distinct possibility. As the interactions occur within the system emergence can arise under certain circumstances. Emergence leads to features which are more than the sum total of elements and processes leading to their creation. Davis and Samarra (2006) argue that such emergence occurs as a result of the interaction of three tensional dyads:

  • decentralised control and neighbour interactions: learning is developed in the interaction between the personal and social. Individual and collective interests should be mutually supportive rather than inherently competitive and it is the interaction between neighbours which allows for the development and emergence of new ideas and perspectives. However, to allow the development of rich neighbour interactions, it is essential that learning is not controlled from a single point; any learning-based group must be given a level of decentralised capability.
  • internal diversity and redundancy: systems need to be able to react in different ways to different situations to ensure a diversity of insights to aid innovative solutions to problems. However, for such diversity to be present there needs to be a level of duplication within the system, such as shared responsibility and interests. It is this duplication which allows for easy interaction within the system and for elements to compensate for inadequacies which reside there.
  • Freedom and coherence: within any system there must be potential for the exploration of possibilities resulting in the opportunity for personal agency and the diversity identified above. However, whilst this inclusion of freedom is central to the emergence of learning, complex systems are not chaotic and require a level of coherence to orientate the activity of the actors within the system. Coherence imposes a loose framework within which individuals are able to operate freely whilst creating frameworks for coherence.

It is the holding of these various tensions within a relatively stable field which allows for the development of an emergent pedagogy and learning. In addition, this set of processes leads to the need for the use of a mixed approach to pedagogy. More transmissive approaches may well be the most appropriate when setting the basic foundation for study. By ensuring that targeted, focused information is given to students in the first instance(i.e. the temporary imposition of more acute complexity reduction) students are exposed to information in a coherent way, but the introduction of greater interaction and diversity within the pedagogic system allows for an emergent pedagogy in the longer run. However, to constantly reduce to a transmissive level potentially leads to the ossifying of the system, with the boundary between environment and system becoming impermeable leading to a closed system and ultimately decay. Therefore, more discovery, enquiry and collaborative-based approaches are needed. The above discussion leads to a preliminary schematic of a complex adaptive learning system which provides a conceptual model for enabling the development of a complexity led curriculum model.

complex learning and teaching contexts

References

Biesta, G. (2010) ‘Five Theses on Complexity Reduction and its Politics.’ in D. Osberg & G. Biesta (eds.) Complexity Theory and the Politics of Education.  Rotterdam: Sense Publishers. pp.5-13.

Cilliers, P. (1998) Complexity and Postmodernism: understanding complex systems. London: Routledge.

Davis, B. & Sumara, D. (2006) Complexity and Education: Inquiries into Learning, Teaching, and Research. New York: Routledge.

Hardman, M. A. (2011) ‘Is Complexity Theory Useful in Describing Classroom Learning?’ in B. Hudson, & M.A. Meinert (eds.) Beyond Fragmentation: Didactics, Learning and Teaching in Europe. Opladen and Farmington Hills: Verlag Barbara Budrich.

Sullivan, J.P. (2010) Emergent Learning: The Power of Complex Adaptive Systems in the Classroom. Saarbrücken: Lambert Academic Publishing

Designing a Complex Curriculum – The Case of Innovation and Reform 2 – a possible complexity

‘Sense making is often about creating a whole out of fragments.’ Burns (2007:2)

This post sets out some of the basic conceptual foundations of complexity as a basis for considering its use in education in the next post in this series. Over the past 30 to 40 years complexity science has developed rapidly from its origins in physics, chemistry, biology and cybernetics to offer new insights within the social sciences. The use of a complexity lens within social contexts has started to create a different way of seeing the social world as well as suggesting new approaches to researching it. Defining complexity itself is difficult as there is no single, unified theory. As Cilliers (2010: vii) argues,

‘….there is a growing realisation that there is no single coherent ‘complexity theory’ which will unlock the secrets of the world in any clear and final way. Instead, we are beginning to understand more about exactly why complex things are so difficult to understand. We really have no choice but to acknowledge that we have to take complexity seriously, even if it does not guarantee perfect solutions.’

