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How Education Works: 6. A Co-Participation Model of Teaching

How Education Works
6. A Co-Participation Model of Teaching
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“6. A Co-Participation Model of Teaching” in “How Education Works”

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6 | A Co-Participation Model of Teaching

We get schooled by the people around us, and it stays inside us deep.

—George P. Pelecanos, (2003, ch. 19)

Having established some broad features of technologies in general, I now examine more closely a particular kind of technology: pedagogies. Our ways of teaching are of great significance in understanding the larger educational machine because they are the sine qua non of all educational interventions. Without them, there can be no education. There are many other things that an education system does, from weeding and sorting to feeding industry, not to mention maintaining social stability, keeping children out of harm’s way, enabling non-profitable but important avenues of research, and providing a home for thinkers and dreamers who otherwise would starve. But, at its heart, an education system is a system for teaching.

Pedagogies as Technologies

“Pedagogies,” as I use the term here, are replicable processes, methods, designs, models, theories, and principles used to help people learn. If this does not accord with your own definition, then you might prefer to substitute “pedagogical methods, theories, models, and designs” when I use the term, but this seems to be a bit unwieldy to me. AECT’s definition of educational technology from as early as 1972 as “a field involved in the facilitation of human learning through the systematic identification, development, organization, and utilization of learning resources and through the management of these processes” (1972, p. 36) or, more recently, its definition as “the study and ethical practice of facilitating learning and improving performance by creating, using, and managing appropriate technological processes and resources” (Januszewski & Molenda, 2008, p. 1, quoted in Hlynka & Jacobsen, 2010) makes it clear that the processes, patterns, and repeatable elements of instructional design have long been thought of as educational technologies, so my claim that pedagogies are technologies is far from controversial. Pedagogies are orchestrations of phenomena that include stuff such as our beliefs about how people learn, the means that we use to instantiate them, and the nature of the competences that we are trying to gain in order to achieve learning.

It might be useful to clarify what I mean by the term “pedagogy,” because it has multiple layers of meaning and a certain amount of fuzziness in its application. To say that pedagogies are technologies is not the same as to say that pedagogy is technology. The Oxford Dictionary of Sociology fairly accurately describes pedagogy as “the science or art of teaching” (Scott & Marshall, n.d.). It is a field of study related to a set of practices, attitudes, and approaches rather than just the methods used. Pedagogy as a field is not the same thing as a pedagogy. The matter is confused further when we talk of pedagogical purposes, pedagogical value, or even, poetically, pedagogical love (Vandenberg, 2002); the term is simply a means of describing a relationship of teacher to student or, more generally, to signify some kind of teaching and learning focus.

It might be simpler to describe pedagogies in languages other than English. Friesen (2007) observes that the English language fails to distinguish pedagogics from didactics (in German didaktik). Didactics is concerned with a consistent and intentional method used for teaching, whereas pedagogics is concerned with the theory, models, and principles behind that teaching. Although, as we have seen, there are good reasons to believe that theories, models, and principles are also technologies, they are fundamentally different kinds of technology. This is thus a potentially useful distinction that, though not unheard of in educational theory circles, is not commonly applied in educational practice in English-speaking countries. In this book, I assume the more common and broader distinction that (for English speakers) a pedagogy is a technique, method, structuring principle, guiding model, or theory for teaching, thus encompassing both didactics and most (but not all) of the denotation of pedagogics.

Pedagogies are repeatable and communicable sets of processes and techniques as well as higher-order principles, theories, and models that structure and constrain those processes, and all are technologies. “Pedagogical” pedagogies are ways that we structure and understand the processes and phenomena on which they act. They are technologies to explain and inform “didactic” pedagogies—meta-pedagogies perhaps. For the sake of simplicity, the meaning that I use here is the one that might be assumed if we were to ask a teacher to describe the pedagogy in a particular intervention and the one implicit in a large number of educational research studies that describe interventions in terms of actions performed, methods used, techniques applied, and principles followed. A pedagogy is a way of teaching, how it is done. Whether we are describing a detailed learning design, a methodology, a theoretical model upon which our design is based, a broader principle, or the activity of teaching, it is still a technology.

Didactic pedagogies are essentially algorithms for teaching or, bearing in mind that they typically can be used by learners as much as by teachers, more accurately can be described as algorithms for learning. Although commonly associated with the fields of mathematics and computing, an algorithm is simply a set of rules that describes how something is done. In some countries, algorithms are patentable inventions, in recognition of their inherent technological nature. Whatever the practical or moral implications of treating algorithms as intellectual property, it is hard to argue that they are not designed, constructed, and implemented for some purpose or purposes. They are both inventions and technologies.

