After quite some time of thinking, this is a summing up and an elaboration on some of the issues that have been under scrutiny in my explorations in this series of blog posts. It represents a recursive process, or maybe a matter of bricolage, as it reveals itself in four parts that can be read as one fairly short piece and three quite long pieces with pauses in between, or as a genuinely long read tuning in on 1) pedagogies in an era of knowledge abundance, 2) learning modes and a posthuman perspective, 3) the state of participatory culture and digital literacies, and 4) knowledge management and learning for and from the future.
The Learning Mode Grid
I am once again facing Steve Wheeler’s Learning Mode Grid – to be seen in The End No 1 – and my question about which pedagogies are suited for connecting knowledge while education is developing from Learning 2.0 to Learning 3.0 (Wheeler 2015:33-45). And yet, I have been building equally on Learning 2.0 and Learning 3.0 in my comments on pedagogies and teaching and learning up till now in this series, so let me elaborate a little bit on the relationship between the developments of the internet and the web and the pedagogies and learning principles that are suited for integrating both emerging social and cultural practices, new media and new smart devices into contemporary education: what is meant by Learning 1.0, Learning 2.0 and Learning 3.0?
In her paper “The Futures of Learning 3: What Kind of Pedagogies for the 21st Century?” (2015) Cynthia Luna Scott introduces Learning 1.0 and Learning 2.0. Learning 1.0 is defined this way:
“The standard learning model, Learning 1.0, evolved in the early part of the twentieth century and incorporates the aspects of schooling generally considered ‘normal and proper: students divided by grades, lessons by subjects, tests to the end of the year, and high school units collected until graduation’ (Kerchner, 2011). In this model, schooling and most other forms of formal learning are built on the principle of acquisition and storage of information with a view to analysing and eventually using it (p.1). ‘Pedagogy becomes the means to transfer knowledge through known and authoritative channels’ (p.2). Traditional roles prevail – in other words, the teachers teach and students learn.” (Scott 2015:8)
Playing on the internet being originally a network of computers building on databases and acquisition and storage of information and knowledge, the definition of Learning 1.0 matches the traditional lecture and textbook models of education Weller and Haythorntwaithe also reflect on in their models of education. And as a parallel to their emerging models of education, Scott abandons Learning 1.0 and advocates for Learning 2.0 instead:
“This model has outgrown its usefulness. Kerchner (2011) argues that Learning 2.0 is a very different proposition, consisting of a more flexible, personalized and experiential form of learning. He attributes the inspiration for this model in part to the internet, but mainly to recent changes in how people think about learning (p.3).” (Scott 2015:8)
Learning 2.0 is linked to the business model of Web 2.0, the principles behind it and the rise of social media (Wheeler 2015:169), which have basically been the backdrop of my writings on this blog so far. But just to be sure, here is a condensed definition of Web 2.0:
“In addition to the openness of Web 2.0, there is an “architecture of participation” (Barsky & Purdon, 2006; O’Reilly, 2005), which entails sharing of digital artifacts by groups, teams, and individuals, ensuring that the Web is responsive to users. It thrives on the concept of collective intelligence, or “wisdom of the crowds” (Surowiecki, 2004), which acknowledges that when working cooperatively and sharing ideas, communities can be significantly more productive than individuals working in isolation.” (McLoughlin and Lee 2008:10)
More than Kerchner, Scott is influenced by Catherine McLoughlin and Mark J. W. Lee who in their article “The Three P’s of Pedagogy for the Networked Society: Personalization, Participation, and Productivity” (2008) couple up pedagogy with what they call Pedagogy 2.0 and “social software that enables participation, communication, personalization and productivity (e.g. content creation), as these are elements of what it means to be educated in a networked age (Bryant 2006).” (McLoughlin and Lee 2008:12). So they elaborate on what rethinking pedagogy in the context of Web 2.0 means for teaching and learning:
“The “new” pedagogy is therefore not a matter of simply offering learners the technologies they are likely to use in the knowledge economy – these, like the knowledge itself, are subject to rapid change. According to Beetham and Sharpe (2007), it involves engaging learners in apprenticeship for different kinds of knowledge practice, new processes of inquiry, dialogue, and connectivity. Practices underpinning effective, innovative pedagogy will differ depending on the subject area or professional discipline in which learners seek to become proficient but are likely to include some or all of the following:
- digital competencies that focus on creativity and performance;
- strategies for meta-learning, including learner-designed learning;
- inductive and creative modes of reasoning and problem-solving;
- learner-driven content creation and collaborative knowledge building;
- horizontal (peer-to-peer) learning and contribution to communities of learning (e.g. through social tagging, collaborative editing, and peer review.” (McLoughlin and Lee 2008:12)
By placing apprenticeship for knowledge practices and processuality at the center of teaching and learning, the focus is moved away from mainly transmitting content towards “helping students to understand each discipline (or subject) as a system of thought (with its own codes, methods, strengths and limits)”, as Scott mentions (Scott 2015:14), and this involves the four types of skills and capabilities advocated for by Tony Bates and listed in The End No 1: the combination of conceptual, practical, personal and social skills to be practiced in highly complex situations.
