4.3 Using action inquiry

Week 10: 3rd - 9th July (Activity: 4.4)

Consistent with the shift from training to learning is the increasing use of an approach known as action inquiry (AI). AI belongs to a family of methods that aim to get learners away from the classroom and from purely theoretical learning sessions and into the ‘real world’ where there are greater opportunities for practice-based learning. You will recall from Unit 1 that the themes of reflection and learning from experience underpin the design of the whole module. These themes also underpin AI.

In this section, you will explore some of the key features of AI, drawing out the practical implications of planning, designing and delivering learning and talent development programmes based on AI principles. AI is, of course, not the only way in which instructional designers can move away from a reliance on formal, classroom-based training methods, but it does offer some ideas that you might find useful both for enhancing your own development experiences and for designing effective learning programmes for others.

What is action inquiry?

AI is a set of methods involving collaborative learning where a small group of learners (sometimes referred to as an ‘action learning set’) meet regularly to work on real issues. AI typically involves this group coming together to plan and deliver a specific project. Often the project relates to something that learners have found frustrating in their respective organisational roles – the sort of thing where people say, ‘why hasn’t anybody done anything to sort this out?!’ Mobilising an AI project gives this sort of issue the extra attention, energy and focus required to sort it out, or at least to make progress with it.

The basic philosophy of AI is that the most effective learning takes place when individuals are faced with a real problem to solve, and are genuinely invested in finding a solution to it. AI traces its roots back to Kurt Lewin (1946), who saw action research as a means of conducting systematic inquiry into organisational and social phenomena, and argued that knowledge is produced in the midst of action. AI is, therefore, a kind of learning within practice, rather than learning about practice.

AI is unlikely to be suitable for all capability and learning needs. For instance, it is not normally the best way to help people develop very technical and/or specialist skills, nor is it likely to be the best option with high-risk skill areas involving issues of health and safety, where there is little or no room for experimentation or learning through trial and error. AI is more likely to be suitable where there is an opportunity to bring together a range of learners with different expertise and experience, and where the focus of learning is on the mobilisation, coordination, leadership and motivation of these different contributions.

Box 4.2: Key features of AI

• AI explicitly constructs the learner as agent, that is, as someone capable of taking ownership and responsibility for aspects of their own learning, rather than being a passive recipient of information decided upon by others. This is why AI is one of the favoured approaches for those who see a crucial shift from training to learning, which you discussed in the last activity and TGF. In AI, the learner is often constructed as a researcher rather than a student, underscoring this move from passive to active learning.
• Related to this notion of agency, AI usually uses the language of ‘projects’ rather than ‘case studies’. There are similarities between these in the sense that both deal with a specific, concrete issue or instance (idiography) rather than more generalised, abstract knowledge (nomotheism). However, there is an important difference in the way we normally understand ‘case studies’ as something defined by instructional designers rather than owned by learners themselves.
• AI usually involves participation and collaboration, and is therefore especially attractive for development programmes geared around teamwork, communication, engagement and stakeholder relations. It is also possible to use AI with individual projects rather than project teams, but this is less common. (The reading for the next activity contains an example of AI with individual projects to give you a sense of how and why AI might be used this way.)
• AI is based on what is real, not just what is realistic. This is seen as more engaging for learners, because they have the opportunity to work on things that really matter to them.
• AI can be seen as an instance of critical reflection in action, because it can surface the taken-for-granted assumptions of organisational life. It involves learners honing a kind of curiosity about their normal, default understandings and behaviours, in order to develop a greater awareness of other options and possibilities.
• AI is based on an iterative cycle of problem identification, diagnosis, planning, intervention and evaluation. In this sense, AI dovetails with a general shift away from linear models of organisation and learning towards more iterative, integrative approaches.

Facilitation of AI

If you are considering using AI, one of your most important decisions will concern the way in which participants’ project work is to be facilitated. A range of facilitation options exists for AI planners – from a very hands-on, directive approach to a more light-touch, enabling approach. Figure 4.6 depicts some of the facilitation options available to you, based on Heron’s (2001) model of authoritative and facilitative techniques.

