Who Really Cares about Using Education Research in Policy and Practice?

8. The role of learning conversations to improve outcomes for students

Chris Brown
University of Warwick, United Kingdom

Cindy Poortman
University of Twente, the Netherlands

Abstract

Teachers and school leaders often need to address complex problems or develop or adopt innovative approaches to improve student outcomes. A more elaborate collaborative learning process is often needed to do so than regular teacher meetings usually allow. Learning conversations refer to an approach in which teachers engage with knowledge (e.g. from data and literature) about problems and ambitions to be able to systematically generate and test ideas, respond to these problems, and achieve ambitions for educational improvement. This chapter explores the idea of learning conversations in detail and outlines how teachers can use them to improve teaching and student outcomes through the development of evidence-informed policies and practices. After explaining the concept, main phases and real-life examples, the chapter discusses the key conditions and effectiveness of learning conversations. The chapter concludes with policy implications.

Introduction

Many teachers and school leaders enjoy working together (within and across schools) to improve student outcomes. Sharing their ways of working, ongoing challenges and new ideas helps them think outside the box and develop their teaching to better address students’ needs. The focus on learning in communities and networks (and, specifically, in professional learning networks or PLNs1) has been growing internationally. Yet collaboration focused on improvement will only ever be fruitful if teachers embark on new courses of action as a result. Collaboration should thus lead to learning. This learning ideally involves a focus on research, data or other forms of evidence, and is encapsulated by the idea of “learning conversations” (Brown et al., 2021[1]). This chapter explores the concept and process of learning conversations in detail and outlines how teachers can use them to improve teaching and student outcomes through the development of evidence-informed policies and practices. The chapter also outlines how learning conversations operate most successfully including the key steps to facilitate learning conversations and the factors that make them effective.

What is a learning conversation?

As part of their modus operandi, teachers and school leaders attend many meetings. For example, there are regular team meetings, department meetings, teacher-parent meetings, professional development sessions, and so on. Much of the conversation in these meetings is centred on administrative topics, and sometimes considerable time is spent thinking about dates and planning for future meetings (Zala-Mezö and Egli, 2022[2]). However, to achieve meaningful change a laser-like focus on student outcomes and ways to improve these is a fundamental starting point. This does not mean that exchanging anecdotes and experiences about practice is not valuable. Typically, such exchange enables colleagues to get to know one another and develops trust and inspiration. Yet, more is needed to achieve the kind of learning necessary to realise positive change for students.

Moreover, meetings are often focused on immediate action rather than the prior step of investigating issues. In other words, they are addressing the symptoms of a problem rather than their cause. Of course, wanting to solve problems as soon as they are identified is understandable. However, it is much more likely that we will actually solve a problem when we know what the current situation is, what is causing it and what research tells us about how similar problems have been solved elsewhere. Taking immediate action runs the risk of spending valuable resources on “solutions” that potentially do not work, and it usually takes a while (often at least a school year) before this becomes apparent.

Consequently, a more elaborate process of collaborative learning is needed to address complex problems or develop or adopt innovative approaches to improving student outcomes. A process in which:

• cognitive dissonance is fostered [i.e. individuals experience a change in their fundamental beliefs regarding how the world operates: Chinn and Brewer (1993[3])]

• teachers engage with knowledge about the problem at hand (e.g. from data and literature) and possible causes of this problem

• teachers systematically generate and test ideas to respond to these problems (Brown et al., 2021[4]).

In the literature, many different labels are used to refer to collaborative learning. Deep-level collaboration, reflective dialogue, depth of inquiry and generative discourse are some examples (Brown, 2018[5]; Zala-Mezö and Egli, 2022[2]; Vangrieken et al., 2017[6]). Our preferred term is learning conversation and the next section will guide the reader through its main features.

Who participates in a learning conversation?

Participants can vary depending on the aims and focus of the learning conversation. For instance, learning conversations may involve teachers and school leaders working on an inter-subject or year group basis, or they may involve intra-subject or year group engagement. They may include teachers from different schools; local or national policy makers; other relevant stakeholders from allied professions such as health, youth justice or social care; or any combination of these. Learning conversations often involve partnerships with academic researchers, who can provide a much-needed facilitative function. The presence of a facilitator ensures that participants benefit more from the conversation. Facilitators typically organise the meetings and ensure resources for the activities. Importantly, they support the effective dynamics and interpersonal relationships between participants as they go through the activities detailed below.