The ‘new’ purpose of learning is exactly reflected in the interplay of digital affordances and the changing view on learning according to McLoughlin and Lee:
“Calls for change and innovation in pedagogy are representative of an emerging view of learning as knowledge creation (Paavola & Hakkarainen, 2005) and mirror the societal shift towards a knowledge age, in which creativity and originality are highly values. Applying social software tools to teaching and learning compels us to reconsider how the affordances and interconnectedness offered by Web 2.0 impacts on pedagogy and opens up the debate on how we conceptualize the dynamics of student learning.” (McLoughlin and Lee 2008:13).
To grasp this emerging view of learning as knowledge creation and different kinds of knowledge practice McLoughlin and Lee introduce knowledge creation as a new metaphor of learning to add to the metaphors of learning as acquisition and learning as participation:
“Sfard (1998) distinguishes between two metaphors of learning: the acquisition metaphor and the participation metaphor. The former represents a passive receptive view according to which learning is mainly a process of acquiring chunks of information, while the latter perceives learning as a process of participating in various cultural practices and shared learning activities. In the participation metaphor, the focus is on the process (i.e., on learning to learn) and not so much on the outcomes or products. According to this view, knowledge does not exist in individual minds but is a product of participation in cultural practices, and learning is embedded in multiple networks of distributed individuals engaging in a variety of social processes, including dialogue, modeling, and “legitimate peripheral participation” (Lave & Wenger, 1991). Learning occurs through sustained interaction and conversation with practitioners.” (McLoughlin and Lee 2008:13-14)
“To keep pace with the content creation processes enabled by Web 2.0 and social software tools, it appears to be necessary to go a step further and venture beyond the acquisition and participation dichotomy. Paavola and Hakkarainen (2005) propose the knowledge creation metaphor of learning, which builds on common elements of Carl Bereiter’s (2002) theory of knowledge building, Ikujiro Nonaka and Hirotaka Takeuchi’s (1995) model of knowledge creation, and Yrjö Engeström’s (1987,1999) theory of expansive learning. From the perspective of the knowledge creation metaphor, learning means becoming part of a community through participation, exchange of ideas, sharing, contribution of ideas, and knowledge generation. Students are both producers and consumers (“prosumers”) of knowledge, ideas and artifacts. .. The knowledge construction paradigm can be appropriately applied to learning environments where digital affordances and tools enable engagement in self-directed activities, and learners exercise agency in moving beyond mere participation in communities of inquiry to become active creators of ideas, resources, and knowledge artifacts.” (McLoughlin and Lee 2008:14)
To conceptualize the dýnamics of student learning as a motive force in a Web 2.0 context, McLoughlin and Lee coin the term ‘Pedagogy 2.0’ and highlights the three p’s – personalization, participation, productivity – as pedagogical principles and clusters of practice in Pedagogy 2.0. But besides that, Pedagogy 2.0 can also be seen as a framework for revising pedagogies while emphasizing the power of social software tools and social media, networks and communities, including communities of practice. Pedagogy 2.0 involves Learning 2.0, so to speak, so the three p’s function both as guidelines for designing learning activities and as strategies for meta-learning through co-creation of learning and knowledge construction. Learning to learn is at stake here:
1. Participation opens up to both horizontal peer-to-peer learning due to communities of practice, communities and networks and to personalization when following one’s own interests and choices in dialogue and collaboration with others:
“ A defining feature of Pedagogy 2.0 is that alongside the increased socialization of learning and teaching, there is a focus on a less prescriptive curriculum and a greater emphasis on teacher- to- student partnerships in learning, with teachers as co-learners…”
“…not only is this element of Pedagogy 2.0 reflective of the “participation model of learning” (Sfard, 1998), as opposed to the “acquisition” model, but it also adds a further dimension to participative learning by increasing the level of socialization and collaboration with experts, community, and peer groups, and by fostering connections that are often global in reach. Jenkins (2007, p. 51) aptly summarizes the process as follows:
Learning in a networked society involves understanding how networks work and how to deploy them for one’s own ends. It involves understanding the social and cultural contexts within which different information emerges…and how to use networks to get one’s own work out into the world and in front of a relevant and, with hope, appreciative public.” (McLoughlin and Lee 2008-16-17)
2. Personalization is about engaging in personally meaningful learning by giving learners control over the whole learning process through facilitation and modeling and eventually fostering self-directed learning. The quest for learning and being part of self-directed, or learner-centered and self- regulated learning as it has also been named, is not new according to McLoughlin and Lee:
“The learning experiences that are made possible by social software tools are active, process based, anchored in and driven by learner’s interests, and therefore have the potential to cultivate self regulated, independent learning. Self regulated learning…refers to the ability of a learner to prepare for his/her own learning, take the necessary steps to learn, manage and evaluate the learning and provide self feedback and judgement, while simultaneously maintaining a high level of motivation. A self regulated learner is able to execute learning activities that lead to knowledge creation, comprehension and higher order learning…by using processes such as monitoring, reflection, testing, questioning and self evaluation.” (McLoughlin and Lee 2010:29)
Nevertheless, four areas go into the development of personalization through digital technologies, and pedagogy must:
- ensure that learners are capable of making informed educational decisions;
- diversify and recognize different forms of skills and knowledge
- create diverse learning environments; and
- include learner focused forms of feedback and assessment. (McLoughin and Lee 2010:33)
One version of personalized learning is peer-to-peer self-organized learning that draws on groups, on egocentric networks/personal learning networks (PLNs) as well as on communities or communities of practice, while another version is following a more individual agenda where learners occasionally collaborate with others when needed. In either case there needs to be room for choice: choice of problems, questions, ideas and issues to work with; choice of cases, activities and tasks to engage in; choice of resources, networks and tools to use; choice of how to engage in knowledge creation, networks and communities; and choice of how to reflect on response and feedback. It might open up not only to motivation but might also cause in-depth engagement in a discipline or a subject matter and end up developing generic skills, competences and transferable knowledge.
