The critical stance of my keynote there seemed to resonate with delegates, who hear a lot about “Big Data” and analytics, but have reservations about the kinds of learning that such technologies may perpetuate. I sought to deconstruct analytics to clarify the ways in which an approach and how it is used embodies an educational worldview. Knowing this, what kinds of learners are needed for 21st century society, and what role can analytics play in advancing this mission?
Part of this emerging picture is what we’re focusing on here at LearningEmergence.net — redefining metrics that value qualities in the learner that many are talking about, but which are hard to evidence.
Abstract: Education is about to experience a data tsunami from online trace data (VLEs; MOOCs; Quantified Self) integrated with conventional educational datasets. This requires new kinds of analytics to make sense of this new resource, which in turn asks us to reflect deeply on what kinds of learning we value. We can choose to know more than ever about learners and teachers, but like any modelling technology or accounting system, analytics do not passively describe sociotechnical reality: they begin to shape it. What realities do we want analytics to perpetuate, or bring into being? Can we talk about analytics in the same breath as the deepest values that a wholistic educational experience should nurture? Could analytics become an ally for those who want to shift assessment regimes towards valuing the qualities that many now regard as critical to thriving in the ‘age of complexity’?
Learning to learn is both a process and an outcome of formal education, along with other trans-disciplinary and life-wide competences. It goes deep into pedagogy and practice and is influenced by culture and context. As an outcome, it is a competence we aspire to measure and celebrate.
Learning how to learn is a crucial competence for human flourishing in 21st century conditions of risk and uncertainty. It is one of eight key competencies identified by the European Union as a key goal within the Lisbon and the 2020 strategies (European Council 2006). The European Union maintains a keen interest in this topic as demonstrated by the European network of policy makers and several working groups on key competencies, including the creation of the European Network on Learning to Learn (Hoskins & Fredriksson, 2008). Internationally, learning to learn is emerging as a focus for school improvement and as a foundation for lifelong and lifewide learning. UNESCO includes approaches to learning as a key domain which should be an entitlement for all children, and one which needs to be assessed.
There is a real need for serious debate about the term ‘learning to learn’ which is frequently used in different ways and in different contexts without clear definition. Often it is used within a conceptually narrow framework, limited to “measurable” study strategies and learning styles (OECD 2009) for which there is little evidence of success. There is an urgent need for a research validated foundation for learning to learn and what constitutes it.
Practitioners, university lecturers, teachers and schools around the world are interested in their students becoming able to take responsibility for their own learning and achievement – and for this they need to learn how to learn. Existing funds of knowledge are all ‘out there on the internet’ and what matters is how individuals and teams make sense out of and utilise the mass of information which bombards them every day. Dialogue between research and practice is crucial to underpin this movement, generating a discipline of research-informed practice which frames and informs both commercial and policy interests. In the absence of a ‘pensee unique’ the global community of scholarship in education provides an important voice which should make a healthy, collaborative contribution to the formation of policy and practice.
Assessment of competence in learning to learn is a critically important policy ideal – one which the European Union embraced and embarked upon with Learning to Learn working group. After some serious effort we came to the conclusion that there are so many different approaches to learning to learn from across the EU, that it was impossible in 2007 to arrive at a consensus in its measurement. Before we can ever effectively assess something we need to know exactly what it is we are measuring – as a matter of professional ethics. We also need to know what measurement models are most suitable and what is the purpose of the assessment before we develop our assessment technologies. This book was conceived by people who participated in that EU project and, we hope, in an important way it keeps the dialogue alive.
Complexity and Learning to Learn
Learning to learn is a complex process rather than either a simple or even a complicated one. Demetriou’s chapter explores an architecture of mind that incorporates four inter-related systems all of which may be relevant to learning to learn. Each contributor proposes a complex mix of processes that coalesce into learning to learn – including affective, cognitive and dispositional factors. All agree that learning to learn is about the promotion of self-directed learning, the cultivation of intrinsic motivation for learning and the development of intentional agency on the part of the learner. All agree that contextual factors – such as pedagogy, assessment regimes, quality of relationships and socio-cultural factors – together interact and influence the ability of an individual to learn how to learn and to become an agent in their own learning journey. Learning to learn is messy and complex.