Pedagogies are technologies of process and method more often applied by human beings than by machines. However, like many other processes and methods, many are technologies that can be embedded as easily in hardware and software as they can be enacted by people. We do not have to go as far as explicit instantiations of pedagogies such as Skinner’s (1960) teaching machines or educational adaptive hypermedia technologies (Brusilovsky, 2001) to see examples of this. Pedagogical theories (implicit, valid, or not) form part of the design of lecture theatres, exam rules, and even the time allocated to classes, not to mention more complex orchestrations such as learning management systems (Lane, 2009). All such things are based upon assumptions about the uses—parts of the assembly—to which they will be put, supporting some methods of teaching more easily than others and preventing or strongly discouraging other methods of teaching. Sometimes explicit pedagogical methods can be built into regulations: requirements for courses to have textbooks, for example, or homework, or standardized curriculums often cemented by standardized tests or other assessments that strongly determine not just what is taught but also how it is taught. In extreme yet all too common cases, they can even be imposed in the form of lesson plans, scripts, and learning designs to which teachers must conform.

Pedagogies, Softer and Harder

Like all technologies, pedagogies are soft for their creators. For the teacher in the classroom, even when that classroom, regulations, expectations, and other extrinsic constraints are taken into account, often great flexibility is possible in the format of the lesson and great scope to develop and use a personal technique. For students in that classroom, the teacher’s (combined with the institution’s) control of the space might leave little scope for flexibility while the class is in progress, but students may yet make important choices about how and whether to pay attention, and of course many other choices can and will be made before and after the event that might help or hinder learning in the classroom. Students also have pedagogical techniques. As always, it makes a huge difference where the boundaries of the technology are placed—and from whose perspective—regarding whether the overall assembly is seen as softer or harder. In all cases, though, the real-world enactment of any learning technology can and must include the orchestration performed by learners, and it makes a big difference whether the pedagogies that they use are part of someone else’s orchestration or something that they orchestrate themselves.

For a teacher, pedagogies can be more or less hard. At the extreme soft end of the scale, the pedagogies might offer little more than principles to guide creative teaching. For example, Chickering and Gamson’s (1987) popular “seven principles” have more to do with attitudes and values than with directing which methods a teacher should use.

  1. Encourage contact between students and faculty.
  2. Develop reciprocity and cooperation among students.
  3. Encourage active learning.
  4. Give prompt feedback.
  5. Emphasize time on task.
  6. Communicate high expectations.
  7. Respect diverse talents and ways of learning.

These principles leave almost nothing but gaps for teachers to fill with technique and invention, offering minimal guidance on the form or content of interventions. They achieve this by shutting down harder paths as much as they increase the adjacent possible. Teaching approaches that follow these principles are constrained—conventional lectures, for instance, rarely fit well with this model—but within their boundaries endless different methods and other technologies can be used.

In the middle range of the spectrum, for example, Gagné’s (1985) equally popular “nine events of instruction” specify a sequence of actions and activities that should be followed and provide an algorithm for teachers seeking to structure a lesson or course design:

  1. gaining attention;
  2. informing participants of objectives;
  3. stimulating recall of prior learning;
  4. presenting the content;
  5. providing learning guidance;
  6. eliciting performance;
  7. providing feedback;
  8. assessing performance; and
  9. enhancing retention and transfer.

Although prescriptive, on a spectrum of soft to hard, even this hard formula offers huge gaps that must be filled by the teacher in countless different ways. The method almost guarantees the use of lectures, presentations, or similarly teacher-driven ways of presenting content, but there is almost infinite variety in how they can be developed. Similarly, the popular “compliment sandwich” approach to feedback, in which problems with the work are sandwiched between positive and encouraging feedback, is prescriptive, but it offers great variety to the teacher in how it is enacted. Technique is overwhelmingly more important than method.

At the extreme hard end of the scale, a lesson script that the teacher is required to follow offers little choice, but even so there can be opportunities for a teacher to diverge (e.g., when students ask questions), different ways of expressing what is written in the script (vocal emphasis, facial expression, etc.), and some interpretive flexibility, depending on how rigid the requirements are to adhere to the script. Rules requiring adherence to the script, or timetabling constraints that inhibit divergence, can make this a lot harder. The hardest of all pedagogies are those embedded in the tools, media, rules, and artifacts used in the assembly. If a textbook is used, say, or if the teacher is tutoring someone else’s online course (or even the teacher’s own if prewritten), then the teacher might have no control over an essentially fixed technology that forms the main motif of the activity. As always, however, it is possible to add parts to the hard assembly in order to create something softer. Teachers can recommend or emphasize different parts of a textbook, tutors can add explanations and interpretations of course materials, and so on. Largely, therefore, pedagogies tend to be soft for teachers, even when they seem to be as hard as they can be.