To demonstrate some of the different perspectives which have grown from a consideration of complexity, some of the contrasting approaches and typologies are given here. Morin (2008) distinguishes between:

  • restricted complexity – complexity as an emergent product between simple agents. Such a view allows for rules-based, agentic modelling (i.e. modelling of the parts to give a sense of the whole), which is ultimately reductionist in character, i.e. leads to the idea that by modelling the parts, even though difficult, we can gain an understanding of the whole system;
  • general complexity – within this definition the system of interest is not merely the sum of the parts/agents as when they interact they give rise to new properties which are themselves emergent and which resist modelling. Indeed Morin suggests that this shows that we are limited in our understanding of complex systems regardless of the approach we use, that we must accept this, and search for a new language in describing and explaining such systems.

Richardson and Cilliers (2001) alternatively classify complexity through three contrasting approaches to study:

  • hard complexity – computational modelling and quantitative approaches, generally used within the sciences;
  • soft complexity – employed in the social sciences and often seen as offering a ‘metaphor’;
  • complexity thinking – an approach which focuses on the lack of full understanding of systems. This inherently partial perspective is due to multiple factors operating across a number of scales. However, by focusing on emergence (the way systems develop through numerous interactions) as a central process, coherent change can be discerned and studied.

Whilst Cilliers (2010) rightly points to the breadth of approaches to complexity, calling into question the idea of a single ‘theory’, Byrne and Callaghan (2014;8) argue that we should see complexity less as a traditional theory and more as a ‘framework for understanding’, based on the notion that ‘much of the world and most of the social world consists of complex systems…if we want to understand it we have to understand it in those terms.’. They quote Castellani and Hafferty (2009:34) to differentiate a traditional theory from a frame of understanding:

‘social complexity theory is more a conceptual framework than a traditional theory. Traditional theories, particularly scientific ones, try to explain things. They provide concepts and causal connections (particularly when mathematicised) that offer insight into some social phenomenon….. Scientific frameworks, in contrast, are less interested in explanation. They provide researchers effective ways to organise the world; logical structures to arrange their topics of study; scaffolds to assemble the models they construct. When using a scientific framework ‘theoretical explanation’ is something the researcher creates, not the other way round.’

Complexity is explained on an ontological basis starting from a foundation of seeing much of social reality as complex (as opposed to either simple/linear or random).

Taken ontologically, complexity rests on a number of basic concepts.

non-linearity: non-linear systems are those in which cause and effect relationships are disproportionate, i.e. small causes may have very large impacts and vice versa. There are different forms of non-linearity, including ‘threshold effects’, where a system might act in a linear and predictable way until it hits a certain threshold beyond which it acts in the non-linear manner, commonly referred to as a ‘bifurcation point’, and ‘general deterministic chaos’ where very small variations in initial conditions give very different outputs (the so-called ‘Butterfly effect’). What non-linearity stresses is an inability to accurately model or predict a complex system. Where mathematical modelling is used to explain and characterise such systems, it makes use of ‘qualitative methods’ which generate approximate descriptions as it is not possible to create exact and accurate simulations of complex, non-linear systems. This is not to suggest that quantitative approaches to complexity have no utility, they do. However, they are best used in mixed methods approaches with qualitative approaches.

Emergence: non-linear systems have the potential to create new properties from the interactions of the multitude of elements within them, properties which are not predictable given the known starting-points within the system. Deacon (2007) argues that three ‘levels’ of emergence exist.

  1. First order emergence is characterised by an aggregate of elements to give a ‘simple’ higher-order property which occurs through statistically or stochastically determined behaviours. It is the relationships between the lower-order elements/properties which give rise to the higher-order properties, a relationship referred to as ‘supervenience’. This form of emergence is perhaps most closely aligned to Morin’s (2008) definition of restricted complexity.
  2. Second order emergence introduces the impact of time on the processes involved. First-order emergence may retain self-similarity in the relationships over time. Second-order emergence shows change and development of both ‘micro and macro-properties over time.’ (Deacon, 2007:99). This means that prior states in the system become irreversibly replaced and superseded by new states/system characteristics.
  3. Third order emergence has an evolutionary character. Here, there can be amplification of global influences on parts which can lead to recursive amplification (positive feedback) or degradation (negative feedback) across all scales of the system. Hence, initial complex states can become amplifiable initial conditions for later states.

Therefore, the basis for understanding emergence is the interplay of many properties over time and across scales which can lead to new, unpredictable states and properties.