3. Productivity is part of both the process of learning and a result of learning when it takes the form of ideas, resources, concepts, work in progress or knowledge artefacts. User-generated content is being placed in the context of education and might be supplemented by e-portfolios tracing and incorporating the pathways of learning, by asynchronous and synchronous dialogue and discussion, by reflective writing or multi modal production as blogs, summaries, reviews by individuals, groups or in a community, and by sharing resources, ideas, people and experts found in networks and communities (McLoughlin and Lee 2008:18). Demonstrating learning through production and performance of ideas, resources, concepts, work in progress and knowledge artefacts is closely connected to the idea of knowledge creation as a metaphor of learning introduced earlier:
“Students are capable of creating and generating ideas, concepts, and knowledge, and it is arguable that the ultimate goal of learning in the knowledge age is to enable this form of creativity and productivity.” (McLoughlin and Lee 2008:17)
Engaging learners in apprenticeship for different kinds of knowledge practice will include not only a re-definition of the role of learners but also a re-definition of the role of educators and teachers. Besides becoming co-learners, Scott suggests that teachers also need to become ‘learning coaches’ – or facilitators, as I would put it:
“Teachers as learning coaches will encourage students to interact with knowledge – to understand, critique, manipulate, design, create and transform it. Teachers will need to reinforce learner’s intellectual curiosity, problem identification and problem solving skills, and their capacity to construct new knowledge with others (Bull and Gilbert, 2012)…A key part of their role will be to model confidence, openness, persistence and commitment for learners in the face of uncertainty (Bull and Gilbert, 2012)” (Scott 2015:14)
So the three p’s, participation, personalization and productivity, are overlapping and complementary pedagogical principles and practices that are interacting with the emerging view of learning as knowledge creation also paid attention to by Martin Weller, Caroline Haythornthwaite and Tony Bates. To use Haythornthwaite’s words, the questions of “…how to plan for complexity, be prepared for emergent factors, and continue to evolve and use a knowledge base” (Haythornthwaite 2015:302) /link/ summarize not only the main concerns of Learning 2.0 but might also serve as a bridge to Learning 3.0.
Learning 3.0 has not yet been explored, standardized and conceptualized fully the same way as Learning 1.0 and Learning 2.0, although several people have taken on framing what is meant by Learning 3.0. Learning 3.0 is characterized by interconnectivity and is based on user- and machine-generated content and data. Content is contextually reinvented through the connections it becomes part of while existing data are being re-connected for other smarter uses (Wheeler 2012). So the notion of knowledge creation as a metaphor of learning already present in Learning 2.0 is being highlighted and transferred to Learning 3.0. At the same time, the issues of learning change their focus towards working with emerging knowledge.