For students, a certain amount of softness is always available in the pedagogies that they themselves apply to the assembly. Even when a teacher controls every second of a lecture that students are forced to attend, with rewards and punishments driving their attendance, they may still use note-taking techniques, daydream to associate ideas freely, connect what is being said to prior experience, and use many other sense-making approaches to adapt the teacher’s pedagogy to their own contexts. However, the teacher’s pedagogies can be extremely directive and hard, thus playing a huge structural role in setting boundaries that cannot be crossed, constraints on action, and many ways of confining the adjacent possible. In many cases, students might be little more than enactors of a teacher’s orchestration. A teacher following, say, Gagné’s (1985) model might leave little room for students to diverge from the established path and will tend to emphasize power relationships that strongly militate against student autonomy. It does not have to be that way: Gagné’s model is soft for teachers and therefore allows them to give students greater agency, but it is always theirs to give or to take away.

In the middle range of pedagogical plasticity for students are those learning activities that invite participation, from the simple ability to ask questions to seminars, tutorials, and group projects in which the teacher sets tasks, perhaps imposes structure on the form of participation, and establishes goals to be achieved. As soon as dialogue enters the frame, the teacher’s control is not as great, and power (and orchestration) are shared among participants. A model like that of Chickering and Gamson (1987), with its heavy emphasis on student autonomy, social interaction, and active engagement, makes softness almost inevitable. However, the teacher might well demand that this softer process is followed, and constraints on fixed learning outcomes and assessments, say, can harden the process considerably. Although students can perform some of the orchestration, the uses to which it is put can remain firmly in the hands of the teacher. If that is what learners need—and if it is their choice—then it can be a good thing, because hardness brings efficiency, replicability, and reliability. For those who do not know how to learn a particular topic or skill, hard pedagogies can provide useful scaffolding so that they have the hard techniques that they need to do so in the future.

At the extreme soft end of the scale, self-directed learning, for example through reading a Wikipedia article and following its links and references, or watching a set of YouTube videos, or following a Khan Academy tutorial, provides a great deal of freedom to learners actively to invent and adapt pedagogies to their own uses, orchestrating what they find in ways that suit their needs, interests, and capabilities. However, even then it is difficult and almost impossible to escape a certain amount of pedagogical hardness, inasmuch as the resources that they use typically include at least implicit pedagogies designed by someone else: the structure of the Wikipedia article (and Wikipedia itself), the explicit pedagogical methods of a Khan Academy or YouTube tutorial, and so on. Soft technologies are always assemblies that include hard components, so this is not unexpected. Importantly, though, self-directed learners faced with a hard tutorial that does not achieve the desired results might stop using it and find a more suitable one, or they might repeat it in the hope of understanding it better, perhaps applying different ways of orchestrating it, and different technologies in the assembly, for instance by making notes, mindmaps, and so on.

Although there is no direct causal relationship between the plasticity of a pedagogy for the teacher and that for the student, there is a tendency for pedagogies that are soft for teachers also to be soft for students. The reason is that softer pedagogies for teachers tend to allow for (at least) a two-way flow between teacher and student and for students to take diverse paths in learning. Perhaps most importantly, soft pedagogies give teachers the flexibility to adapt to what they observe students want or need. Thus, as long as they can be aware of how students are responding, they can take greater control over the teaching process, so that both they and their students gain greater control over the learning process. Conversely, harder pedagogies for teachers also tend to be harder for students because they (rather than the needs of students) drive the process.

Pedagogies in Assembly

Pedagogies are technologies for the same reason that computer programs are technologies, and like computer programs they are nothing without their instantiation: they need a machine to run on, whether that is a soft substrate of human interaction, or an institutional system, or an LMS. Thus, though it is possible to describe pedagogies in fairly abstract terms, they become working technologies only when they are organized with other stuff to attempt to achieve the aim of learning. Teachers do not need to design all the parts. As Hlynka and Jacobsen (2010) observe, “most educators are not in the business of designing or inventing the hardware, cables and connectors. Instead, educators select and evaluate technological processes and resources; they create environments and design learning experiences; they assess learners and deep learning and evaluate the quality of performances. In short, educational technologists are interested in creating and evaluating learning and performances that are more effective or efficient because of the technological processes and resources.”

What Hlynka and Jacobsen (2010) leave unsaid is that it is common for some of the parts themselves to include pedagogies, for instance the implicit assumptions of lecture theatres, the multiple pedagogies embedded in textbooks, regulations for exams, and so on. Pedagogies can be instantiated in countless ways as simple as words, gestures, or actions or as complex as a book, computer program, or classroom, with all the surrounding complex interrelations that such technologies entail. Although we might be able to describe it in abstract terms as a distinct tool/method/technique/process/structure/model/and so on, a pedagogy is not recognizable as a technology until it is instantiated, which always means that it is part of an assembly. If we are investigating the effects of pedagogies, then of necessity we are also investigating the assemblies, including other technologies, with which they are orchestrated.