Far from equilibrium systems: Systems can exist in a number of states. Those in equilibrium tend to be isolated with no exchange of energy with the environment beyond. In many cases the lack of interaction with the wider environment leads to decay and death. Much more common are systems which are close to equilibrium, called ‘closed’ systems where there is limited interaction with the environment beyond. Here, any move away from equilibrium tends to lead to damping effects to bring it back towards the near equilibrium state. In other words closed systems tend to be driven by negative feedback loops. Finally, there are ‘open’ systems of which most human systems are examples. These can be impacted by negative feedback but may also be impacted by positive feedback which can move the system further away from equilibrium. It is the introduction of elements from beyond the system which keep these systems in a state of flux and disequilibrium. One specific type of open system is the ‘adaptive’ system, commonly referred to in complexity theory as a Complex Adaptive System. Here, change is in part the result of experience with constant exchange of information between the system and the wider environment. Such systems have a number of features, the most important of which are discussed by Cilliers (1998) who identifies them as characterised by:

  1. a large number of elements with many interactions;
  2. interactions which are non-linear, i.e. large-scale causes can have small-scale impacts and vice versa;
  3. interactions which lead to feedback loops, both negative and positive;
  4. an ‘open’ system, having interactions with elements in external environments beyond the immediate system;
  5. elements which interact with their environment making the identification of boundaries difficult;
  6. a system which is far from equilibrium and therefore needs a constant energy flow for it to operate;
  7. the importance of history, past processes playing a role in forming the present, often unpredictably;
  8. each element only acting on local information rather than information from the whole system.

Cilliers (1998: 13) goes on to argue that such systems are so complex that any total representation of them would have to be as large as the system itself – a practical impossibility;

‘In building representations of open systems, we are forced to leave things out, and since the effects of these omissions are nonlinear, we cannot predict their magnitude.’

Consequently, Richardson et al (2007) refer to CASs as ‘incompressible’, and argue that to understand them, at last in part, we need to use different perspectives to build ever richer, if incomplete, models of the system we are interested in. To some, this might be an excuse not to bother; why research something we cannot understand in its entirety? For others, there is the temptation to use experimental approaches which isolate single variables and assume they operate in the same way in a complex context. But in both cases, the complexity of the system is lost and in many experimental approaches interactive processes are assumed to have little, or no, impact.

Final thoughts

The characterisation and understanding of complexity, particularly in social research, is still very much in its infancy. We have to accept that there is no single, accepted body of theory which is unchallenged, but whilst these restrictions exist, the basic building blocks and ontological underpinning of complexity as a ‘framework for understanding’ seem to me to be worth pursuing. Methodologically, complexity accepts that research will never ‘see’ an ultimately reality (however, I accept the epistemological perspective of ‘complex realism’ developed and advocated by Reed and Harvey (1992 and 1996) and Byrne and Callaghan (2014)). As Richardson and Tait (2010: 92-93) state,

‘Just because a complex system is incompressible it does not follow that there are (incomplete) representations of the system that cannot be useful – otherwise how would we have knowledge of anything, however limited? Incompressibility is not an excuse for not bothering.’

 

References

Burns, D. (2007) Systemic Action Research: A strategy for whole system change. Bristol: Policy Press

Byrne, D. & Callaghan, G. (2014) Complexity Theory and the Social Sciences: The state of the art. Abingdon: Routledge.

Castellani, B. & Hafferty, F. (2009) Sociology and Complexity Science. Berlin: Springer.

Cilliers, P. (1998) Complexity and Postmodernism: understanding complex systems. London: Routledge

Cilliers, P. (2010) ‘Acknowledging Complexity: A Forward.’ In D. Osberg & G. Biesta (eds) Complexity Theory and the Politics of Education. Rotterdam: Sense Publishers. vii-viii

Deacon, T.W. (2007) ‘Three Levels of Emergent Phenomena.’ In N. Murphy & W.R. Stoeger (eds) Evolution and Emergence: Systems, Organisms, Persons. Oxford: Oxford University Press. 88-110

Morin, E. (2008) On Complexity. Cresskill, NJ: Hampton Press.

Reed, M. & Harvey, D.L. (1992) ‘The New Science and the Old: Complexity and Realism in the Social Sciences.’ Journal for the Theory of Social Behaviour, 22, 356-79.

Reed, M. & Harvey, D.L. (1996) ‘Social Science as the Study of Complex Systems.’ In L.D. Kiel & E. Elliott (eds) Chaos Theory in the Social Sciences. Ann Arbor: University of Michigan Press. 295-324.

Richardson, K. A., & Cilliers, P. (2001). What is complexity science? A view from different directions. Emergence, 3, 5–22.