Learning 3.0 is linked to the semantic web and is conditioned by connecting people, ideas, questions, devices, data, information, communication and knowledge, so that the processes of learning are based on the confrontation of multiple perspectives through learners engaging with resources, ideas, people, experts and serendipity in ego-centric networks/personal learning networks (PLNs), other kinds of networks and communities, including communities of practice – like the learning processes of rhizomatic learning and networked learning, I compared in Part 5 of this series . But I would like to introduce a broader understanding of the semantic web which includes the Internet of Things (IoT) because it is the intersection of ‘things’ and the networks that connect them and the data, the information, the communication and the knowledge they generate that is the concern with IoT (Weber and Wong 2017) and the basis of interpretations, meaning-making and decisions:
“The internet started by connecting computers; in its second major phase it connected people and organizations. A third major phase of connectivity now emerging is about connections between ‘things’. While there is no precise and agreed definition of IoT, what exists is a proliferation of descriptive phrases which imply that something resembling a difference in kind (not just a difference of degree) is happening or is about to happen at the intersection of these things and the networks that connect them (Bassi and Horn, 2008). Importantly, the ’things’ that IoT will connect subsume and go beyond devices with computational capabilities, to include any and potentially all devices that have some ability to sense their environment or generate data about their interactions with other devices and/or people.” (Weber and Wong 2017)
So Beetham and Shape’s point of view is still counting in Learning 3.0: 21st century pedagogy will involve “engaging learners in apprenticeships for different kinds of knowledge practice, new processes of inquiry, dialogue and connectivity” (McLoughlin and Lee 2008:12), but the conditions for and the consequences of working with data in a world of complexity and uncertainty become a new issue with Learning 3.0, I would say. Also, when knowledge creation as a metaphor of learning has an increased focus on sensing and working with emerging knowledge in order to produce new knowledge, it means that knowledge creation very easily becomes equated with innovation. But it might be a too narrow view on knowledge creation. The creation of new knowledge not only includes understanding a domain, a discipline or a subject matter as a system of thought with its own codes, methods, strengths and limits, as I quoted Scott earlier, but it also includes working with creativity and imagination, serendipity and bricolage in a context of contingency and complexity, so that the processes of producing new ideas, new concepts and new knowledge show ways of evolving and using a knowledge base, in Haythornthwaite’s sense, that possibly involves data. It is a process of creating knowledge that is in interaction with the semantic web, but it is also challenged by it and by artificial intelligence. I have already discussed knowledge production in relation to rhizomatic learning in such a way in Part 5 of this series, that rhizomatic learning clearly qualifies as a pedagogy that agrees with Learning 3.0, and I just want to add that I still see the rest of the pedagogies on my list as suitable for Learning 3.0, too. The list is to be found in The End No 1.
A cluster of definitions and perspectives around Learning 3.0
As an example of the somewhat diverse conceptualizations connected to Learning 3.0, I would like to present a cluster of definitions and perspectives around concepts like Education 3.0, Society 3.0, e-learning 3.0, and the knowmad society, that might all merge into an understanding of what Learning 3.0 might become. In her blog post “Education 3.0 and the Pedagogy (Andragogy, Heutagogy) of Mobile learning” (2013), Jackie Gerstein confirms that the connection between self-directed learning, participatory culture and knowledge creation are important in Learning 3.0. In favor of connectivism she states that “Education 3.0 is a connectivist, heutagogical approach to teaching and learning”, building on self-determined learning, or self-directed learning, that involves non-linear learning, connectivity and learner choice. In her blog post Gerstein presents an e-learning grid where the characterization of e-learning 3.0 can be seen as an attempt to defining Learning 3.0 and trying to meet the affordances of the semantic web, but Gerstein also introduces the concept Education 3.0 that goes well together with Learning 3.0. Here Gerstein builds on John Moravec who coins the concepts Society 3.0 and Education 3.0 in his presentation “Towards Society 3.0: A New Paradigm for 21st Century Education” (2008):
According to Morvec, Society 3.0 is an innovation society characterized by conveying technology and social change, expressed by using the notion of ‘the singularity’, and accordingly schools and education need to focus on sharing, remixing and capitalizing on new ideas, on producing new knowledge and on embracing accelerating change rather than fighting it. So knowledge creation as a metaphor of learning is part of John Moravec’s thinking, but here the idea of knowledge creation is closely connected to innovation, and in his talk “Rise of Knowmads” (2013) Moravec states what this focus on innovation means for schools (and education in general, I would add): they need to provide education for learners who are used to learn, unlearn and adapt to new ideas. In his equally sociological and pedagogical analysis and its future-facing aspirations and expectations Moravec is expressing ideas involving theories of the knowledge society and eventually also predicts the rise of the precariate:
In other words, Learning 3.0 must provide spaces and opportunities for teaching and learning that allow for blended and online learning working with emerging knowledge and knowledge creation as ways of evolving and using a knowledge base. And this version of Learning 3.0 involves innovation and the new world of data as part of teaching and learning in a domain, a discipline or a subject matter.
But what should actually decide the top issues for education in the postmodern, or the late modern: an economic, technological and growth perspective according to the ideas of the knowledge society , or the idea of the public good, preparing for life and a common interest in co-creating education and society as part of imagining the future? Steve Wheeler gave a presentation some years ago that pins down his view on Learning 3.0 in the context of technological change, social change and possible futures:
Not surprisingly, IoT, the Internet of Things, turns up in Wheeler’s presentation, but I think that in an educational context the new data, IoT will generate, and the ways of working with data as knowledge practices, that evolve with IoT, are as important as the devices, the computers and other ‘things’, although computers and ‘things’ can provide and mediate learning activities and spaces for learning. Having briefly defined the Internet of Things in their article “The new world of data: Four provocations on the Internet of Things”, Steven Weber and Richmond Y. Wong introduce more fully a pragmatic definition of IoT that foregrounds two components:
“Component 1 is about how data flows. Older distinctions (like Machine-to-Machine or Business-to-Consumer, M2M or B2C) are becoming obsolete in the IoT, where data moves in a more truly networked fashion that disregards most of these boundaries. Component 2 is about the granularity of these data flows./…The Internet of Things combines these two definitional components. As data flows move toward becoming continuous, 24/7, and correlated through networked connectivity with many other data flows of similar granularity, we have something that is more likely to demonstrate a difference of kind and thus be called IoT./…Our definition foregrounds the ‘sensing’ side of the IoT not the ‘acting’ side; it puts sensors rather than actuators at the center of the discussion.” (Weber and Wong 2017).
