Designing a Complex Curriculum – The Case of Innovation and Reform 1 – some basic assumptions

One of the specialist pathways which we offer on our MA International Education course is focused on the area of ‘Innovation and Reform’. With the seemingly constant shifts which are occurring in education systems throughout the world, both of these concepts seem relevant for study. This is especially the case for many of the students with whom we work who come from countries where new ideas about the role and content of education are being constantly discussed and developed.

As part of a regular review process we have decided that we want to focus on complexity theory as an organising perspective for this pathway as it offers new and different ways of thinking about education and change. This perceptual shift offers opportunities in both the content we cover and the way we understand and develop curriculum and pedagogy. The basis for using this approach is a discussion/debate we believe needs to be explored more explicitly within education, a debate concerning the nature of educative processes and the approaches needed to research them.

A distinction which is central to much of the policy generation and management of education, but which is rarely acknowledged or explicitly explored, is the conceptual difference between reductionism and holism. Reductionism is based on the notion that any system can be split into its constituent parts, parts which once identified can then be analysed and understood before being reassembled to reconstitute the whole. This approach leads to the identification of simple cause and effect relationships where particular inputs to a system are believed to lead to certain outputs. Education research becomes a process of identifying and characterising these cause and effect relationships. The end-point of this philosophy is the uncovering of a completely ‘knowable’ system which can then be manipulated with the use of simple predictive input-output processes. Such systems might be ‘simple’ – having a few variables which can be tracked and predicted, or ‘complicated’ where the only additional feature is a greater number of variables, and in both cases can be characterised as being ‘linear’.

Education has been increasingly managed and developed using these underlying assumptions under the guise of New Public Management. Schools are run through recourse to numeric data (the potential damaging effects of which I’ve previously discussed here), ‘improvement’ being the result of individual, atomistic interventions. Performance management becomes reliant to the belief that analysis of data can lead to the identification of specific interventions which are then related to simplistic outcome targets as if there is a clear one-to-one relationship at the heart of teaching and learning processes. More widely, some research funding bodies have taken a similar perspective. Approaches such as randomised controlled trials have become increasingly popular, the assumption being that single variables can be distilled out from the complexity of educational settings and then assessed for their impact on educational outcomes. All of these approaches assume a mechanistic (and perhaps even deterministic) view of the educative process.

Contrary to the reductionist view is that of holism, a perspective which stresses that both the constituent elements and the wealth of connections between them need to be taken into account to understand the system with any degree of depth. Here, the whole is greater than the sum of the parts and as such is not open to being disaggregated into constituent elements. This leads to a very different view of educational processes. There are a number of different traditions within this worldview, two of the most well-known being systems theory and complexity theory.

In developing a new approach to our Innovation and Reform specialist pathway we have decided to use a complexity lens as an alternative medium through which to consider and discuss the dynamics and processes of educational change. This is the first in a series of posts which begins to consider and develop some of our thinking as we plan and execute this new approach. What is at the core of the philosophy in developing this strand is the belief that we should not only use complexity theory as a framework for engaging with educational ideas, but that the very course itself should be planned and experienced through a complexity approach. Therefore, in considering these issues, posts which follow will consider:

  • some of the basic foundations of complexity theory;
  • a conceptual framework for structuring a complexity-driven pathway;
  • an exploration of learning through a complexity lens;
  • creating a positive tension between prescriptive and emergent learning (including a discussion of knowledge and understanding)
  • the foundations for an ‘emergent curriculum’;
  • how all of this can be used to develop a coherent and meaningful curriculum for international masters students.

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 ( 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.


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,

Thinking Through Learning and Research – 2

In the first post on thinking though learning and research I briefly outlined how we define the process of learning (through the work of Knud Illeris), as well as some of the contextual assumptions we make. In this post, I want to explore how these foundations translate into a conceptual model and from there to the curriculum model outlined at the end of the first post and which is augmented here.

Our first theoretical standpoint is that we are dealing with a complex adaptive system (as outlined in the first post) which leads to the acceptance that the processes involved are non-linear, interact in unpredictable ways and are therefore emergent in nature. We are also assuming that student learning rests on developing ever more complex and detailed schemata relating to research methods. These develop and coalesce around a small number of threshold concepts (which I first discussed here). The threshold concepts we believe a master’s level course should address are:

  • Criticality (in reading and writing)
  • Theory
  • Methodology
  • Analysis
  • Epistemology/Ontology/World View
  • Ethics

These are central to understanding, designing and competing small-scale research projects. They are therefore the basis for laying a strong foundation for those advancing to doctoral-level study.