Richardson, K.A.; Cilliers, P. & Lissack, M. (2007) ‘Complexity Science: A ‘Gray’ Science for the ‘Stuff in Between’ in Thinking Complexity: Complexity and Philosophy volume 1, Cilliers, P. (ed.). Mansfield, USA: ISCE Publishing, 25-35. (an original version of the paper can be found at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.7038&rep=rep1&type=pdf)

Richardson, K.A. & Tait, A. (2010) ‘The Death of the Expert?’ E:CO, 12(2), 87-97.

Thinking Through Learning and Research – 3

Towards a research design

In the last 2 posts on developing learning within a research methods module (here and here) we’ve set out our view of learning, the assumptions on which the work rests, and the conceptualisation of learning and curriculum as a practical outcome. This post turns towards research methods we intend to use to help us understand how the module, learning, pedagogy and assessment work in reality.

As suggested previously, in developing a research design some conceptual assumptions have to be made. These have been, in part, the focus of the previous two posts. However, one assumption is worth developing here a little more as it is central to the resultant research design. Given the number of spaces, pedagogic approaches, and the breadth and depth of content and conceptualisation of research methods to be taught/learned, not to mention the diverse cultural, educational and disciplinary backgrounds of the students involved, we argue that this module acts as a complex adaptive system (CAS). Such systems have a number of features, the most important of which are discussed by Cilliers (1998) who identifies them as characterised by:

  1. a large number of elements with many interactions;
  2. interactions which are non-linear, i.e. large-scale causes can have small-scale impacts and vice versa;
  3. interactions which lead to feedback loops, both negative and positive;
  4. an ‘open’ system, having interactions with elements in external environments beyond the immediate system;
  5. elements which interact with their environment making the identification of boundaries difficult;
  6. a system which is far from equilibrium and therefore needs a constant energy flow for it to operate;
  7. the importance of history, past processes playing a role in forming the present, often unpredictably;
  8. each element only acting on local information rather than information from the whole system.

Cilliers (1998: 13) goes on to argue that such systems are so complex that any total representation of them would have to be as large as the system itself – a practical impossibility;

‘In building representations of open systems, we are forced to leave things out, and since the effects of these omissions are nonlinear, we cannot predict their magnitude.’

Consequently, Richardson et al (2007) refer to CASs as ‘incompressible’, and argue that to understand them, at last in part, we need to use different perspectives to build ever richer, if incomplete, models of the system we are interested in. To some, this might be an excuse not to bother; why research something we cannot understand in its entirety? For others, there is the temptation to use experimental approaches which isolate single variables and assume they operate in the same way in a complex context. But in both cases, the complexity of the system is lost and in many experimental approaches interactive processes are assumed to have little, or no, impact. This is a huge assumption to make. Alternatively, Richardson and Tait (2010: 92-93) make the case that,

‘Just because a complex system is incompressible it does not follow that there are (incomplete) representations of the system that cannot be useful – otherwise how would we have knowledge of anything, however limited? Incompressibility is not an excuse for not bothering.’

To research learning, pedagogy, curriculum and assessment in natural settings is to triangulate and extrapolate from a number of ‘glimpses’, which whilst not perfect, are not an excuse for not bothering!

Gaining glimpses

The following methods will be used as a way of capturing different perspectives of the ‘research methods pedagogic system’. Some of them are also summarised in the course diagram which appeared at the end of post 2 and is reproduced here (figure 1)

 RM course final

Figure 1 Research methods module with data capture tools. 

N.B. It is important to state that all data capture will be completed on the basis of ethical approval and student consent. Where individuals do not give active consent, data will not be analysed, or captured beyond normal group activity.

1) Baseline data: This will focus on prior learning about research methods, and on expectations and pre-existing knowledge and conceptualisation. The data here will be captured through three channels:

i) student applications: baseline data concerning degree disciplines and exposure to research methods training

ii) baseline questionnaire: focusing on demographic data and prior learning as well as expectations of the course.

iii) semi-structured interviews: using purposive sampling to gain a range of views on past experience of research methods. Consideration of student views as to what they believe a research methods course is likely to include.