“Why then, if the Internet has always been an Internet of things, and ubiquitous computing research and deployments have occurred for decades, has the phrase IoT burst onto the scene in the last few years?/…However, we think there is something simpler and more profound in play that marks IoT as something different. Five developments have come together in time to make today’s IoT (and tomorrow’s) something different, possibly in the same way that the development of the World Wide Web was transformative and more than simply an application running on the Internet./ The first ingredient is the rapid decline in cost and size of sensors. The second is nearly ubiquitous and inexpensive wireless connectivity. The third is distributed ‘super’-computing, conveniently masquerading as mobile phones. The fourth is the recent development and widespread deployment of software tools for managing and working with very large data sets. Fifth is the development of an ecosystem of knowledge, techniques, institutions, and capital that purport to make valuable use out of large quantities of data./ Putting those five ingredients together creates a network that has a distinctively higher level of interdependencies and potential classes of applications, and thus deserves a new label like IoT (Zelenkauskaite, et al.,2012).” (Weber and Wong 2017)
We may not anticipate the social and cultural changes that the interconnected, interdependent and boundary-crossing nature of the Internet of Things might lead to, but they will most likely affect our assumptions about what teaching and learning ought to be like. But I would also like to touch on the other side of the Internet of Things: robotics and artificial intelligence. It worries Weber and Wong:
“We have emphasized for the purpose of this paper the sensing (and by implication the data) side of IoT based on a view of comparative developments in computation and robotics. That distinction may very well collapse over time, in which case critical choices about autonomous decision making and where humans do and do not belong ‘in the loop’ will rise to the fore and could re-cast many of the issues we’ve raised. In the longer term the relationship between human and machine autonomy may be the most important choice that IoT presents to individuals and societies;” (Weber and Wong 2017)
Robotics and artificial intelligence are not only threats of the future, it is already a dimension to consider in education today, although many educators and institutions from K-12 to university are still working on reimagining and recasting pedagogies for a digital age and implementing Learning 2.0 in their teaching and learning while considering to what degree they want to cave in. Or they have moved on to Learning 3.0 but are all the same still evaluating how Learning 2.0 and Learning 3.0 might benefit their domain, discipline, subject matter or institution. Nevertheless, I think it is important to be aware of what has already arrived by the back door and is present without us noticing, what is in the melting pot, what are buzz words to critically examine and discuss, and what might influence Learning 3.0, so here is Roy Clariana’s presentation on artificial intelligence in e-learning:
In his presentation Clariana refers to the report “Intelligence Unleashed. An argument for AI in Education” (2016) by Rose Luckin, Wayne Holmes, Mark Griffiths and Laurie B. Forcier. The report introduces artificial intelligence and definitions and issues that are relevant for discussing the implications of artificial intelligence in education, no matter if you are positive, more hesitating or reluctant or purely negative towards the idea. Luckin et al distinguish domain specific artificial intelligence that focuses on one thing (like playing Go or driving a car) from general artificial intelligence which is able to perform any intellectual task a human being can perform, and as they say – which might comfort Weber and Wong a little: “And right now, general AI does not exist.” (Luckin, Holmes, Griffiths and Forcier 2016:15).
One idea connects the report on AI in education and the presentations by John Moravec, Steve Wheeler and Roy Clariana: the notion of ‘the singularity’ shows up as an explicit or implicit point of reference in all of them. The term ‘technological singularity’ by Vernon Vinge coins the idea of a tipping point “…at which an AI-powered computer or robot becomes capable of redesigning and improving itself or of designing AI more advanced than itself. Inevitably, it is argued, this will lead to AI that far exceeds human intelligence, understanding, and control, and to what Vinge describes as the end of the human era…” (Luckin, Holmes, Griffiths and Forcier 2016:15). As an imagination of the future ‘the singularity’ signifies both fears and possibilities that might influence not only the educational thinkers and researchers introduced here but also will affect more generally debates on what education is for and what education is about. And although ‘the singularity’ and general artificial intelligence still belong to the future, this opens up to a posthuman perspective on the intermediating dynamics between the human, the digital computer, computation and things and to the thinking of N. Katherine Hayles.