Your choice of facilitation strategy will need to take account of the characteristics of the learners in any particular programme (skill set, background, seniority, etc.), as well as being consistent with the overall learning objectives for the programme. Facilitators will typically be involved in regular meetings with the project team to review progress, discuss issues and encourage peer-level feedback, whether this is in directive or enabling mode – or indeed, a mixture of both. Facilitators may also be involved in observing the project team in action, assuming this is consistent with the practical and ethical nature of the project. Although the choice of facilitators is always important in development programmes, with AI it is particularly important. Since the projects are usually embedded in day-to-day operational and organisational dynamics, choosing facilitators who come across as very academic and/or detached from these dynamics is likely to provoke a certain amount of learner resistance!

The topic of learner resistance is covered in more depth later in this unit. You will have more opportunity to reflect on differences in facilitation technique in Units 6 and 7 of this module.

Practical considerations with AI

There are a number of practical issues to decide when mobilising an AI project within the context of a development programme.

• Constitution of the group (‘learning set’): Is this self-evident based on certain obvious groupings of learners? Do you, as the HRD professional, want to decide on the groups, or will you let them form themselves? Will you set any criteria or ground- rules for group size or membership?
• Contracting and expectations management: AI tends to operate on the principle that the more ownership the group itself has over the agenda and conduct of the project, the better. However, this does not mean that participants have to start from a blank piece of paper. As the HRD professional, you might decide to give the group some sort of briefing, offering suggestions for scope, timescales, roles and responsibilities, etc., for the project, in line with your overall facilitation strategy.
• Project planning: Because this is a development project – an opportunity constructed with the explicit aim of gaining and reflecting on knowledge and experience – planning timescales may be longer and perhaps more flexible than for normal, that is, non-developmental projects. This is because enough time and space need to be allowed for cycles of both action and reflection – for both getting things done and coming together as a group to discuss, evaluate and potentially revisit or change the group’s activities and priorities. Developmental AI projects are characterised by a greater degree of iteration and revision than most normal projects can sustain.
• Roles: It is helpful for the group to agree their respective roles relatively early. Again, in line with your overall facilitation strategy, you will decide how much you stipulate and direct, versus allowing the group to take ownership of this themselves. Factors to consider include whether you want to encourage learners to adopt their most natural roles, or whether you specifically want to encourage them to practise in roles with which they are less accustomed and/or comfortable.
• Outputs: It is important for both you, as the HRD professional, and the group to decide and agree up-front what the outputs of the project will be. Will they be written outputs, such as a report or an essay? Will there be an intention to try to influence organisational stakeholders, in which case the outputs might include the development and presentation of the project’s findings to a board or other key stakeholder group? As the designer of this learning intervention, you will need to link these outputs with your overall evaluation strategy for the programme.

Critical thinking about AI

Beyond these practical and technical issues, Reason (1999) suggests some of the political and psychological factors influencing the success of such programmes:

• Managing expectations about the iterative nature of AI: The iterative nature means not only that AI projects will typically take longer, but also that allowance should be made for divergence as well as convergence between group members. If full use is to be made of the dynamics of AI, learners need to be supported and encouraged to suspend their normal urge for closure and completeness in order to acknowledge and discuss any differences that arise among project team members in their interpretations of the way the project is progressing. One of the benefits that many scholars see in AI is the way it encourages participants to acknowledge the uncertainty, ambiguity and occasional messiness of day-to-day organisational life, as opposed to the seemingly ordered and calm world that is often depicted in the text books.
• Getting a balance between action and reflection: As Reason (1999, p. 212) puts it, ‘too much time in reflection is just armchair theorising; too much time in action is mere activism. But it may be important, particularly in the early stages, to spend considerable time reflecting in order to gather together experience; and it may be important later to concentrate on trying out different actions to see how they work. Each inquiry group needs to find its own balance between action and reflection, depending on the topic being explored.’
• Engaging collaboratively: An AI project can be fatally undermined if the project team does not find ways of working successfully with one another. This does not have to mean that everyone contributes absolutely equally, but project team members do need to feel that everyone is invested in the learning experience. As the HRD professional responsible for the learning project, you will need to decide how, when and whether to intervene if problems with team collaboration emerge.
• Dealing with distress: Learning can be an unsettling business – something which is not always acknowledged in manuals of instructional design! AI programmes can evoke particularly strong emotions in participants, because they tend to involve issues of genuine concern to them. This means participants are more likely to be engaged and invested in learning, but also that they may be more distressed if the project goes awry or if there are disagreements among project members. As the HRD professional, you need to be alert to the possibility of this, and think through whether you wish to provide any additional support to learners. For instance, you might want to think about providing access to counselling services. There will be more on the topic of learner distress and anxiety later in this unit.