The four phases of learning conversations

Once the facilitator has been selected, participants take part in a process which comprises the following four phases:

1. Learning conversations begin with a process of formulating collaborative goals for improving student outcomes.

• Current success(es) and/or challenge(s) are the starting point for developing this goal.

• These successes and/or challenges are derived based on data on the current situation in teachers’ own practice.

Formulating a goal that guides the learning conversation is an activity that does not always take place in a single meeting. The process of formulating a goal starts with deciding on the theme that is considered challenging, e.g. motivational problems, poor mathematics results, inequity. Assumptions, feelings, prejudice and other types of ideas that have not yet been confirmed about the theme usually play a role in determining the need for change. Once it is agreed that change is needed – and an analysis of relevant data confirms this need – it is important to formulate a more focused, clear and measurable goal to guide further activities and ensure we can evaluate their success at the end of the process.

2. The second step is to develop an idea to achieve the goal by investigating, based on data and/or research evidence from the professional and scientific literature:

• the cause(s) of problems with regards to current student outcomes, as well as the factors that can help to achieve the goal (Box 8.1)

• what needs to be learnt to achieve the goal, not only by students, but first by teachers to change their practice.

Box 8.1. From data to taking action

Data about the causes of the problem and taking action

A school has identified student performance in reading as a problem. Teachers at the school have always assumed student age (i.e. an increasing number of younger students enrolling) to be the cause of the problem. The data show, however, that (younger) age and disappointing performance are unrelated. Participants study the literature to find more probable causes and collect related data, in this case, about students’ conditional knowledge (i.e. knowledge of letters and naming speed) in the year before the performance problem is apparent. They find a relationship and develop an intervention focused on conditional knowledge for reading.

3. The next step, taking action to solve causes of problems and/or positively influence factors that help achieve the goal is key:

• Including thinking about who is/should be involved and how to secure their engagement.

• Considering what activities and resources are needed and when.

4. To complete the conversation, evaluating process and outcomes, as well as reflection, is vital to answer the following questions:

• Were the actions implemented as intended; how did students, teachers and possibly other stakeholders experience them?

• What have learning conversation participants learnt together and as individual members?

• How effective were the actions: i.e. has the goal, in terms of student outcomes, been achieved?

• Do the actions or goal need to be adapted? If the goal has not yet been achieved, for instance, the implementation of actions might need to be adjusted. If the goals have been achieved sooner than expected, should a more ambitious goal have been set?

In relation to this fourth and final stage, it is clear that underpinning any approach to evaluation is being able to measure change in relation to our goals.

Real-life examples: Data teams and research learning networks

Learning researchers have developed many guidelines and protocols to structure such learning conversations and ensure they can help teachers make sense of various forms of evidence to drive real changes in student learning (Brown, 2018[5]).

The first example of structured learning conversations is data teams (Schildkamp, Poortman and Handelzalts, 2018, p. 232[7]). Data teams consist of four to six teachers and one to two school leaders (and possibly students) who use eight steps to structure data team conversations to foster teachers engaging in deep forms of enquiry (see Schildkamp et al. (2018[8])). In the first step – problem definition – participants gather and analyse data to establish the extent of the problem and the desired goal. Such data can be about both student well-being (e.g. survey results) and student performance and learning (e.g. mathematics test results; passing percentage in a certain grade). Some schools administer student and parent satisfaction or well-being surveys on a yearly basis. In addition, schools often have student achievement data available that they should not only use for accountability purposes but also for educational improvement in learning conversations. Next (Step 2) comes developing hypotheses (or a research question) regarding the cause of the problem or the reason the goal is not being achieved. Both qualitative and quantitative data about student learning or well-being can be used to investigate. Examples of qualitative data are interviews with students about their learning process related to the set goal and/or students’ assignments showing potential causes of problems in their learning. Examples of quantitative data are the frequency of student absence or percentage of failure on previous tests related to the subject under investigation. Participants follow specific sub-steps for analysing data and drawing conclusions (main Steps 3-6) about these causes and use templates (and literature) to develop an action plan to solve the problem (Step 7). In the eighth and final step, data are again collected about whether the problem has been reduced or the goal has been achieved, and about the implementation process of the actions.

