Reengineering Education
This is the first article in a series on reengineering education — as a path forward to modern, effective, coherent learning ecosystems.
Education is the foundation to a free, literate, and thriving society. Stated more completely, access to free, equitable, and quality learning environments at scale is the foundation not just for a healthy and thriving society, but for healthy, thriving individuals in that society. It is a promise we should be able deliver on for all young people. Yet delivering on that promise has proven challenging, and doing so in our rapidly evolving world will require us to dramatically rethink what that can — and should — look like. How do we rethink the environments, the experiences, and then the systems structures we use to deliver better learning at scale?
How might we reimagine what high quality, equitable, and meaningful learning experiences and environments look like in our rapidly shifting world? This question has been asked for quite some time, and we in fact have many good answers. The past several decades have produced a wealth of research and innovation in the learning sciences and learning technologies to help point the way towards delivering on this promise. Now more than ever, we have a broad range of innovative curricula, technologies, pedagogies, and models of learning environments that embody the principles of deep, meaningful, and powerful learning. Yet, the vast majority of these innovations have not scaled into the everyday experience of learners, and we still largely have education systems that in many dimensions are diametrically opposed to our understanding how people learn, grow, and thrive.
This gap — this chasm — between what we know and what we actually do, can no longer be ignored. It is the challenge that most pressingly faces our children and their futures, today. As such, we must then turn our attention to the structures that create their everyday learning experiences, and how we might unravel them, to dramatically reimagine them.
EDUCATION SYSTEMS, ENGINEERED
Education systems are complex, interconnected, distributed systems, and it is the nature of this complexity and interconnectedness that has made them notoriously difficult to change (Hargreaves, Lieberman, Fullan, & Hopkins, 2014). History, and a considerable amount of research, has demonstrated that systems of education are notoriously difficult to manage well, and to reform. Decades of work on centralized, large-scale reform initiatives across a range of educational systems and countries have repeatedly shown the limited impact of these efforts on learning and achievement of young people (Fullan, 2000, 2016; Harris, 2003). Although the critical need for transformation of learning environments and systems has been well-established, the relationship between systems change and classroom practice has shown to be exceedingly complex (Levine, 2009; Smylie & Perry, 1998). Unfortunately, lack of understanding of this complexity and interconnectedness, and subsequent application of technical approaches to address this complexity has resulted in such poor impact and little transformation (Fullan, 2010).
Education systems exhibit a number of key traits that are contributing factors to this challenge — including a wide range of agents and variables, complex interrelationships, long cycles and delays in policy implementation and data collection, highly dynamic and uncertain environments, and measurement variances/shortcomings (Groff, 2013; Lave Jr & Kyle, 1968; Mital, Moore, & Llewellyn, 2014). These are classic traits of complex, dynamic systems (Sterman, 2001).
Cyberneticists and systems scientists have long understood the need for emphasizing the interactions and connectedness of the different components of a system in order to more effectively understand the structure and functions of systems and their models. As a result, cybernetics and systems engineering have helped enable transformative impact in a range of fields — outside of education.
Even as early as the mid-1960s, researchers have argued for identifying and working with this level of complexity in order to build better learning environments:
“Any attempt to meaningfully aid education decision-making must appreciate the complex environment in which such decisions take place. In other words, the various analysis tasks must occur within a framework which embraces the total system and which integrates the problem elements into a unified whole.”
Yet few programs, initiatives, or agents in education systems understand the need for this type of complexity approach — let alone have the purview or support for it (Sterman, 2001). Although from a philosophical perspective, efforts to redirect the trajectory of systems of learning began back in the early 1900s with the work of John Dewey (1916), it wasn’t until decades later that more tangible efforts had been initiated in order to produce such change. These more recent efforts have operated under many labels, most notably school reform, education reform, and whole system reform (Fullan, 2011a). Over the decades, many of these initiatives have demonstrated limited impact, and most efforts to improve learning experiences and outcomes has been focused at the level of instructional interventions and reform initiatives focused on an aspect of the system, including teacher support and training, school organization, and even district-level/community- centered efforts (Harris, 2003). Most of these efforts have focused on piecemeal or incremental reform, and are rarely organized into a comprehensive system of change (Noguera, 2017; Banathy & Jenks, 1993). With such system complexity, and disjointed reform efforts, these initiatives would be ultimately blocked or inhibited by another aspect of the system (Fullan, 2011a). In other words, we simply weren’t looking broadly enough at the system itself, and perhaps bringing the right complexity tools to bear on the problem.