Learning 3.0 and a posthuman perspective
In an interview a few years ago Hayles – who is intensely concerned about the relations between science, literature and technology – explains her idea of posthumanism:
“Posthumanism as I define it in my book How we became Posthuman (1999) was in part about the deconstruction of the liberal humanist subject and the attributes normally associated with it such as autonomy, free will, self determination and so forth. What I saw happening in the 1980s and 1990s was the rise of thinking about human beings that was in flat contradictions to all these attributes; that was what I called posthumanism. One of its manifestations was the idea that if you capture the informational patterns of the human brain, you could then upload it to a computer and achieve effective immortality. To me this seemed absolutely wrong, even pernicious, because it plays on mere fantasies of cognition and of what constitutes human life. I was, at this point, very concerned to insert embodiment back into the equation.” (Pötzsch & Hayles 2014:95-96).
In an earlier article, Hayles commented on the project of her book in terms a little closer to the cybernetics perspective that is also a part of the book:
“…I argued that a shift was under way from the human to the posthuman. I regard the posthuman, like the ‘human’, as a historically specific and contingent term, rather than a stable ontology. Whereas the ‘human’ has since the Enlightenment been associated with rationality, free will, autonomy and a celebration of consciousness as the seat of identity, the posthuman in its more nefarious forms is construed as an informational pattern that happens to be instantiated in a biological substrate. There are, however, more benign forms of the posthuman that can serve as effective counterbalances to the liberal humanist subject, transforming untrammeled free will into a recognition that agency is always relational and distributed, and correcting an over-emphasis on consciousness to a more accurate view of cognition as embodied through human flesh and extended into the social and technological environment.” (Hayles 2006:160-161).
By the time of the article Hayles had added a fourth stage to the mapping of cybernetics in her book, a stage she calls the regime of computation:
“The characteristic dynamic of this formation is the penetration of computational processes not only into every aspect of biological, social, economic and political realms but also into the construction of reality itself…In highly developed and networked societies such as the US, human awareness comprises the tip of a huge pyramid of data flows, most of which occur between machines. Emphasizing the dynamic and interactive nature of these exchanges, Thomas Whalen (2000) has called this global phenomenon the cognisphere. Expanded to include not only the Internet but also networked and programmable systems that feed into it, including wired and wireless data flows across the electro-magnetic spectrum, the cognisphere gives a name and shape to the globally interconnected cognitive systems in which humans are increasingly embedded. As the name implies, humans are not the only actors within this system; machine cognizers are crucial players as well. If our machines are ‘lively’…they are also more intensely cognitive than ever before in human history.” (Hayles 2006:161)
This is a stage where the Internet of Things and artificial intelligence have become parts of the systems, as far as I can see (but eventually check out Hayles (2009)). It makes it worth considering if the computational metaphor Hayles introduces here can also be seen as a metaphor of learning, eventually being added to the line of metaphors of learning introduced earlier: learning as acquisition, learning as participation and learning as knowledge creation. It would place learning as computation as a metaphor of learning belonging to Learning 3.0. And this brings me back to the interview with Hayles, where she is being asked if we can still account for creativity and change – two aspects most relevant to my narrative of education, teaching and learning in a digital age – if the liberal humanist subject is deconstructed. She replied:
“Why would this deconstruction impede change, creativity, or as others have claimed progress? Can we assume 1) that human beings actually can be isolated from their technological or other contexts, and 2) that humans are the only agents capable of complex cognitive operations? I do not think we can. On the other hand, posthumanist thinking might help us to take a new look at the boundaries between what counts as human, animal, machine, or object. A redrawing of this boundary certainly entails highly political questions that can point either toward an inclusive and progressive, or an exclusory, direction.” (Pötzsch & Hayles 2014:97).
Agency and the posthuman perspective
So while Hayles’ project is focused on deconstructing the liberal humanist subject, she is at the same time reconsidering aspects of Enlightenment thinking through discussing, criticizing and contextualizing the values of liberal humanism which are: “a coherent, rational self; the right of that self to autonomy and freedom; and a sense of agency linked with a belief in enlightened self-interest.” (Hayles 1999:85-86). These are the values Hayles draws on in her comments above, and in the interview the question of agency is being followed up by asking how posthumanism does change received ideas of agency. Hayles says:
“In the version of the human articulated within the liberal-humanist tradition, agency is seen to reside primarily in the individual subject. Individuals can be incorporated into large structures, but it is ultimately the individual that possesses agency. As we move deeper into a highly technological regime and as the technological infrastructure surrounding us becomes more and more complex, it becomes increasingly obvious that human agency cannot ever be seen in isolation from the systems with which humans are in constant and constitutive interaction. In fact the ideas that human agency is paramount appears to be an illusion; as Bruno Latour and others have pointed out, it is a good corrective to see agency as distributed among both human and non-human entities. This is a primary focus of the emerging field of new materialism that looks into how technological, and also biological and social, processes predispose and channel human action.” (Pötzsch & Hayles 2014:97)
This posthuman perspective challenges the values of liberal humanism, the notion of Bildung and the Humboldtian ideals of education I emphasized as important dimensions of a pedagogy for the digital age when I was exploring and discussing rhizomatic learning in previous parts of this series of blog posts. Agency, autonomy, choice and self-directed learning are capabilities closely connected to Bildung and liberal humanist ideals of education, and they are also embedded in the pedagogical principles and strategies of personalization, participation, collaboration and knowledge creation I have framed as characteristic for Learning 2.0. But agency is also questioned by the networked, distributed and heterogeneous aspects of the pedagogies suited for Learning 2.0 and Learning 3.0: the ideas of independence and interdependencies are enmeshed in Learning 2.0 and 3.0 and exceed the idea of individual agency, too.