Pedagogies Rarely If Ever Come First

If they are parts of other assemblies, then pedagogies should rarely if ever literally come first in a learning design process. Pedagogies are technologies that orchestrate other technologies and phenomena, and exist within larger assemblies, so these other technologies and phenomena must already exist. More often than not, pedagogies’ forms are dictated at least partially by technologies with which they are assembled and by the limits of how they can be orchestrated together. Often the parts with which they are assembled can be harder, and thus more structurally dominant, than the pedagogies themselves, and/or the pedagogies can be part of a larger assembly, such as an educational system. This means that, though it is reasonable for learning designers and teachers to say “pedagogy first” if all that they mean by it is that we should not forget that our purpose is to teach, it is not true if they mean that pedagogies should always come first in the list of procedures and methods used to achieve that end and certainly not if they are talking about the assembly and orchestration used to attain it. Pedagogies are inseparable parts of an educational assembly, but they are just parts of the orchestration of the educational machine. Similarly, a bicycle must have wheels or it would not be a bicycle, but wheels do not have to come first in the design, nor should they always dictate how the vehicle is designed (though they will always impose their own constraints, which can be strong in some cases).

Analogous to remembering that the purpose of teaching is to teach, a bicycle should normally transport its rider from A to B, but cost, comfort, speed, reliability, safety, and so on can be at least as important. Similarly, when designing or performing a learning intervention, costs, timetables, curricular constraints, resource availability, time constraints, tech options, and so on can matter to the designer or performer of them at least as much as the fact that the purpose is to teach and might well come prior to a consideration of teaching methods. Pedagogies are soft. The assembly always matters more than the parts. The parts are significant only in terms of how they relate to the whole. Equally, a student’s pedagogies, in a formal learning context, can be subservient to a teacher’s pedagogies. Although students can choose to orchestrate different phenomena to help them learn, the range of options available can be constrained. In too many cases, if they diverge too far from those intended or condoned, they can suffer punishments such as poor grades or the censure of the teacher.

Distributed Pedagogies

Pedagogies are not just technologies used by teachers in classrooms. Learners themselves are always the final orchestrators of phenomena for learning, and consciously or not they always apply strategies, techniques, and methods of their own to the process of learning. As Fawns (2022, p. 715) puts it, “students co-configure and co-design as they reinterpret and complete teachers’ plans.” Although others can strongly influence them, and they are certainly skills that can be learned and refined, a learner’s own pedagogies invariably are parts of any educational assembly, and every learner will apply them differently because every learner is different. But learners and designated teachers are far from being the only orchestrators of phenomena in a typical learning experience.

Beyond the pedagogies supplied by people designated as teachers and learners themselves, almost always, intentionally or not, pedagogies are added to the assembly by many others. Even within the extremely limited context of a conventional classroom, we might find pedagogies commonly used by

  • other students discussing what they have learned;
  • authors of textbooks writing in ways meant to teach;
  • textbook illustrators using visual technologies to explain or amplify;
  • textbook editors clarifying language and structure for clearer transmission;
  • website developers building information and tutorial sources;
  • computer technicians setting up projectors and smartboards who assume how they will be used;
  • lab technicians setting out equipment in ways that assist understanding;
  • technical authors of instruction manuals, like textbook authors, aiming to help learners understand their tools;
  • writers of notices on walls intending to communicate quickly and efficiently;
  • designers of school regulations intending to support successful learning;
  • creators of timetables seeking appropriate times and durations for learning;
  • librarians helping learners to learn how to find resources as well as finding resources themselves;
  • classroom designers and architects whose assumptions about the teaching program influence their designs, and hence affect which teaching methods can be used effectively;
  • purchasers of classroom furnishings assuming how they will be used;
  • makers of exercise books assuming generic features of pedagogically useful note taking (e.g., flexible organization or margins); and
  • curriculum designers operating sometimes at a national level.

All of these actors make decisions based upon (often tacit and sometimes erroneous) assumptions about their probable effects on learning and/or the teaching and learning methods that they will support, and all can have a greater or lesser effect on student learning in the classroom. Most can make all the difference between successful and unsuccessful learning. Invariably, there are countless co-participants contributing processes and structures that affect learning, for better or worse. There is often a recursive and complex relationship between these co-participants. For example, designers of classrooms (hopefully) will be influenced by what they imagine students and teachers in those classrooms will do, which in turn will influence what they actually do, which (if it differs from the program intended by the designer) will influence future designs.