The implications of this complexity are enormous. As Edgar Morinargues (and Jung before him), Western thought has been dominated by the principles of disjunction, reduction and abstraction. Engaging with learning to learn as a complex process requires a paradigm of distinction-conjunction, so that we can distinguish without disjoining and associate without identifying or reducing. In short we need to develop new and more holistic ways of understanding, facilitating and enabling learning to learn in our education communities, so that we can hold in tension the inner personal aspects of agency, purpose and desire and dispositions and the more measurable external and public manifestations of learning and performance and collaboration with others in learning to learn. We need measurement models that can account for quality of trust as a core resource, and story as a vehicle for agency as well as the more traditional and familiar measures of performance and problem solving.
Becoming self-organising agents in our own lives
If learning to learn is about human beings becoming self-organising agents of their own lives, as our contributors suggest, then it is clear that ‘top down’, transmission oriented approaches to learning, teaching and school improvement are no longer enough. The challenge is how to create the conditions in which individual students are able to take responsibility for their own learning over time. By definition, this cannot be done for them. It has to be by invitation, allowing learning to learn to emerge and fuel agency and purpose.
The establishment of the framework for international comparison of educational achievement provided by the OECD through the Program for International Student Assessment (PISA) and the means for regularly compiling the data is a considerable achievement. It has provided an evidence base for Governments to inform domestic educational policy and against which to allocate priorities. What this data set is less effective at revealing are the reasons behind international and regional difference: we still understand too little about what drives these broad numbers. Furthermore the numbers continue to reveal deep, intractable challenges in education such as embedded disadvantage linked to geography, economics and ethnicity.
There is a pressing need to assemble an internationally comparable set of data which can better inform our understanding of factors such as learning how to learn and how this varies within and between different contexts. The academic and theoretical work that has been undertaken on these issues to date, while rich and deep, has focused on aspects of the problem, often failing to cross disciplinary boundaries. The real world challenge of educational improvement, meanwhile, is relentlessly trans-disciplinary, involving a complex interplay between social, institutional and individual factors. It presents a challenge both to theory and practice. The PISA data by comparison achieves comparability through the use of widely available proxy indicators but lacks the depth and resolution needed to provide an understanding of the mechanisms driving the patterns it surfaces.
What is also clear from this volume is the value of different cultures in the debate about learning to learn. Two chapters are written explicitly from an Eastern perspective – demonstrating how Confucian philosophy can enrich our understanding of learning to learn and challenging some deeply held Western assumptions. We have contributions from Australia, New Zealand, Finland, UK, Spain, Austria, China, Italy and the USA and uniquely, a set of case studies from learning to learn projects in remote Indigenous communities where the cultural differences are enormous. This is a ‘brolga’ a community metaphor for creativity for children in Daly River School, in Northern Territory.
However comprehensive, this volume does not address a number of research and practice themes or leaves unanswered questions for further research. Among these, perhaps the most relevant is the road towards the assessment of learning to learn which is a daunting endeavour – although it provides a foundation for this through its contribution in exploring what it is that should be assessed in learning to learn and why. Other open questions concern the deployment of learning to learn in school improvement; in the training of trainers, educators and educational leaders; in personal development and empowerment. The connection of learning to learn with other key competencies, such as active citizenship and entrepreneurship, also requires further study.
This book draws on a rich, global tradition of research and practice. It is written by researchers and practitioners who care deeply about education and about learning how to learn in particular. Our purpose is to generate debate, to link learning communities and to make a contribution to the ways in which societies worldwide are seeking to re-imagine their education systems. Our hope is that learning to learn will soon find a consistent place in educational policies worldwide.