Yet according to Reigeluth (1993), since the seminal publication of “A Nation at Risk” in 1983, over 150 reports have called for fundamental change rather than the traditional, piecemeal, “tinkering at the edges” approach to educational improvement (Perelman, 1988). That was in 1983. Yet here we are, nearly 40 years later, and while there are more pockets of innovation in the system, the system itself has not changed much for the better.
Reform vs. Redesign
Seymour Papert framed this dichotomy as the problem-solving approach versus the systemic approach to ‘renovating school’ (as he quipped) — identifying and trying to solve the many problems that afflict schools versus stepping back to understand how the whole system works (Papert, 2000). Similarly, this has been framed an ‘ecological’ systemic thinkers view to changing education, because this view embraces the powerful relationship between the components in and out of the system, and thus advocate for a comprehensive approach to systemic change that considers the redesign of all aspects of the system (Banathy, 1992; Squire & Reigeluth, 2000).
Michael Fullan has been one of the most successful champions of whole system change in recent decades (Fullan, 2010). Despite many promising outcomes in Ontario, even Fullan feels that the results of these efforts have fallen short in the aim of producing the type of deeper learning valued for learners today (Fullan, 2011). From a systems view, Fullan has framed this challenge well:
“…the US, for example, has a habit of breaking things into pieces — and what looks like a system is not, because the pieces are not well connected. This problem is aggravated when some of the pieces are the wrong ones to begin with. Standards over here, assessments over there, and teacher appraisal and incentives in still another box: what can be portrayed as a system (the pieces are there, and can be made to sound comprehensive) is not integrated as a coherent whole, and thus does not function ‘systemically’. Implementation then becomes a hodgepodge….“In the absence of a system mindset, individual pieces — each of which contains half-truths — are pitted against each other as vested interests bash each other with proverbial baseball bats. No one wins; the system loses every time.”
Yet despite the success of many more rigorous systems tools and frameworks in such a broad range of fields, they have been slow to be picked up in education. Since the early 1990s, Bela Banathy — also of the ‘ecological’ mindset to education systems change — had been arguing for the use of systems design approach for emerging a new education system (1992); however, the idea has gotten little traction since then.
Designing the Future at Bell Labs
Russell Ackoff is known for his tale of how Bell Labs imagined — and created — the telephone system of the future (Ackoff, 2006). It was 1951, and Ackoff was visiting Bell Labs on what happened to be a particularly important day. The vice president there had called a last minute emergency meeting of key personnel — based on his demeanor, something was very wrong. He finally approached the podium, and explained “Gentlemen, the telephone system of the United States was destroyed last night.” After much explanation, the group assembled was aghast. Once the color returned to the VP’s face, he began to laugh and explain the hoax — it was motivation to set the stage for the task they were about to engage in, and to explain that not a single one of any of the most important, current technologies being used in the modern telephone system had been developed in this century. Innovation, in this space, had been barren for quite some time. Using this context as a backdrop, he presented his teams with a challenge: “We are going to begin by designing the system with which we would replace the existing system right now if we were free to replace it with whatever system we wanted, subject to only two not- very-restrictive constraints: First, technological feasibility, meaning we cannot use any but currently available knowledge. No science fiction. We can’t replace the phone with mental telepathy; Second constraint, the system we design must be operationally viable — meaning it must be able to function and survive in the current environment.”
From this context, the design teams went on to build a range of innovations, still in use today — including the keypad dial, touch-tone phones, consumer ownership of phones, call waiting, call forwarding, voice mail, caller ID, conference calls, speakerphones, speed dialing of numbers in memory, and mobile phones. Such a “green field” approach created the possibility for innovations that might never have come about, yet have brought much advancement to end user’s experience and benefit.
Ackoff draws particular attention to what he calls ‘idealized redesign’ — a design to replace an existing system right now, if it were able to be replaced with whatever we wanted; to this he attaches three design constraints (Ackoff, 1988, p. 242):
- It must be technologically feasible — no science fiction.
- It must be able to survive in the current environment and, therefore, satisfy whatever legal, social, economic and other externally imposed constraints or regulations apply in that environment; there is no requirement, however, that the system designed be capable of being implemented.
- The system must be designed so that it is capable of learning and adapting rapidly and effectively.
The idealized redesign framing is particularly relevant for education, where there has been a recent upsurge in the application of redesign methods to rethink what modern learning environments might look like (Groff, 2013; Kern & Rubin, 2012). Yet the challenge remains that these learning environments still must thrive, and attempt to disseminate, across systems at scale.