Beyond the Class or Course

Learning does not begin or end in the classroom. Even if we confine ourselves solely to the subject of a lesson, it will also be affected by news articles read before and after the lesson, movies, Wikipedia articles, discussions on social media, conversations around the dinner table, a large number of objects, and interactions with other people before and well after the class, months, years, or decades in the future, all of which embed methods of passing on knowledge and skills. We place convenient boundaries around the time, the place, and the actors in a learning transaction, but those boundaries, in real life, are extremely fuzzy, permeable, and wide. What and how we learn become both grist and mill (Heyes, 2018) for future learning, and neither the “what” nor the “how” remains static. Knowledge and skills—including the skills of learning—are not saved to our brains like bits on a computer storage device but participate as active, constantly renewed, constantly transformed elements in our cognitive toolchest. When educators claim that students have achieved specific learning outcomes, they are referring at best to a snapshot of an ever-unfolding process that will continue indefinitely, whether gaining in richness or being forgotten (usually a bit of both). At least some of my teachers of 50 years ago continue to teach me today, for better or worse, persisting (with countless others) as co-participants in my ongoing learning journey.

Perhaps the majority of acts of communication are intended to affect the knowledge and behaviour of those with whom we communicate: in effect, to teach. As Dewey (1916, p. 9) put it, “not only is social life identical with communication, but all communication (and hence all genuine social life) is educative.” To communicate, we must make assumptions about how our messages will be understood by others, and we must make decisions about how to express them effectively: in other words, we apply pedagogies. Most intentional communication is meant to bring about learning, whether or not we intentionally aim to teach. There are some possible exceptions, including performative utterances such as “I do” in a wedding ceremony (Austin, 2013), phatic expressions such as small talk (Zegarac, 1998), discussions of dinner plans, and so on. However, a great deal of what we try to express in language, image, video, sculpture, dance, and so on teaches, or attempts to do so, even if it is only an attempt to express how we feel, to impart information of transient value (e.g., how to get to one’s hotel room), or to reinforce something already known. Even a poem—if it affects us—teaches us. It changes how we think, feel, or perceive. In fact, even phatic communication is usually intended to affect: to cement a relationship, acknowledge a connection, signal a willingness to communicate, and so on. It might not contribute directly to our long-term learning or ability to adapt, but it can be an important component of an assembly that does, supporting necessary bonding social capital for trust building and relationship forming between learners and between learners and teachers.

At the fuzziest end of the teaching spectrum, pedagogies can be found embedded in many structures and technologies, from classroom designs to learning management systems (the software as well as the contents that it displays or the interactions that occur within it). At the least, designers have some general scenarios and uses in mind when they are built that can reinforce some behaviours (e.g., tiered seating to support lectures) and inhibit others (e.g., user roles and permissions in an LMS that prevent exchanges between different courses, needlessly perpetuating a path-dependent design pattern of classroom walls invented solely to solve problems caused by the limitations of physical spaces).

Designers of technologies, buildings, furniture, and even clothing typically attempt to teach users about their purposes through the designs themselves, using obvious and subtle cues to invite people to use them in their intended ways. Even those that deliberately make their purposes obscure—hidden doors or safes that look like cans of beans—invite reflection on why that is. Books are made to be read, cups to be held and drank from. The neck of a guitar invites a particular kind of grip, and its frets invite a certain kind of finger placement. Symbols and labels tell us what buttons or bottle caps are supposed to do. Metaphors, conventions, skeuomorphic designs that recall prior technologies, and countless other acts of communication fill our designed world. We design things to be used, and in so doing we make assumptions about how people will learn to use them. In structuring our world to be intelligible, we are also making decisions about what makes it intelligible.

Cyborgs and Collectives

Even in the most highly structured and constrained circumstances, teaching is always a highly distributed technology, orchestrating many phenomena at many levels and in many assemblies, involving multiple pedagogies as well as other technologies, enacted by many different co-participants, each of whom, directly or indirectly, affects others in the assembly.

The many teachers who, intentionally or not, contribute to any learning that we accomplish can be thought of as a gestalt, as a distinct (if fuzzily boundaried) entity, a collective intelligence composed of purposeful acts of teaching, engagements with others, embedded learning in our technologies and artifacts, reified teaching in our communications, and active pedagogies in processes or methods that we have learned in the past. Each part contributes to the assembly through which we learn and with which we think. From words to textbooks, from theories to desks, from whiteboards to windows, the act of deliberate teaching is a largely unwitting cooperation between myriad teachers, all of whom are co-participants in multiple technologies.