Personal reflections on 2 workshops and a lecture with Tony Bryk (Carnegie Foundation for the Advancement of Teaching), hosted last week by Ruth Deakin Crick at University of Bristol. What follows after a brief introduction to the concept of NICs, are my thoughts on the intersection of NICs with Learning Analytics. I made a number of connection points between the features of the DEED+NIC approach, and learning analytics, which I’ll highlight in green.
The ideas of the human-centred computing pioneer Douglas Engelbart (dougengelbart.org) run like DNA through my work, I find so much depth of insight [see his Afterword to my book]. Doug showed the world in the 1960s many of the features that we now take for granted in our personal computing: the mouse, windows, hyperlinks, videoconferencing, direct editing of text on screen.
However, his work on making computers more intuitive as personal tools for thought was just part of his bigger vision for improving what he called our Collective IQ — humanity’s capacity to tackle “the complex, urgent problems” we face by working more effectively together.
“A” represents how the organization or community goes about its core business or mission; “B” represents the process by which it improves its core business activity (through the efforts of individuals and improvement communities); an Improvement Alliance is a “C” activity; “C” is any activity that improves “B” activity. By definition, improvement communities operate at the B and C levels. Conversely, any time more than one person is involved in a B or C activity, it’s an improvement community. An important function of “C” is to network improvement communities within and across organizations, forming a C level improvement community, aka “C Community” or “Improvement Alliance” of representative stakeholders from a variety of B activities. Organizations can also join forces at the C level to create a more robust C function, forming a super Improvement Alliance.
Many people have explored and trialled this concept, experimenting with a range of technologies and ways of working that are designed to make evidence-based advances on complex problems. Eductional examples of particular relevance include the Carnegie Foundation’s DEED methodology and Alpha Labs, University of Bristol’s dispositional analytics research programme, the Learning Emergence network and its Evidence Hub, and the many Collaboratories for distributed research communities.
Tony Bryk used this alternative figure to show how “B” improvement clusters seeking to improve frontline “A” activities can themselves network to create a level “C” NIC:
Bryk and Gomez have documented the rationale behind their educational improvement science strategy in detail [pdf]. Their concept of an educational NIC cannot be applied to any collective, but comes with some distinctive features which I summarized as follows in the Edu-NICs workshop I ran:
Scavenging from healthcare improvement science
In his workshop and public lecture, Tony Bryk described how he has ‘scavenged’ as much as he can from the healthcare profession’s adoption of improvement science, which apparently about 25 years ago was where education stands today. It turns out that it has taken two and half decades’ concerted effort by the US Institute of Health Improvement (IHI) to establish a new professional discipline, working on translating innovation into scaleable practice. Healthcare shares with education a very similar gulf between academic scientific research and its reward systems, and the translation of insights into scaleable practice on the frontline.
Bryk pointed us to the work of Atul Gawande, who concluded his TED talk (18:10):
“Making systems work” is the great task of our generation for health, education, climate change, poverty…
This vignette from a UK Health Foundation movie shows how the concepts such as practice-based learning, collective intelligence and evidence-informed practice are now embedding, although, of course, nobody is declaring “mission accomplished”. Swap the words maternity care/hospitals/doctors/patients with education/universities/educators/learners — and it still all makes sense:
Bryk’s call to action is that within education, there is precious little of this systematic, systemic, intentional improvement methodology to be found. Education is still stuck where healthcare was, with a fixation on the scientific paradigm for truth, grounded in randomized controlled trials. Instead a new methodological paradigm is needed whose core question is not simply What works? in an isolated context, but How do we replicate and scale what works? across contexts. This is not because we can hope for ‘one size fits all’ solutions — quite the opposite — because we understand how important certain contextual factors are to the embedding of that innovation:
Analytics implication? By extension, the challenge of improving education applies to learning analytics (which are after all, new kinds of tools for supporting different kinds of pedagogy and assessment). Learning analytics faces the same challenge of bridging the gulf between academic research and frontline practice, and generalizing findings. As success and failure stories in the field emerge, there is exactly the same need to try and understand the contributing contextual variables. A distinguishing feature may be that learning analytics contains the seeds for its own success, in this regard, since computational and statistical approaches to identifying the most predictive variables from large datasets could be used to advance the field’s own Level C learning — not just the learning of the students being tracked at Levels A and B.