Peer learning

When used with groups (‘action learning sets’), AI typically involves aspects of peer learning (Boud, 2001), and related practices of peer coaching (Parker et al., 2008) and peer assessment (Brutus and Donia, 2010). All these practices are based on the idea of students learning from one another, as development moves beyond independent towards interdependent or mutual learning.

Peer learning can feature in many different learning designs, including private study groups, collaborative project work (such as an AI project), ‘buddy’ systems at work, and peer-feedback schemes. It also features in many technology-enabled designs, such as online discussion groups and webinars. Whatever the specific delivery mechanism, peer learning is supposed to be mutually beneficial for all participants; the aim is to encourage democratic learning, not to serve the needs of some students over others. Indeed, participants will only engage fully with peer learning if they perceive it as equitable, not if it feels like some learners are ‘carrying’ the others.

You will have noticed that aspects of peer learning are designed into this masters programme, and in this module in particular; for instance, in the way in which the TGFs and online rooms operate. Peer learning can also play a crucial role in coaching and mentoring. This overview is designed to give you a flavour of what is involved in designing programmes incorporating peer learning, and a sense of the main issues that are inspiring critical reflection among academics and practitioners.

Benefits and challenges of peer learning

Peer learning has become popular with HRD experts for several reasons. Philosophically, its popularity is related to efforts to acknowledge, perhaps even try to dissolve, some of the power dynamics in organisational life, including those you discussed in Unit 3. It is seen as a more democratic form of learning than traditional teacher-centred approaches, providing fertile ground for multiple views and viewpoints to be expressed, challenged and refined. With adult learners, in particular, it is seen as a good way to try to acknowledge and make use of the considerable expertise and experience that learners often bring into a development programme, rather than merely assuming that the only person with anything to contribute is the teacher/facilitator. Peer learning is considered especially suited to fostering critical reflection, with some educational theorists suggesting that it is more effective at developing learners’ reflective skills than even the best-planned and most skilfully executed teacher interventions (Boud, 2001).

For Boud (2001), the key challenge for peer learning is the question of how we learn from people with whom we do not identify, in other words, where there does not seem to be enough in common for us to understand where they are coming from on an issue. He therefore places the issue of difference and diversity right at the heart of instructional designs that incorporate peer learning. The issue of diversity in learning is being raised more and more frequently with the internationalisation of learning environments – both professional and academic – where learning groups are increasingly made up of learners from different cultural backgrounds, speaking different languages, and holding different implicit and explicit models of the learning experience.

Tomkins and Ulus (2015a) turn this issue on its head and suggest that an equally significant issue for peer learning is how we learn from people with whom we identify too easily, that is, that the crucial issue is not difference but apparent similarity. This is especially the case when it comes to peers evaluating one another’s work, whether formally or informally. The authors suggest that we all fall back on what is familiar to us when evaluating another person’s ideas, usually without realising it. When giving feedback to a peer, our implicit frame of reference is ‘how would I have done this myself if I had been in this person’s shoes?’ And in this way, we stay locked in our existing ways of thinking, rather than being prompted to engage in the fact that other people see things differently. This gets in the way of genuine learning from one another.