Another example of how learning conversations can be structured is the research learning networks (RLN) process. RLNs involve small groups of teachers coming together from a number of schools to tackle key issues related to teaching and learning. Participants attend four workshops over the course of an academic year. These workshops aim to enable participants to:

1. focus on understanding the research and current practitioner-held knowledge about the specific issues being explored and gain an understanding of what impact might look like and how (and what) to collect in order to establish the baseline (i.e. the here and now);

2. explore the baseline in more detail, develop a research-informed approach to improving practice within each school and consider how this approach might be trialled effectively;

3. trial (and, if needed, refine) their research-informed approach to improving practice and consider the idea of whole school change and how they might roll out interventions across their school;

4. consider both the impact their work has achieved and how to share knowledge of impact more widely.

The RLN and Data Team examples show that learning conversations typically also involve those not directly participating in the collaboration group. Activities also take place between learning conversation meetings. For instance, data and literature will need to be gathered in between meetings or discussed more widely with colleagues in one’s school. Moreover, reflecting on evidence, discussing it with other colleagues, using their input and applying their insights when evaluating the effects of a new intervention are all essential elements in this process. There are many other examples of approaches in addition to those outlined above [for instance, teacher design teams, Binkhorst (2018[9]); or spirals of inquiry, Kaser and Halbert (2017[10])].

Learning conversations’ effectiveness and supporting conditions

There is emerging evidence on the effectiveness of learning conversations. Learning conversations within RLNs have been shown to help teachers successfully engage with research evidence on effective pedagogical practices. They are also linked to enhanced teaching practices and improved student outcomes (Brown and Flood, 2018[11]; Brown, MacGregor and Flood, 2020[12]; Rose, 2017[13]). Reviews about data‑informed decision-making approaches show that they can be effective in terms of both student and teacher learning. Teachers learn, for example, about how to use data and how education can be improved (Marsh, 2012[14]). Moreover, various studies find a (substantial) positive effect at the student level (Marsh, 2012[14]; Spiele, Schildkamp and Janssen, 2020[15]; Grabarek and Kallemeyn, 2020[16]) and the organisation level (e.g. more collaboration among colleagues).

However, effects of learning conversation approaches in PLNs appear to depend strongly on conditions such as leadership, facilitation, data access [see the section on “Other success factors” and Schildkamp and Poortman (2015[17]); and Schildkamp, Poortman and Groothengel (in progress[18])]. The following sections summarise lessons learnt from how these approaches and activities help support effective learning conversations.

Starting with a vision of success

What is clear from our description of both data teams and RLNs is that any learning conversation has to start with an understanding of what change in student outcomes (and thus teaching practice) is required, as well as an awareness of whether this change has been realised. For example, within RLNs, developing such an understanding involves RLN facilitators taking participants through a suite of exercises. Premised on the idea that good professional development starts with “the end in mind” (Stoll, Harris and Handscomb, 2012[19]), the first exercise asks participants to imagine what the future holds in 12 months’ time. Specifically, given the problem or focus area in question – for instance, how to develop more inclusive practices for looking after students [e.g. (Poortman and Brown, 2023[20])] – RLN participants are asked to consider “what difference do you want to make?” and “what will success look like?”. They are encouraged to think deeply about: what students will be achieving and doing; how students will be feeling; what will students be saying; and how will students be responding if the new approaches participants hope to develop in relation to the given focus area prove to be effective. Participants then repeat the exercise with respect to the actions and behaviours they might engage in that would lead to this change in students. Thinking about future success this way helps participants come to a common understanding of, and a vision for, what needs to be achieved. This, in turn, helps ensure that the views of school participants are in alignment, providing a foundation for action.

Developing a deep understanding of the problem

A concrete understanding of the current situation is also required before any action commences. First, participants need to make sure the problem is worth investigating. Second, they need to understand the current situation well to know if there has been an impact in the end. Participants’ pre-existing assumptions about problems and their causes are often wrong (see Box 8.2). Therefore, the next step in the learning conversation process is arriving at a comprehensive picture of the here and now. With this step, teachers need a way of measuring the “baseline” so that they know exactly what the gap between the vision and the current situation is and, over time, whether they are closing it. Baseline data also help teachers firm up their understanding in relation to potential causes of the gap and, therefore, what interventions might serve to change the current situation. Questions to ask when thinking about collecting baseline data include:

• What data need to be collected?

• What do these data concern? Do they concern students? Your teaching practice? Your team?

• Are the data readily available or do you need to collect them?

• If you need additional data, what methods will you use and why?