Implications for ICT
Moving towards thinking about opportunities for ICT to add value, I summarised a set of functional roles as follows:
It is no coincidence that the above defines a socio-technical infrastructure not only for professionals seeking to advance their field, but also for scaffolding students in authentic, collaborative inquiry. Given the challenges we face, at many societal scales, we need to train the next generation more effectively to design inquiries, make sense of complex, heterogeneous scientific and practitioner data, from multiple perspectives and epistemic traditions, via a diversity of human and computational tools, as well as learning the skills of collaborative knowledge negotiation and community facilitation in the role of ‘hub’ catalysts.
I then stepped through this cycle, as detailed in these slides:
The remainder of this note focuses on the role of analytics.
Implications for Learning Analytics
Understanding the interplay between different levels in complex systems
In a special issue devoted to complexity science, social science and computation, colleagues documented the frontline challenges that need to be tackled in modelling complex social systems, among them, multilevel dynamics: how different levels, and systems of systems, influence each other. For computational social scientists seeking to simulate a social system formally in order to understand its structure and dynamics, this is a basic research frontier. We are not so ambitious as to want to simulate the social richness of schools or courses, but the challenge of understanding how the macro and micro shape each other is at the heart of the difficulty of educational reform, and the challenge of creating what Bryk calls “practical theories and methods” which are robust enough to make the journey from academia to the front line, negotiating all the constraints of politics and practice on the way.
The learning analytics community recognizes the different levels of data and analytics that are now in play within educational systems, but has no good accounts yet of how these influence each other. George Siemens and Phil Long introduced this diagram to distinguish learning analytics that attend to fine-grained patterns in learner behavior from academic analytics that focus on the more static demographics and periodic course outcomes of interest to strategic decision makers in institutions:
In my own attempt to summarise the levels, I used micro/meso/macro terminology, and hinted at how the levels may start to inform each other:
Macro-level analytics seek to enable cross-institutional analytics, for instance, through ‘maturity’ surveys of current institutional practices or improving state-wide data access to standardized assessment data over students’ lifetimes. Macro-analytics will become increasingly real-time, incorporating more data from the finer-granularity meso/micro levels, and could conceivably benefit from benchmarking and data integration methodologies developed in non-educational sectors (although see below for concerns about the dangers of decontextualized data and the educational paradigms they implicitly perpetuate).
Meso-levelanalytics operate at institutional level. To the extent that educational institutions share common business processes to sectors already benefitting from Business Intelligence (BI) methods and technologies, they can be seen as a new BI market sector, who can usefully appropriate tools to integrate data silos in enterprise warehouses, optimize workflows, generate dashboards, mine unstructured data, better predict ‘customer churn’ and future markets, and so forth. It is the BI imperative to optimise business processes that partly motivates efforts to build institutional-level “academic analytics”, and we see communities of practice specifically for BI within educational organisations, which have their own cultures and legacy technologies.
Micro-level analytics support the tracking and interpretation of process-level data for individual learners (and by extension, groups). This data is of primary interest to learners themselves, and those responsible for their success, since it can provide the finest level of detail, ideally as rapidly as possible. This data is correspondingly the most personal, since (depending on platforms) it can disclose online activity click-by-click, physical activity such as geolocation, library loans, purchases, and interpersonal data such as social networks. Researchers are adapting techniques from fields including serious gaming, automated marking, educational data mining, computer-supported collaborative learning, recommender systems, intelligent tutoring systems/adaptive hypermedia, information visualization, computational linguistics and argumentation, and social network analysis.