Franklin’s (1999) conflation of culture and technology acknowledges the fact that we and our technologies are inseparable parts of the same entangled coalition. Our technologies are a fundamental facet of what it means to be human living among other humans. Our technologies are what provide intellectual and creative potential that far exceeds that of any other known species. To live as a human being in a human society is continually to invent and instantiate soft technologies, as well as to incorporate the hard inventions of others into our own thinking, in an ongoing process of assembly and orchestration. The intelligence that results is only partly human. It is also partly something emergent, different from and perhaps greater than the sum of its parts, an entity in its own right, a collective. Indeed, it is a lot more than a single collective: there are many layers of emergence, many collective entities that make a difference, from groups to cultures to networks of people whom we know, along with the artifacts that they create (Davis & Sumara, 2006).

It is not unreasonable (if a little uncomfortable) to see ourselves as cyborgs, partly composed of technology (Haraway, 2013), but being a cyborg is an important part of what makes us distinctly human. Equally, though, and perhaps more interestingly, it is possible to see our technologies as cyborgs, partly made of us, partly made of one another, each part of the assembly perhaps another cyborg, a collective made of collectives. Without our technologies, we are just smart, social, and not particularly effective apes. Technologies embed as well as support pedagogies, and they mediate the collectives that learn, linking our cognition with part of a dynamic whole distributed in time and space. Humanity can only be understood properly as not just a collection of organisms but also the artifacts and processes that those organisms create and share.

Technologies—and our roles as co-participants in them—are what make us as individuals smart and what make our species collectively (and as a collective of collectives) intelligent or, as Cohen and Stewart (1997) put it, extelligent. This is not the same kind of smartness possessed by an individual human, and almost certainly it is not sentience or consciousness in any form that we would recognize. Intelligence, more generally, can be seen as the ability dynamically to adapt to and survive in an ever-changing environment: the evolution of species and even evolution itself (constantly evolving greater evolvability), in this sense, can be seen as an intelligent process (Watson & Szathmáry, 2016). Intelligence results in, and draws from, learning, but the learning does not have to be embodied in a brain. A brain is just one bounded emergent entity among many. Which entities we choose depend on where we draw the boundaries and what level of emergence we choose to observe. Davis and Sumara (2006, p. 86), for example, distinguish between species-level learning and individual-level learning: “Most dogs will instinctively leap back when encountering a snake or a snake-like object. Such an action is clearly an intelligent one, and has no doubt preserved the existence of many canines. However, it would be inappropriate to attribute the intelligence to the individual animal. Rather, this instance of smart response operates at the species-evolutionary level. The species selected the response, not the individual.”

Collectively intelligent behaviours cannot just be ascribed to us as individuals, but seldom do they operate at a species level like the instinctive behaviours of dogs. We work and learn only with the technologies—tools, methods, artifacts, structures, and so on—that we encounter in our lives, only with a small subset of them, and only in a limited number of ways. There is thus a great deal of local variation in the skills and knowledge of a given network or community operating at multiple scales.

Given the massive spread of communication technologies, combined with the enormous networks of trade and travel that have featured in our evolution, what we have encountered for thousands of years, and at a vastly accelerating rate in recent centuries, has included technologies from around the world, leading some to suggest that what results is a worldwide collective, akin to or in some way implementing a global brain (Bloom, 2000). This might be so, in a broad sense, though it is not at all like a single, thinking, purpose-driven mind with its own will and consciousness. A large part of the reason for this is that none of us can ever see more than a fraction of it, let alone understand it in its entirety, any more than individual termites understand the complex mounds that they build or the collective behaviours of their colony. The parts in which we co-participate involve local, not global, action, and there is differentiation at an indefinitely large number of scales, from our personal networks or families, to the geographical communities that we inhabit, to our cultures, nations, religions, and many more smaller- and larger-scale clusters. One group, network, or set of people and its shared objects (cognitive and physical) can differ considerably from another, forming differently boundaried extelligences, though, just as each individual connects to every other, so too all connect at some level and thus co-participate in one another and ultimately with all others.

The various collective entities that we participate in, in some though not all ways, might be smarter than us as individuals: they certainly know a lot more, but they can act more intelligently too. Collectively, for instance, we have created many extraordinarily complex technologies without any individual actually understanding them. As Derex et al. (2019) demonstrate, the accumulated improvements of technologies made over generations can lead to technologies based upon poor causal reasoning (individually) but that nonetheless embody far more complex causal relations. For example, to create optimal bows and arrows from scratch would require multidimensional causal thinking—including knowledge of things such as gravity, inertia, and stored energy—that would have been impossible for our forebears. However, thanks to improvements made over many generations, using incorrect causal reasoning, bows and arrows used by our ancestors were as highly optimized as any that we could create with our more advanced knowledge of physics. In effect, by embedding the learning of many individuals, our technologies can become smarter than us as individuals, and thus we, as participants in them, become smarter too. Perhaps as interestingly, we (and the word we speaks volumes) have now developed the cognitive technologies to understand the complex design issues involved, at least in part thanks to the examples provided by such technologies. Technologies are not just the results of intelligence but also participate in it, because we participate in them.