In addition to the baseline, two other vital sources that can inform participants’ understanding of the problem and the common foci that might be supported are: teachers’ own knowledge and current research knowledge (produced by universities or other research organisations). With RLNs, for example, workshop protocols and exercises are used to enable participants to bring together what is known from existing research knowledge with what they know about their context, their students and what they currently see as effective practice (i.e. their experience and the experience of others) (Brown, 2018[5]). For instance, participants might be guided to discuss and record:

• an aspect of their practice that works in relation to the topic

• the absolute best practice in their school in relation to the topic

• the basis for making these statements: i.e. what’s the evidence for their claims?

After exploring challenges in relation to responding to these questions (especially the evidence for making the claims), RLN participants are presented with a literature review that sets out what is known about the focus area. The purpose of these reviews is to present research-informed principles and recommendations that can be employed as part of finding or developing solutions to the problem at hand. Following the review of literature, participants complete a “data capture” mat, a pro forma which asks participants to consider how the research and their resultant themes: connect with their own knowledge and practice; deepen their own knowledge and practice; and challenge their own knowledge and practice. In all three instances, participants refer to what was expressed in the first exercise.

Box 8.2. Vignette: The importance of data about the current situation

A school is concerned about the disappointing performance of students in the third year of secondary school. They are certain the passing rate is below par compared to the performance of third-year students nationally. Teachers consider the third-year students as unmotivated and hard to teach. However, the data they subsequently collected show that a little higher percentage of students pass to the fourth year than the national average. Further exploration reveals that performance is actually lower in the fourth year of the programme. This means that the team needs to reformulate the problem and identify potential causes for a different target group than expected. At the same time, it is quite an eye‑opener for the team that the original group of students is not as problematic as they have thought for years!

Applying and evaluating

The next thing participants must do, of course, is then use their newly created knowledge to develop an approach to teaching and learning that has an impact. This approach should then be tested, evaluated and refined. One effective approach including testing, evaluation and refinement is that of lesson study, which is widely used in Japan as a form of professional development (Cheung and Wong, 2014[21]) (Box 8.3). In general, carefully planning and communicating the implementation of the approach is crucial. Participants need to make sure that everyone has the opportunity, knowledge and materials to participate in the implementation, including teachers not directly participating in the learning conversations. The implementation process and the effects both need to be evaluated.

Box 8.3. Lesson study

As a process, lesson study involves teachers collaborating, normally in groups of three, to progress cycles of iterative practice development. Such cycles typically involve the following steps:

• discussing student learning goals and identifying a teaching strategy that might meet these

• planning an actual classroom lesson (called a “research lesson”) that employs this strategy

• observing how the lesson works in practice

• discussing and embedding revisions to enable improvement (Lewis, 2000[22]).

In addition, three students, who represent wider groups of interest, will be observed and their progress will be monitored as case studies of the impact of the approach (Dudley, 2011[23]).

Other success factors: Norms of trust and innovation

There are other factors to consider in the success of learning conversations, which interact with and influence each of the four phases described above. The first is a trusting environment within the school. Trust is critical because in learning conversations, teachers need to feel able to expose gaps in their knowledge and experiment with what emerges from such conversations (Brown, 2017[24]). Trust is also vital more generally for enabling social networks within schools to share and adopt innovation effectively (Mitton et al., 2007[25]; Sebba, Tregenza and Kent, 2012[26]; Warren Little, 1990[27]). For instance, Finnegan and Daly (2012[28]) argue that where teachers feel they do not have the knowledge or skills to challenge the introduction of an innovation, trust enables a given innovation to be widely adopted. In other words, trust helps signify that it is safe or okay to use this innovation. What’s more, higher levels of trust are significantly associated with more frequent (and useful) relationships between individuals. This benefits a variety of relationship-related efforts, including collaboration, learning, complex information sharing and problem solving, shared decision making, and co-ordinated action (Bryk et al., 2010[29]; Tschannen-Moran, 2004[30]).