As the figure shows, what we now see taking place is the integration of, and mutual enrichment between, these layers. Company mergers and partnerships show business intelligence products and enterprise analytics capacity from the corporate world being integrated with course delivery and social learning platforms that track micro-level user activity. The aggregation of thousands of learners’ interaction histories across cohorts, temporal periods, institutions, regions and countries creates meso + macro level analytics with an unprecedented level of fine-grained process data (Scenario: comparing similar courses across institutions for the quality of online discourse in final year politics students). In turn, the creation of such large datasets begins to make possible the identification and validation of patterns that may be robust across the idiosyncrasies of specific contexts. In other words, the breadth and depth at the macro + meso levels add power to micro-analytics (Scenario: better predictive models and feedback to learners, because statistically, one may have greater confidence in the predictive power of key learner behaviours when they have been validated against a nationally aggregated dataset, than from an isolated institution).
Example: Bryk reported that their Statway developmental mathematics initiative can triple the success rate of current programmes, in half the time. However, the next step is not merely to promote its success, publish, hope others pick it up, and move on to next thing. Bryk emphasised the need to look at the variation, and ask why did one school fail dismally? What can we learn? It turned out that success was dependent on the presence of certain kinds of staff. In Improvement Science, “failure is a treasure”. That’s counter-cultural to most kinds of research where one always hopes for success, and requires a bigger frame of reference which values the understanding of contextual variables, and expects failure.
What I think we see with Bryk’s work on the DEED methodology is a mechanism by which we can build knowledge about how the micro/meso/macro layers of an educational system interact — the arrows in the figure. Since local context matters, micro-level results should be passed ‘up’ the levels in order to pool data, detect patterns, and interpret why things are breaking/working, in order to then make more effective interventions back ‘down’ in local contexts. The data explosion coming from the new kinds of micro-level learning analytics must be escalated and interrogated for higher order systemic learning, so that successful analytics interventions can be adapted and replicated for other contexts.
Seeing the system
Central to Bryk & Gomez’s conception of a NIC are shared representations, which help orient the collective to the nature and scope of the problem, candidate solutions, and criteria for success. Essentially, we’re talking about maps that help people know which piece of the jigsaw they are working on. As a collective builds common ground in language and terminology, they may be able to map the system in a way that serves as a common reference point (a boundary object in Leigh Star’s terms). One example would be:
In a collective intelligence NIC platform designed to support the emergence of a community aligning themselves to such a map, we would then expect that these maps can serve as navigational aids around the knowledge space:
This is scaffolded, for instance, in the Evidence Hub platform, e.g. click on this image to see how the Hub’s building blocks (Issues, Claims, Supporting and Challenging Evidence, People, Organizations, Projects)interconnect around a given central theme:
As the NIC builds its knowledge, one wants to know the state of the debate, and open issues, e.g. What evidence-based claims can we make? In what context does this approach work? Who is working on this problem in a Muslim context? etc. A NIC platform should serve as an analytics hub, generating views from the aggregated data flowing to it from the many local experiments. Two examples are a Knowledge Tree and an Argument Map:
The DEED methodology introduces educational leaders to some of the most common problem structuring representations in business analysis, such as Fishbone (Ishikawa) Diagrams and Driver Diagrams.
In this method, the Fishbone is used to map how the team is defining the system to be improved, e.g.
Systems thinkers and engineers do of course bring a well tested armoury of representational schemes and support tools to the task of evolving a picture of the system in a participatory way. Bryk has simply found the ones shown here to be simple and effective when working with educators, but I doubt he would exclude the relevance of other schemes.
Mapping the drivers
Focal areas of such a system picture are then selected for intervention, based on the best available knowledge of what drives a desired Aim:
For instance, there is an Alpha Lab NIC targeting Productive Persistence in student mathematics, using the following driver diagram:
This is itself a distillation of a significant, complex research literature (identifying many variables from many survey tools) into a “Practical Theory” that practitioners can work with. Expanded slightly, it looks like this, showing candidate interventions to be tried, and the sources of evidence underpinning them:
Zooming in on the right hand Change Ideas column, we see candidate interventions:
Within the Open University, we are developing a similar approach to justifying why we think an intervention will pay off, and how it will be tracked. In the figure below, a given row in the matrix represents a student experience intervention, and the columns specified a range of metadata which included: data sources required, time windows for expected impact, who was responsible, and the behavioural measures which would be tracked in order to evidence impact (or lack thereof). As shown in the figure below, one would want the Rationale and Outcome cells in the matrix to have some backing stronger than a hunch. They could link out to a living document of some sort where we build our collective understanding of what works, what doesn’t, and why we believe this.