Our mental models of what is, creates assumptions about that which might not hold true for our preferred futures. Banathy explains that “a ‘focus within’ limits our perception to designing better systems; our current educational system design is still rooted in the 19th century machine age. A design rooted in an outdated image creates more problems than it solves. Our ‘horse-and-buggy’ system of education cannot be rebuilt or restructured into a ‘spacecraft’ model.” (Banathy, 1992, p. 42)
The same redesign approach for schools, leveraged at the systems level, invites the opportunity to generate a new ecosystem and infrastructure to support the growth of such innovative learning environments.
Engineering Alternate Futures of Learning, Learning Environments, and Systems
This emphasis on design, and Ackoff’s framing with design constraints, are of particular interest because they have so rarely been brought to the systems level for how we think of [eco]systems of learning. We don’t often give ourselves the space to ‘greenfield’ at the systems levels in education. The notion has rarely showed up in discourse, and certainly less so in practice. Perhaps it is because of the gravitas to the current system. Perhaps it is because the systemic, pervasive nature of our current reality of these systems makes it difficult to imagine, and reach for, something drastically different. Perhaps it is because when we envision the ‘micro’, day-to-day experiences that we hope for learners to have, it’s not intuitive or common practice to zoom out and think about the type of radically different system structures that would be required to make that a systemic reality for all learners. Yet it is for these reasons that we must engage with these types of systems tools and realities if we ever want to arrive at a better future for learning — because as Banathy (1992) reminds us, “adjusting a design rooted in an outdated image creates far more problems than it solves.”
What visions of future realities of learning systems might we conjure? More than a century ago, John Dewey gave us visions of what a learner-centered system in a democratic society might embody (1916). As the semantic web began to come into view, Danny Hillis gave us a vision of a future where every learner has their own digital ‘Aristotle’ personal tutor — that knows where you are as a learner, where you want to go, and how to support you on that individual pathway (Hillis, 2004). Some have even suggested that we need to hold out for this reality because systems of education will never be able to catch up and transform before the AI revolution. Even if you fall into this camp, the AI revolution won’t inherently create this vision of reality, and it doesn’t mean every learner will even necessarily understand how to engage with it well. Getting to better visions of learning will require that we not only design, but that we engineer our way there. If we were to take a ‘first principles’ approach to education, how might we take the fundamentals of learning to reorganize how we conceive of learning environments but also the systems that are able to enable them?
In our current reality, there is general agreement — in both research and policy/practice — on the type of learning environments and experiences that promote deep and meaningful learning, and are therefore, what we should be striving for as we seek to overcome the challenges in our current education system. To be simply summarized, they are that each learner’s journey is inherently unique and that the learner’s experience (needs, motivations, background) is at the center; learning and development is constructed through playful, context-based, social/collaborative, embodied experiences, that enable the learner to construct their own understanding and experiences by engage with a wide range of tools and modalities. The OECD Innovative Learning Environments project summarized these findings from the learning sciences literatures as the “7 First Principles of Learning” (DuMont, Istance & Benavides, 2010). In this literature, calls for using learner-centered principles as the foundation for a systemic redesign of K-20 education show up more than 15 years ago (McCombs, 2003). Since that time there has been much emphasis on the learner-centered aspect, but much less on the systemic redesign.
By leveraging systems engineering tools with a design mindset we can engage with the complexity of delivering meaningful learning at scale, to build the system that meets our needs. Such approaches have rarely been used in education. What innovations and new future might we be able to develop if we did?
ENGINEERING FOR MODERN LEARNING AT SCALE
Education is arguably one of society’s most complex endeavors. Unlike mechanical engineering and chemical engineering, education’s primary object of focus (learning) is invisible and largely intangible; the end users (learners and educators) have limited say and authority over decision-making, while multiple layers of local, state, and national policies come together to create the governance structures that ultimately largely define the everyday practices that ultimately define learning outcomes (Bar-Yam, 2004). In fact, today we can stand on a considerable foundation or research, methods, and applications that define the nature of human learning. Yet given the challenges and complexity of enabling quality learning at scale across our societies, now more than ever we need a science of education systems, and to apply the same intensity of approaches to the design of these systems. Now more than ever, we need educational engineers — to tackle the reengineering of the systems that deliver learning at scale.
How do you create systems and structures that manage learning experiences for millions of learners? Certainly, it is no simple task. Through the late 1800s and early 1900s as one-room schoolhouses evolved into the education systems we have today, consider the design decisions that had to be made about how to manage that complexity — long before our current technologies were available. In this light, and given the previously defined goals of the system, the system structures such as grade levels, school timetables and calendars, curriculum documents, etc., make a fair amount of sense. Yet, given a blank slate today, is this what you would design?
In the next article in this series, we will unpack the structures of system we currently have, and how this has negatively impacted learning.
This was the first article in this series. See also part 2 and part 3.