2) Lesson Study: Used with four sessions throughout the course (marked LS1,2,3, and 4). This will allow us to consider in detail what we are attempting to develop in terms of concepts, knowledge and application. It will also reflect back to us our assumptions and viewpoints in terms of planning, execution and evaluation of learning in the module. We will predict student approaches to learning in areas of the subject matter where they find it to gain proficiency (e.g. epistemology, research design, etc). Used in conjunction with other data capture techniques it will also give us useful information on the process of learning itself, particularly in relation to the social learning dimension (Illeris, 2007). To augment and triangulate observation insights we will carry out stimulated recall interviews (Gass and Mackey, 2000) with the students who have been observed, and we will make copies of any notes they have made. Both of these will act as useful, if imperfect, sources of data on the cognitive dimension of learning.

3) Staged capture of experiences: At three points over the course of the module, we will run a questionnaire focusing on the concepts, content and application of research methods within the course and student confidence in each of these areas. This will then be expanded on with the use of further, more general stimulated recall interviews. We will also ask those who are interviewed if they will allow us to copy their course notes (both formal and informal).

4) Participatory focus groups: Given we are interested in developing a better course of study, and given our work within a complex adaptive system, we intend to hold three participatory focus groups at the same time as the ‘staged capture of experiences’. These will be in the form of discussions to give the students an opportunity to help shape the form and detail of the module as it unfolds (in a similar form to that used by Wood and Butt, 2014), so that their insights become part of the ongoing planning process.

5) Documentary evidence: The collection of assignment work, our planning and resources documents and eventually, copies of dissertations will all be useful in comparing student outcomes against other data channels.

6) Self-explanation videos: Self-explanation theory (for example see Renkl, 2002; Kuhn & Katz, 2009; Chamberland et al 2013) rests on the notion that asking students to explain to themselves how they are undertaking a task as they do it enhances their understanding and learning. The evidence for the utility of this approach to learning is uncertain with mixed results. In this research design we intend to shift the focus of its use to act as a reflective tool rather than as a process used in the action of learning itself. Towards the end of each area of study, we will ask students to produce some form of short summary, be it a series of images, a list or a concept map. Once they have completed these on their laptops, we will ask them to open Screecast-O-Matic (http://www.screencast-o-matic.com/) and record an audio narrative over screen capture (maximum of 5 minutes), explaining to themselves what they have learned, how it links across to other facets of the module, and what they believe they still need to work on. Once their narratives are complete they will be asked to save them for their own use, and also to send a copy to us. This will give us the opportunity to understand some of their conceptualisation and understanding, as well as incomplete, emerging and misconceptions. Over the whole module it will also allow us to track individual learning trajectories, particularly if used in conjunction with interviewing and work scrutiny.

The data capture approaches described above will result in a very large volume of data which will take a considerable amount of time and effort to analyse and cross-reference – what we are currently referring to as a ‘thick description mixed methods’ approach. However, what it will give us is a fine grained, if incomplete, perspective on the interplay of learning, pedagogy, curriculum and assessment. By capturing a number of different perspectives it will still only offer the potential for an incomplete model of the research methods module system, but one which will potentially give us a number of useful and important insights nevertheless.

References

Chamberland, M. et al (2013) ‘Students’ self-explanations while solving unfamiliar cases: the role of biomedical knowledge.’ Medical Education, 47, 1109-1116.

Cilliers, P. (1998) Complexity and Postmodernism: understanding complex systems. London: Routledge.

Gass, S.M. & Mackey, A. (2000) Stimulated Recall Methodology in Second Language Research. Abingdon: Routledge.

Illeris, K. (2007) How We Learn: Learning and non-learning in school and beyond. Abingdon: Routledge.

Kuhn, D. & Katz, J. (2009) ‘Are self-explanations always beneficial?’ Journal of Experimental Child Psychology, 103, 386-394.

Renkl, A. (2002) ‘Worked-out examples: instructional explanations support learning by self-explanations.’ Learning and Instruction, 12, 529-556.

Richardson, K.A. & Tait, A. (2010) ‘The Death of the Expert?’ E:CO, 12(2), 87-97.