We are not at all like Star Trek’s Borg, in the sense of being one vast collective entity with a single and centrally managed will. Instead, we interact with people and artifacts around us, which in turn interact with others, and so on, each making its own interpretations and transformations, in ever-spreading, scale-free networks that cluster around us and that unfold continuously into the world. In this sense, we are somewhat like ants or termites, communicating stigmergically with one another through the signs that we leave in our environments (Dron, 2004). Like ants and termites, mostly we see and communicate only with our immediate environment (people, groups of people, and their artifacts), and we have a dim idea, at most, of the whole. Unlike termites, through our technologies we can come to know any part of the whole.

Technologies enable us to achieve goals more quickly, more easily, without needing to learn the knowledge that they embody, and to move on from there. Johnson (2012, Section 2, para. 10), for instance, describes the near-miraculous safe landing of a stricken plane as “a kind of duet between a single human being at the helm of the aircraft and the embedded knowledge of the thousands of human beings that had collaborated over the years to build the Airbus A320’s fly-by-wire technology.” Notwithstanding the effort that might be needed to learn to use them, hard technologies often eliminate the need for their users to go through the sometimes gruelling and, for any moderately complex system, impracticably lengthy process of learning the same things. They are co-participants in both our actions and our cognition, extensions of our minds that overlap with extensions of other people’s minds, in a rich and ever-shifting tapestry of shared cognition.

This is the essence of the dynamic of socially distributed cognition: the learning of others is a part of the objects, buildings, and other stuff they create. It is what makes the human race smart (Henrich, 2017), far more than the individual intelligence of its members. We are able to affect our environment massively, in both negative and positive ways, because we do not have to rely on our own intelligence, or even that of those in the vicinity, but can incorporate the combined wisdom and reified knowledge of countless others, including our forebears, into our thinking and use the technologies that others have built (cognitive, physical, or whatever) in our activities. Any individual intelligence that we possess is almost entirely founded on our collective intelligence as cultures and societies.

My cognition is partly shared with yours, and partly with anyone (though likely not everyone) else’s on the planet, including many of those who lived before us. We are able to use and participate in technologies that we have learned from others to orchestrate other technologies around us, in order to think and act intelligently ourselves, in ways that can be used and orchestrated by others. When we enact hard technologies, we are just part of that orchestration, but when idiosyncratic technique and creativity come into play, especially when others must perform acts of interpretation using their own extended minds, our collective mind adapts to the world that it both invents and inhabits. The technologies are not just extensions of our own minds but also the means through which our minds become intertwingled with those of others.

Given these multiple layers of bounded learning systems in which we participate, learning can be understood properly only as a distributed function, and teaching can be seen only as a collective pursuit in which we are at once co-participants and co-beneficiaries. This is one of the reasons that learning with, from, and through others is such a good idea. There are richly recursive feedback loops that are filtered through and orchestrated by those involved, all of whom see different parts of the whole, and that make the whole much greater than the sum of its parts. Teachers do not just teach individuals in a class: they teach the class itself, and the class teaches back. Classrooms cannot and should not be seen as disconnected entities, however. All members of the class are part of many cultures, large and small, partly defined by common technologies—at least vocabularies, norms, and shared communication tools—that participate in our learning and our thinking.

Given the irreducible complexity and extraordinary scope of the phenomena that must be orchestrated, not to mention the vast range of possible orchestrations, the chances of two particular assemblies—two instances of learning—ever being more than a bit similar are remote. The fact that the larger and slower parts of the system will likely result in some recognizable shapes and patterns when viewed at a coarse level hides a wealth of detailed differences. The chances that they are identical are zero because the world (as experienced) is constantly unfolding and always experienced differently by each person at a particular place and time, each with unique histories interacting with countless other unique histories. It is no more possible to repeat a learning experience than it is to restore prairies or woodlands to their historical states (Katz, 1992), because the unique complex phenomena that led to those states can never be the same twice. To learn is to change in ways that have never happened before and will never happen again in the whole history of the universe. Although an individual teacher might precisely replicate a method, or it might be recorded through a replicable medium, teaching, viewed as a distributed technology involving countless phenomena, including those provided by the learner, is therefore always a creative act that can never fully repeat itself, any more than one meadow or forest can ever be identical to another, let alone to one in the past. Parts can be the same, but the whole never is. Just as we have seen how computers and many other technologies should be treated as different technologies according to the boundaries that we choose and the points of view that we take, so too pedagogies that we apply as intentional teachers can only ever be part of a much larger assembly. And, as for the computer, the pedagogies that such teachers intentionally use can be among the least significant parts of the technology that brings about learning. They are not, however, unimportant.