With seeing agency as distributed among both human and non-human entities, a posthuman perspective rethinks agency more explicitly and conceptualizes agency as distributed and networked. This understanding of agency agrees with seeing subjectivity as networked and distributed, too, and works with the construction of subjectivity in relation to computers and artificial intelligence, for example. The possibility of subjective agency is not ruled out in a posthuman perspective, instead agency is to be seen as the both/and of complexity, so that both human subjectivity and the possibility of agency are transformed through interactions with technology (Flanagan 2014:20-21).
Learning 3.0 and cognitive assemblage
In a recent article N. Katherine Hayles widens her understanding of agency and introduces the concept ‘cognitive assemblage’ as a multi-perspective, multi-layered, multi-dimensional network that involves technical agency as well as human interactions:
“I want to define cognition as a process of interpreting information in contexts that connect it with meaning. This view foregrounds interpretation, choice, and decision and highlights the special properties that cognition bestows, expanding the traditional view of cognition as human thought to processes occurring at multiple levels and sites within biological life forms and technical systems. Cognitive assemblage emphasizes cognition as the common element among parts and as the functionality by which parts connect.” (Hayles 2016:32)
The definition connects knowledge networks with meaning-making and transforms the understanding of networks into a complex of agency and interactions of interpretations, conditions, contexts and meaning-making at multiple levels of data flows and relations between human, machine, animate, thing :
“My reason for choosing assemblage over network (the obvious alternative) is to allow for arrangements that scale up. Starting with cognitive processes occurring at a low threshold – using information to make choises within contexts – cognitive assemblages can progress to higher levels of cognition and consequently decisions affecting larger areas of concern. Other advantages include the notion of an arrangement not so tightly bound that it cannot lose or add parts, yet not so loosely connected that the relations between parts cease to matter; indeed, they matter a great deal. A cognitive assemblage operates through contextual relations at multiple levels and sites, with boundaries fluctuating as conditions and contexts change. Further comparisons emerge through considering the kinds of materialities involved in networks versus those in assemblages. Networks consist of edges and nodes and are analyzed trough graph theory, conveying a sense of a spare, clean materiality. Assemblage, by contrast, allow for contiguity in a fleshy sense – touching, incorporating, repelling, mutating. When analyzed as dynamic systems, networks are sites of exchange, transformation, and dissemination, but they lack the sense of these interactions occurring across complex three-dimensional surfaces, whereas assemblages include information transactions occurring across membranes, involuted and convoluted surfaces, and multiple volumetric entities interacting with many conspecifics simultaneously.” (Hayles 2016:32-33).
With her definition of assemblages as interpretation of information, decisions and meaning-making, Hayles’ shift from network to assemblage reflects the increased focus on contingency, complexity and emergent knowledge, that subsequently has shown itself as an interest in assemblages in education and pedagogy. Hayles’ definition makes room for including the semantic web, the Internet of Things and artificial intelligence, while she weighs the assemblage of Deleuze and Guattari against the assemblage of Bruno Latour. She seems to choose Latour’s understanding of assemblage affected by transformative technologies and the relations that emerge through the processes involving human and non-human actors:
“As a whole, a cognitive assemblage performs the functions identified with cognition – flexibly attending to new situations, incorporating this knowledge into adaptive strategies, and evolving through experience to create new strategies and kind of responses…” (Hayles 2016:33)
And to stress that cognitive assemblages do not belong to her imagination of the future but are already existing as emblems of the digital age and the computational regime, Hayles states: “The most transformative technologies of the later twentieth century have been cognitive assemblages; the internet is a prime example.” (Hayles 2016:34)
If computation is a metaphor of learning in Learning 3.0, then the cognitive assemblage must be considered as part of rethinking pedagogy for Learning 3.0, if we take on Hayles’ thinking. Whether you prefer Deleuze and Guattari’s assemblage, Bruno Latour’s actor-networks, Hayles’ cognitive assemblage or others as the framework for understanding assemblages, the communities, the networks and the ego-centric networks/personal learning networks (PLNs) of Learning 2.0 and Learning 3.0 need to have a place as part of the assemblages of Learning 3.0. As I wrote earlier, the pedagogies on my list can be recast to provide for Learning 3.0, including participation, knowledge creation, innovation and working with data in networked and distributed contexts, and I am aware of that there are both epistemological and ontological questions and issues to consider in relation to rethinking any of the pedagogies on my list. My discussion of rhizomatic learning in Part 5 in this series hopefully shows this while it exemplifies one version of combining pedagogy and the concepts and ideas of rhizome, chaos and order, lines of flight and assemblage adapted from Deleuze and Guattari.