Wood, P. & Butt, G. (2014) ‘Exploring the use of complexity theory and action research as frameworks for curriculum change, Journal of Curriculum Studies, http://www.tandfonline.com/doi/full/10.1080/00220272.2014.921840#.U6xU47BwaUk

Some Initial Thoughts on Pedagogic Literacy – Contextualising Scholarship of Teaching and Learning

All humans have the capacity to both learn and teach. Children teach each other games, parents teach their children how to tie their shoe laces, how to behave and how to ride a bicycle. Teaching is as ubiquitous as learning; it is part of what it is to be ‘human’. In pre-history, humans taught and learned through mimetic and experiential learning and teaching. However as societies become more complex and individuals begin to specialise in the jobs they do, one pursuit which emerges is that of teaching children. Whilst some have responsibility to look after animals, to harvest crops, or build computers, etc, others become expert in teaching others. This is the obvious extension of the formalisation of the act of education. In specialising in pedagogy (here taken to mean the process of bringing together teaching, learning, curriculum and assessment to enable others to extend their experience of the world) teachers move from an informal process of teaching, i.e. trial and error, often implicit and subconscious, to one which is made explicit, problematic, and around which can be built structures and processes to enable better, more efficient, more creative ways of helping others to learn.

By seeing pedagogy as a process which all people have ability in it becomes a continuum rather than something that some of us have and others do not. Instead, what distinguishes teachers is a heightened awareness, understanding and pursuit of better pedagogy. The elements and processes involved in developing pedagogic skill and insight is what we term pedagogic literacy.  Pedagogic literacy is constituted of a wide repertoire of teaching-related knowledge, skills, values, dispositions and attributes. Figure 1 gives a summary of some of the main features involved.

PL outline

Figure 1. Outline of some of the main features of pedagogic literacy

Pedagogy is a complex process, where complexity should be understood by its scientific meaning of being nonlinear, incompressible, and emergent. Taking each of these ideas in turn, nonlinearity emphasises that teaching and learning do not occur in simple cause-and-effect relationships; by teaching X, I cannot universally predict that student Y will learn Z. Sometimes major interventions will have little effect, at other times the smallest of interactions may have profound impacts. The work of teachers is also incompressible in that any attempt to boil down and summarise the complexity of pedagogy into a simple list is doomed to curtail the depth and breadth of the reality of the pedagogic environment (see this post on complexity and graded observation as an example). Finally, associated with the incompressibility of teaching is the notion that pedagogy has features which are beyond the sum total of factors which come together to make it possible, in other words the act of pedagogy is itself emergent and as such is highly unpredictable at any level other than the most general. It must be stated that by accepting that pedagogic literacy is a complex adaptive system, and hence is incompressible, any model of its characteristics such as that in figure 1 can only ever hope to be a guide rather than a totality. It is given as partial description as opposed to a definitive explanation.

For those teachers who become expert at their job there is a career long investment in engaging in understanding the detail of their chosen profession. The complexity of pedagogy is such that we must continue to develop our understanding over the course of many years; in fact it is a truism that we will have neither a perfect understanding of the processes of teaching and learning nor ever teach the perfect lesson over the course of our career’s span. As a medium for understanding the ways in which individual teachers develop, pedagogic literacy is constituted of a wide (though not exhaustive) repertoire of teaching related knowledge, skills, values, dispositions and attributes.

Figure 1 highlights two important features of the concept of pedagogic literacy. Firstly, we see pedagogic environments as complex adaptive systems in which teachers move between the different levels constantly; they ‘level jump’ as they experience pedagogic events and develop pedagogic expertise. As an example, if a student does not understand a concept or area of knowledge, the values of the individual will work with their professional skills and understanding (personal growth) whilst also operating within the organisational and societal contexts to develop pedagogic solutions to aid understanding. The act of pedagogy is a complex interplay between these different levels and processes. Secondly, given the wide range of knowledge, skills, values and contexts inherent in the pedagogic environment, the development of teachers is emergent, and by extension, pedagogic literacy is an emergent process. Teachers’ developing expertise is more than the sum total of the constituent parts, it has extra value and meaning in its own right. However, pedagogic literacy as an emergent process is not deterministic, in other words emergence does not ensure positive change and expertise.

Over several future posts we’ll develop different aspects of pedagogic literacy to illustrate the ideas, concepts and processes which constitute the pedagogic network. Two limitations must be stressed immediately however. As suggested earlier, due to the incompressibility of pedagogic literacy any treatment can only hope to be a partial description, but is nonetheless useful for it. Secondly, the same problem exists in writing about this concept as Deleuze and Guattari (1987) had in writing The Thousand Plateaus, in that writing is a linear construct which is attempting to describe and explain a networked concept. Therefore, whilst we attempt to make links between ideas and posts, this cannot be exhaustive. However, one advantage in this is that we encourage anyone reading these posts to make links and networks of their own.