The Hub could take many forms, from an internal spreadsheet/wiki, to a Driver Diagram, possibly organized in a purpose designed knowledge-building platform like the Evidence Hub, or its descendant the Impact Map.
So, the progress we are making here, is to encourage the representation of the working theory about why certain interventions (Change Ideas) may have an impact on the desired learning behaviour. Once Change Ideas are coupled with one or more learning analytics, one has created a rapid feedback loop. This is essentially a methodology and design rationale for the selection and orchestration of analytics based on the strongest practitioner and scientific evidence available at the time to that team: this is their local collective intelligence, incomplete or possibly even wrong to start with, but being refined by being passed to higher levels of sensemaking in the NIC, perhaps borrowing from and adapting other teams’ theories: a broader, deeper form of collective intelligence.
Analytics-powered Driver Diagrams: Perimeta System Models
We have been having an extraordinarily fruitful collaboration with colleagues in the University of Bristol Systems Centre, who recognise the pivotal role that a disposition to learn has in the design of solutions to multi-stakeholder, wicked, socio-technical problems. They have developed a methodology, algorithm and support tool called Perimeta for modeling complex systems, in the explicit recognition that uncertainty is inherent in decision-making. However, it is vital (1) to see both supporting and challenging evidence of progress, and (2) it also pays to know what one doesn’t know.
As detailed in a Learning Emergence technical report, in which the approach was piloted in a schools context [pdf], of the many systems thinking approaches available, one of the most appropriate for supporting collaborative development and leadership decisioning in complex systems such as learning communities, is hierarchical process modeling, which has three important characteristics:
Visual/ effective reporting of complex ideas and information is enhanced using hierarchical mapping of processes and an ‘Italian Flag’ model of evidence;
Assimilating all forms of evidence – data, prediction and opinion; and
Facilitating access to key information required for informed discussion, innovation and agreement.
Perimeta supports collaborative development of solutions to complex problems by providing a highly visual interface for understanding complex cause-and-effect and complex evidence. Perimeta can be described as:
a learning analytic designed to model diverse and complex processes
driven by stakeholder purpose
capable of dealing with hard, soft and narrative data in evidence of success, failure and ‘what we don’t know’
a visual environment for sense-making
a framework for self-evaluation and dialogue
The key point to make is that this hierarchical process model is essentially a Driver Diagram in terms of Bryk’s work, a working theory of what factors contribute to desired outcomes:
The difference is that this Driver Diagram is ‘executable’, since HPM provides a way to aggregate different kinds of evidence being gathered at the ‘leaves’ of the branches, resulting in a kind of analytics ‘dashboard’:
Recognising the uncertainty inherent in most data, the Perimeta model adopts an ‘Italian Flag’ visual to represent the quality of all of the evidence and consisting of:
‘Green’ representing the strength of positive evidence
‘Red’ representing the strength of negative evidence
‘White’ representing lack of evidence, or uncertainty (the ‘white space’ awaiting exploration)
The evidence can be sourced from many places, but must be mapped into a weighting table. For instance, to map from responses to a Likert scale survey tool, HPM uses the following:
So to conclude an extremely fruitful collision of research programmes, two points:
We can envisage combining Driver Diagrams sourced from the literature (cf. the Productive Persistence figure), with DDs sourced from staff practitioner knowledge about local conditions, in order to design analytics which contribute to a system-wide Perimeta model, which is used to monitor the health of the system as a whole.