A second key factor is the existence of any historical norms regarding innovation and adoption generally (Rogers, 1995[31]). As Warren Little (1990, p. 530[27]) notes, the likelihood of new innovations influencing individuals will rest, in part, on their congruence with established behaviours regarding the adoption of “the new”. Schools particularly attuned to innovation are sometimes referred to as learning organisations. The OECD (2016[32]) publication What Makes a School a Learning Organisation? suggests that schools operating as learning organisations are viewed as having a dynamic, adaptive culture for change. Within this culture a range of strategies can be accessed to address the needs of the particular school community and, ultimately, the learning needs of all students. Linked to the trust factor mentioned above, learning organisations also place an emphasis on the development of professional relationships, which build a school climate of trust and co-operation (Silins and Mulford, 2004[33]). It is likely to be easy to broker innovations within innovative school cultures or within learning organisations.

However, even if such a culture does not yet exist, it can be promoted by school leaders. For instance, school leaders can extoll the benefits of innovative ideas and normalise experimenting with new ways of working (Leithwood et al., 2006[34]). An innovative culture can also be promoted by modelling an “inquiry habit of mind”. This involves senior leaders actively and visibly seeking out a range of perspectives to help them address given issues; purposefully seeking relevant information from numerous and diverse sources; and continually exploring new ways to tackle perennial problems. Likewise, school leaders need to make the assumptions underpinning proposed new practices explicit so they can be challenged and improved (Schildkamp and Ehren, 2012[35]). School leaders also need to create an environment that enables new practices to be trialled, evaluated and refined (Datnow, Park and Lewis, 2013[36]). School leaders should therefore put in place structures for knowledge to be shared. This includes making available and co‑ordinating time (and related processes) to enable teachers to discuss new approaches to practice.

Third, consideration needs to be given to the cultural norms regarding the specific type of innovation: for instance, whether “formative assessment” is currently standard teaching practice (Rogers, 1995[31]). If the innovative practice is totally distinct from what has happened previously, evidence suggests that a number of factors will be more likely to influence its adoption. These include: the context of the school; wider pressures and forces shaping the environment in which the schools are situated; the resources available to the school; the capacity and capability of the staff within the school; practical aspects of implementation, such as existing routines; and current norms within the school (Neal et al., 2019[37]; Koutsiuris and Norwich, 2018[38]).

And now the irrational: Emotions

There are some aspects that might be less immediately apparent but will still affect the success of learning conversations. One such factor is the role of emotion. The field of art and design provides useful insight into how emotion might be used to facilitate (or indeed hinder) learning conversations. Leading design academic, Donald Norman, argues that “the emotional system is a powerful information processing system that determines whether a situation is safe or threatening, whether something that is happening is good or bad, desirable or not” (2013, p. 47[39]). In tense and threatening situations, the emotional system will trigger the release of hormones that bias the brain in preparation for action. In calm, non-threatening situations, the emotional system triggers the release of hormones that bias the brain towards exploration and creativity (Norman, 2013[39]). A positive emotional state is, therefore, ideal for reflective thought, while a brain in a negative emotional state provides focus: precisely what is needed to maintain attention on a task and finish it.

This perspective links nicely with the educational perspectives provided by Schildkamp and Datnow (2020, p. 18[40]), who argue that the way in which practitioners view the purpose of learning conversations is vital. In particular, efforts focused on accountability are far less fruitful than those focused on continuous improvement or equity, which are far more likely to lead to educational policies and practices that expand students’ opportunities to learn. Schildkamp and Datnow (2020[40]) also link such outcomes to emotion. They suggest that when teachers have negative experiences with learning conversations, such as shaming and blaming, or feel that their time is being wasted, they are far less likely to be engaged. Positive experiences, on the other hand (for example, working with a productive team that is delving deeply into learning), are likely to encourage teachers to become more engaged and, in turn, more reflective (display higher levels of depth of inquiry).

Cycles of inquiry and learning conversations

While the four key phases described above might seem straightforward, in reality, the process is not neatly defined nor linear and will be constantly buffeted by environmental factors as people come and go and contexts evolve. The process may involve going back and forth between steps to fine-tune ideas. New insights might require adaptations of the original goal, ideas and actions that participants had previously agreed upon. Coming together regularly for a sufficient amount of time is also essential to enable an intensive learning process. In terms of organisation, many variants of learning conversations are possible as long as the approach fits the context. For example, RLNs use a four-workshop approach with four four‑hour workshops spread over a period of a year (or even a more intensive eight-workshop model, if time allows). The data team approach typically involves meetings every three to four weeks over a school year period (Schildkamp et al., 2019[41]). Nevertheless, research suggests that longer term professional development with a larger number of hours works better than short-term and less intensive approaches (Yoon et al., 2007[42]; Van Veen et al., 2010[43]). While we recognise the constraints of teachers’ time, the models described in this chapter (e.g. RLNs, data teams) offer sufficient time to engage participants in a learning conversation effectively.