So, there may be some subtle power dynamics in peer learning which need to be managed if peer learning is to achieve its desired outcomes. Tomkins and Ulus (2015a) suggest that, as instructional designers and facilitators, you should give participants guidance and support on how to give and receive feedback as peers, rather than just assuming that it is something that all professionals are able to do as a matter of course. You may also want to consider reinforcing the importance of kindness and mutual respect in any peer feedback exercise you initiate. There is a bit of an irony that, while peer learning has become popular as a learner-centred approach, it still needs strong facilitation to make it effective.

You will have the opportunity to work with these ideas in a more practical context in Units 6 and 7 of this module.

Communities of practice

Related to AI and peer learning is the concept of communities of practice. You will recall from Unit 2 that this term is associated with the classic work of Lave and Wenger (1991), who developed a model of situated learning based on the idea of engagement in learning communities. Their work is a critique of the ways in which learning has traditionally been seen as something that individuals do, and as something with a clear beginning and end, as in our everyday understanding of what a training course or qualification looks like. The idea of a community of practice is intended to encourage an alternative – or at least complementary – view of learning as an ongoing, social and intersubjective experience. Communities of practice are perhaps the strongest manifestation of one of the main themes of this unit, namely the increasing importance afforded to learner agency and ownership of developmental needs and outcomes.

For Lave and Wenger (1991), three elements are important in distinguishing a community of practice from other groups and communities. All three underscore the connections between situated learning, action inquiry and peer learning:

• The domain: A community of practice is something more than a club of friends or a network of connections between people. Its scope and identity are defined by a shared domain of interest. Membership therefore implies a commitment to the domain, and an assumption that members will be able and motivated to contribute to it.
• The community: Members of a community of practice engage in joint activities and discussions, help each other, and share information. They build relationships that enable them to learn from each other. The community is not necessarily contained within a single organisation or corporation; increasingly, the community extends beyond the boundaries of a single institution, encouraging people from various different institutions to collaborate. This can mean that people who are competitors in a business context come together as collaborators and partners in the context of learning and development.
• The practice: Members of a community of practice are practitioners. They develop a shared repertoire of resources: experiences, stories and anecdotes, tools, examples of best practice and ways of addressing recurring problems, etc.

Communities of practice are not usually something that can be mandated either in or across organisations. Rather, HRD professionals focus on the ways in which they can be cultivated, that is, encouraged, enabled and rewarded. The idea that people form communities based on common interests and a desire to enhance their own learning and development is closely associated with the notion of the learning organisation (Argyris and Schön,1992; Senge, 1990) that you covered earlier in this unit. These communities therefore have the potential to contribute quite significantly to learning at both an individual and an organisational level (see Table 4.1).

Table 4.1: Potential benefits from communities of practice
	Short-term value: Improving business outcomes	Long-term value: Developing a culture of learning
Benefits to the organisation	·        Arena for problem-solving ·        Improved quality of decisions ·        More perspectives on problems ·        Resources for implementing strategies ·        Strengthened quality assurance (QA) ·        Securing profile and stakeholder backing for new projects	·        Increased retention of talent ·        Increased authority with clients and other external stakeholders ·        Capacity for knowledge management ·        Forum for competitive benchmarking ·        Capacity to develop new strategic options ·        Ability to predict technological developments ·        Ability to take advantage of new market opportunities
Benefits to individual community members	·        Sense of organisational identity and belonging ·        Access to expertise ·        Meaningful contribution to organisational success ·        Greater ownership of own development	·        Forum for sharing expertise and developing new skills ·        Enhanced professional reputation and employability ·        Enhanced sense of professional and/or subject matter identity
(Adapted from Wenger et al., 2002)

AI in practice

The activity below gives you an opportunity to work with AI. You might also want to bring elements of peer learning and communities of practice into your design, depending on how well these match the learning objectives you set for the programme.

Activity 4.4: Using action inquiry (120 minutes)