Hierarchical process models such as the above provide a way to create a more wholistic set of analytics: a way to quantify the wider range of educational outcomes that institutions value, adding a systems-level view to the many kinds of micro-level analytics now being developed.
The agenda to develop a wholistic conception of the learner and citizen, and analytics fit for such a purpose, is now building momentum as a wider network of people connect with each other. I’d recommend the ongoing series of Reinvent the University for the Whole Person video roundtables as a great way to tune in. . .
‘Making Systems Work – whether in healthcare, education, climate change, or making a pathway out of poverty – is the great task of our generation as a whole’ and at the heart of making systems work is the problem of complexity.
Prof Tony Bryk, President of the Carnegie Foundation for the Advancement of Teaching, spent a week with people from the Learning Emergence network, leading a Master Class for practitioners, delivering two public lectures and participating in a consultation on Learning Analytics Hubs in Networked Improvement Communities (background). A key idea is that in order to engage in quality improvement in any system, we need to be able to ‘see the system as a whole’ and not just step in and meddle with one part of it.
This is an approach which links ‘top down’ measures of performance with a ‘bottom up’ approach to organisational improvement, including all stakeholders in understanding and analysing the problem and developing shared ‘aims and purposes‘. Having identified a ‘high leverage’ problem for improvement and a community generated driver diagram, attention is focused on processes which need improvement and will contribute to achieving the shared purpose. Commitment to a common measurement model and multiple rapid prototype interventions which proceed as part of a shared network of improvement, enable the networked community to ‘learn fast, fail fast and improve fast’.
These slides are from Professor Bryk’s public lecture…links on this page will take allow you to access some more practical slides from the Master Class.
six principles of networked improvement communities
1. Make the work problem-specific and user-centered. 2. Variation in performance is the core problem to address. 3. See the system that produces the current outcomes. 4. We cannot improve at scale what we cannot measure. 5. Anchor practice improvement in disciplined inquiry. 6. Accelerate improvements through networked communities.
Learning Analytics for NICS
The social learning infrastructure required for a successful Networked Improvement Community is both organisational and virtual. Learning analytics and virtual learning networks can rapidly speed up the process of sharing learning and feedback of data from prototypes and enhance the speed and quality of improvement. A workshop on Educator-NICs: Envisaging the Future of ICT–enabled Networked Improvement Communities shared current knowledge and know how providing an exciting vision for the future of learning analytics (leading to these reflections on Bryk’s work and learning analytics).
Tony Bryk: the extraordinary time we live in. Any one of these alone is tough. We need to improve all 3 at once! pic.twitter.com/mS0K6RdpZV
The Southern Educational Leadership Trust, The Hampshire Teaching Schools Alliance Bath Spa University, The Cabot Institute, University of Bristol The International Centre for Infrastructure Futures, The Graduate School of Education, University of Bristol.
Tony Bryk was a leading figure in the Consortium on Chicago School Research (CSSR). Over twenty years they developed a theoretical and empirical framework which is holistic, participatory and based on understanding that “schools are complex organisations consisting of multiple interacting sub-systems. Each subsystem involves a mix of human and social factors that shape the activities that occur and the meaning that individuals attribute to these events. These social interactions are bounded by various rules, roles and prevailing practices that, in combination with technical resources, constitute schools as formal organisations. In a simple sense, almost everything interacts with everything else”. (2010: 45). Bryk et al went on to identify essential school supports – agents, processes and structures – which were characteristic of improving schools, as measured by student engagement in learning and achievement. Each of these supports, stimulated by leadership, focus on dynamic processes of change and learning and need to be implemented tenaciously and attended to as a whole. They provide an explanation of how the organisation and relational dynamics of a school, including parents and community, interact with work inside its classrooms to advance student learning. Professor Tony Bryk Summary
Professor Bryk’s work in Design Educational Engineering and Development as a framework for sustainable improvement in schools has inspired the Hampshire Teaching Schools Alliance in their project ‘Deep Learning Across Transitions’.