We finish by spotlighting an important additional purpose of learning conversations. The learning conversation process is not only about finding “the” right solution as soon as possible (Schildkamp and Poortman, 2022[44]). It is also about learning how to address educational problems and realise ambitions in an evidence-informed and contextually meaningful way. This collective learning outcome enables teachers to address new issues meaningfully. Therefore, to achieve sustainable school improvement, this process should be systematic and continuous; with new cycles of inquiry enacted in relation to new problems and ambitions.

Conclusion: Learning conversations and policy

Policy makers are increasingly interested in stimulating teacher professional development in learning conversations (in PLNs). However, teachers do not always feel they have the opportunity to develop the knowledge and skills necessary for effective learning conversations in the longer term. A vision for the role of learning conversations in educational improvement at the level of the school, the school board or district, and the national level is an important condition in this respect. Moreover, room to experiment (particularly, in phases 3 and 4) and implement actions for sustainable school improvement is key. This requires sufficient meeting time but also sufficient opportunities. The wider education policy context, including teacher policies and accountability frameworks, can influence these opportunities both positively and negatively. If we expect teachers to improve education in an evidence-informed way, systemic incentives and conditions need to be in place.

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[17] Schildkamp, K. and C. Poortman (2015), “Factors influencing the functioning of data teams”, Teachers College Record, Vol. 117/4, pp. 1-42, https://doi.org/10.1177/016146811511700403.

[41] Schildkamp, K. et al. (2019), “How school leaders can build effective data teams: Five building blocks for a new wave of data-informed decision making”, Journal of Educational Change, Vol. 20/3, pp. 283-325, https://link.springer.com/article/10.1007/s10833-019-09345-3.

[18] Schildkamp, K., C. Poortman and P. Groothengel (in progress), “Data-informed decision making and learning analytics: From data to educational improvement”.

[7] Schildkamp, K., C. Poortman and A. Handelzalts (2018), “Data teams for school improvement.”, School Effectiveness and School Improvement, Vol. 27/2, pp. 228-254, https://doi.org/10.1080/09243453.2015.1056192.

[26] Sebba, J., J. Tregenza and P. Kent (2012), Powerful Professional Learning: A School Leader’s Guide to Joint Practice Development, National College for School Leadership, Nottingham.

[33] Silins, H. and B. Mulford (2004), “Schools as learning organisations – Effects on teacher leadership and student outcomes”, School Effectiveness and School Improvement, Vol. 15/3-4, pp. 443-466, https://doi.org/10.1080/09243450512331383272.

[15] Spiele, S., K. Schildkamp and J. Janssen (2020), “Do data use professional development interventions for teachers impact student achievement? A meta-analysis”, paper presented at the 2021 Virtual ICSEI Conference.

[19] Stoll, L., A. Harris and G. Handscomb (2012), Great Professional Development Which Leads to Great Pedagogy: Nine Claims from Research, National College for School Leadership.

[30] Tschannen-Moran, M. (2004), Trust Matters: Leadership for Successful Schools, Jossey-Bass, San Francisco, CA.

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[6] Vangrieken, K. et al. (2017), “Teacher communities as a context for professional development: A systematic review”, Teaching and Teacher Education, Vol. 61, pp. 47-59, https://doi.org/10.1016/j.tate.2016.10.001.

[27] Warren Little, J. (1990), “The persistence of privacy: Autonomy and initiative in teachers’ professional relations”, Teachers College Record, Vol. 91/4, pp. 509-535, https://doi.org/10.1177/016146819009100403.

[42] Yoon, K. et al. (2007), Reviewing the Evidence on How Teacher Professional Development Affects Student Achievement: Issues & Answers, rel 2007, No. 033, Regional Educational Laboratory Southwest (NJ1).

[2] Zala-Mezö, E. and J. Egli (2022), “What exactly happens during team meetings? Introduction of an analytical instrument and an overview of the results”, paper presented at the 2022 Virtual ICSEI Conference, January 2022.

Note

← 1.  For more information and practical guidelines about PLNs, refer to The Teacher’s Guide to Successful Professional Learning Networks, and the sample Chapter available here: https://cloud.3dissue.net/14552/14572/14643/93270/index.html?44335.