A schema, or schemata, will emerge and coalesce around these concepts. These will then, hopefully act as an emerging framework for critical engagement with published research in the form of both empirical research and the research methods literature itself. In addition, the framework will be the basis for the practical application of these ideas and for the development and completion of small-scale research activities and projects which will eventually culminate in a master’s dissertation.

In considering the threshold concepts at the core of the course development, we decided at an early stage that we needed to give ourselves and students time to engage with both content and practical application. To do this we want to be able to introduce areas of research methods, for example methodology and its various forms, or ethics in a critical and in-depth way. In introducing each area we need to continually build links so that knowledge development is situated in both a wider schematic of research methods, whilst also being deeply rooted in relation to concepts. As a consequence, all research methods sessions will last for a minimum of one day (Figure 2).

By having longer, but less frequent sessions, time is given for introduction and discussion of new knowledge, the development of understanding of the links of that knowledge to concepts, as well as a consideration of practical application and use. This then suggests a set of inter-related processes (figure 1), such that

understanding elements of learning for a master's RM programme

The diagram in Figure 1 is an initial attempt to create a framework for research methods learning. It takes the growth of knowledge, the understanding of threshold concepts through liminal processes of thought, discussion, and reflection, and the application through the enhancing of practical skills as a holistic model of emerging research ability. All three elements are vital, and need to be intertwined to bring a critical understanding and practical ability in carrying out research. Each element is important for if there is any element missing there is less than a holistic approach. If students are given knowledge and told to apply this, then they may see application as a simple set of tick lists, a ‘mechanistic’ application as the knowledge instilled will tend to be technical in form where it is not underpinned by conceptual understanding. This also means that research quality will be compromised as when difficulties arise, or alternative approaches need to be developed in a particular context, the lack of deep, conceptual underpinning will lead to less flexibility and possibly to the use of inappropriate approaches.

Likewise, if concepts are explicitly discussed, but are not linked to a breadth and depth of knowledge, any link to application will be weak as a knowledge-base is important for practical application. Finally, the cross-over between concepts and knowledge is where I would place the recent surge of interest in ‘research literacy’. Here, engagement with the conceptual framework of research methods, together with a developing knowledge of approaches and examples, will lead to an emerging theoretical engagement and understanding. However, it will be devoid of practical skills in application and the ‘messiness’ of research as it is planned and executed. This is not a major weakness for those wanting to engage with the research of others, but will mean that some of the messiness inherent in research is not clearly or critically understood.

It is where all three elements of learning are focused on and developed that critical understanding and application will emerge. However, this has to be seen as an iterative process, one which extends beyond the end of any level of formal learning and training. It is the interplay of these elements which, over very long periods of research activity, lead to individuals who can be identified as ‘experts’ in research.

Taking the intertwined development of knowledge, concepts and application as the core of a research methods course, we have developed an approach which tries to engender these principles. As a result, the course has the following form (Figure 2).

RM course final

The course covers each of the main conceptual areas, beginning with a consideration of what we actually mean by research and moving forward to cover each area in turn, thus creating a ‘research methods’ narrative. These build on each other, and once an element has been covered it will be enfolded into later discussions. No session is less than a day long, and some are longer. For example, research design and tools is a 3 day session. This allows for revisiting of previous knowledge and conceptual bases, and the incorporation of research design as a further element of successful research planning. Having introduced these elements more formally, the length of the session will give time for discussion, planning, creation and problem-based and discovery learning. The result will be the development of a draft research design for a dissertation, as well as a research tool to be trialled as a pilot.

As well as the knowledge and conceptual bases being developed in the face-to-face and online materials, an application strand will start from early in the course. Initially this will take the form of pairs of students carrying out semi-structured interviews with established researchers to investigate their views concerning research, preferences of research approach, as well as some idea of research career history. Results will then be shared later in the course. Having completed this component, students will then need to carry out individual piloting of a research tool which they have created based on their work in the course. This will give them an opportunity to develop and critique research tools in a supported and structured environment. The data from both exercises will then act as the core for a two day session on analysis tools making use of the authentic data sets collected by the students themselves (supplemented where necessary to ensure engagement with both quantitative and qualitative analysis).

The development of application, knowledge and conceptual understanding throughout the course will culminate in the completion of a dissertation. By developing all strands together in a holistic approach, this will be the ‘acid-test’ of how well the students have developed a deepening, critical and positive potential in carrying out their own small-scale research.


The final post of this strand will consider how we can capture a useful understanding of the various elements and processes in the course.