The picture that I have painted of a massively complex, only partially designed gestalt might seem to leave little room for education systems and formal teaching. However, like all complex systems, the harder, larger, slower-moving parts have large roles to play in giving shape, purpose, and structure to the overall system. Just because teachers, education systems, and all their associated methods do not lead to predictable, deterministic results (and, even if they do, invariably they lead to others unintended) does not mean that they lack value or influence.

The Value of Education

Education (in its broadest sense) brings stability or—if it works well—metastability in society, a state of continuity that, like human bodies or ecosystems, maintains its identity but constantly adapts to changes from within and without. Education is concerned with enabling us to operate as humans in a human society, co-participating in its many technologies, and thus for society itself to adapt to its needs and to operate effectively. Without education, societies as we know them today could not exist or, at best, would be horrifically unstable and weakly adaptive. Education is for the benefit not just of individual students but also of everyone in a society. The kinds of knowledge enabled through education systems make it possible for each of us not only to operate the technologies of our societies and cultures successfully but also to play our roles in making them work for everyone. The numerous technologies and the great complexity of this collective endeavour thus provide a good case for moderately consistent, fairly hard education systems, albeit that institutions of the sort with which we are most familiar might not be the only or best solutions to the problem. Excessive hardness—where everyone is forced to learn the same things in the same ways—creates far bigger problems than those that it solves, however. If the needs of society are solely for people to play their roles in predesigned hard technologies, then such uniformity might have some value, though the value of such a society itself might be limited, unless you happen to be one of the few who has control over those hard technologies. Societies that can adapt to changing conditions need people with soft skills who can orchestrate technologies creatively, flexibly, and well, not just correctly. Systems that seek to inflict hard pedagogies on teachers and/or students, especially at scale, run a huge risk of training a population to be part of a large, inflexible machine, adept at performing large-scale coordinated tasks, capable of solving known problems, but less able to adapt to new ones. Given the inevitable expansion of both the adjacent possible brought about by our technologies and the problems that they in turn cause, which consequently have to be solved by counter technologies, an overly hard educational focus is unlikely to be the best way forward in the years ahead.

The technologies of institutional education can make designated teachers among the harder, more influential parts of the educational assembly. In sharing received wisdom they tend to act as preservers of relatively invariant cultural knowledge. However, as teachers, in our pedagogical designs and methods, we must be aware of the gestalts with which they are combined; we need to remember that we must be responsive to the effects that our interventions have when assembled with countless other interventions; we should be prepared to adapt or at least to acknowledge ways that our plans can and will be subverted, transformed, or distorted by the whole. The deeply complex interweaving of technologies in which we are co-participants means that outcomes can be very different from, and almost always far richer than, those that we intend. Teaching changes the extended mind in which we are co-participants, and thus it changes (or should change) us and our teaching in an ever-repeating and complex recursive cycle.

Teaching, done right, is learning. Pedagogies are soft technologies in which we constantly reinvent, transform, and embroider the coarser and harder methods from which they are assembled, in ways never to be replicated again. As we do so, the fabric of knowledge and skills woven takes on a character and form that no one can predict with any precision but that, acting locally and seeing how it changes, we can build upon and influence. Often we can take the mistakes, the serendipitous emergent forms, or the unexpected patterns and turn them into something closer to what we aimed for or (perhaps) into something new and more wonderful than what we planned.

Teaching is a form of distributed, partly emergent, contextually situated bricolage in which our designs are assembled with as well as from other pieces, so we must be aware of and responsive to all those pieces, including those provided by the learner, if our designs are to be successful.

The in-person teacher can use any of an almost infinite variety of technologies, including pedagogies, as part of the bricolage and can observe the learning behaviours of students closely as long as there are not too many of them. However, being aware of the parts is perhaps even more important when we have limited opportunities to interact with our students, such as when teaching asynchronously online or dealing with large classes. Because we cannot be as directly responsive, and because students will inevitably learn independently no matter what we might plan, our designs need to acknowledge the distributed teacher, to provide freedom to diverge. We need to design ways to observe and, if possible, to engage with that distributed teacher.

Pedagogical technologies that reveal the process such as shared reflective learning diaries (blogs, wikis, etc.), shared discussion spaces, as well as shared products of learning (assignments, essays, projects and other shared inventions and discoveries) can help to reveal many of the other participants in the gestalt. We might not be able accurately to plan everything that will happen, but we can respond to what does happen and, in so doing, help to guide the process. We are more like sailors or balloonists, seeking the winds and using them to guide us, than like drivers of trains, guiding machines along well-defined tracks.

For those of us who are employed as teachers in an institutional learning environment, the realization that we are not in control, that we are part of a collective, and that teaching is a soft technology enacted by many people apart from us comes with the critical proviso that we must stay close to our students in order to understand how they are navigating this complexity.

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7. Theories of Teaching
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