Hayles’ posthuman perspective might provoke, and it does challenge the Enlightenment foundations of education from K-12 to higher education and university when she deconstructs the liberal humanist subject and shakes the traditional preconditions for both legitimacy structures, knowledge practices and Bildung. But it doesn’t end it, “rather ‘human’ and ‘posthuman’ coexist in shifting configurations”, as she puts it (Hayles 1999:6), so reimagining, rethinking and recasting pedagogy for Learning 3.0 still means discussing what education is for and what education is about. Weber and Wong and Hayles help pinning down some of the technological changes that have made and make the semantic web, the Internet of Things and domain specific artificial intelligence transformative, and furthermore Hayles’ project of deconstructing the liberal humanist subject is not only involving the development of cybernetics and issues related to scientific and technological progress but also focuses on the social and cultural changes equally important to uncovering and questioning the ethical and cultural implications of cybernetic technologies (Hayles 1999:21). And here, literature and narrative as fictional representations of the past, the present and the future play an important part in Hayles’ book and help her discussing the social, cultural and ethical implications of scientific and technological transformations while demonstrating that “culture circulates through science no less than science circulates through culture.” (Hayles 1999:21).
I’ll leave the narrative and literary history aspects of Hayles’ project for now and bring this section to an end by implicating that embedded in her thinking there is not only an aesthetic and anthropological role but also a societal role for literature to play (Flanagan 2014:5-6), indicating that science, literature and technology are more intertwined than we are used to think:
“Literary texts are not, of course, merely passive conduits. They actively shape what the technologies mean and what the scientific theories signify in cultural contexts.” (Hayles 1999:21).
In other words, literature and narrative belong as part of cognitive assemblages with the double agenda of reinscribing traditional ideas and assumptions but also articulating something new (Hayles 1999:6), and this way assemblages are making historically processes visible, embodied and interpreted as emerging knowledge and as a melting pot of knowledge creation.
Learning for an unknown and unexplored future
In his essay “Education: under, for and in spite of postmodernity” (2001), Zygmunt Bauman gives his view on what education is for and what education is about today:
“’Preparing for life’ – that perennial, invariable task of all education – must mean first and foremost cultivating the ability to live daily and at peace with uncertainty and ambivalence, with a variety of standpoints and the absence of unerring and trustworthy authorities: must mean instilling tolerance of difference and the will to respect the right to be different, must mean fortifying critical and self-critical faculties and the courage needed to assure responsibility for one’s choices and their consequences; must mean training the capacity for ‘changing the frames’ and for resisting the temptation to escape from freedom, with the anxiety of indecision it brings alongside the joys of the new and the unexplored.” (Bauman 2001:138)
“The point is, though, that such qualities can hardly be developed in full through that aspect of the educational process which lends itself to the designing and controlling powers of the theorists and professional practitioners of education: through the verbally explicit contents of curricula and vested in what Bateson called ‘proto-learning’. One could attach more hope to the ‘deutero-learning’ aspect of education, which, however, is notoriously less amenable to planning and to comprehensive all-out control. The qualities in question can be expected to emerge, though, primarily out of the ‘tertiary learning’ aspect of educational processes, related not only to one particular curriculum and to the setting of one particular educational event, but precisely to the variety of criss-crossing and competing curricula and events.” (Bauman 2001:138)
So to realize learning for a present of complexity and for an uncertain future, where knowledge is abundant and dynamic, continuously changing and emergent, Bauman identifies three forms of learning – following Gregory Bateson’s theory of learning: primary learning, secondary learning and tertiary learning. Bateson’s three forms of learning condense an ecological perspective on the relations and mutual influences of individuals and culture on human meaning-making and knowledge and the way learning is structured. Bauman’s and Hayles’ agendas are criss-crossing here, as Bateson was also participating in the foundational debates on cybernetics, uncovered and disussed by Hayles in “How we became posthuman”.
Bateson’s three forms of learning express an expectation of how to think of the processes of learning and how to organize for learning, and Bauman claims that they all belong and need to be practiced in education today, although Bateson regarded tertiary learning as abnormality when he first introduced his theory.
Primary learning – or proto-learning – is the content, curriculum and the learning of something already known to the educator and the domain, discipline or subject matter and with learning being planned and designed for a certain outcome. Primary learning is ‘to learn’.
Secondary learning – or deutero-learning – is learning how to expect and handle sets of alternatives in specific contexts despite the contingencies one encounters. Secondary learning is ‘learning how to learn’ and the understanding of ‘learning to learn’.
Tertiary learning is learning “when the subject of education acquires the skills to modify the set of alternatives which they have learned to expect and handle in the course of deutero-learning” (Bauman 2001:124). It is the need “to reassemble an established conceptual network” to make it able “to capture novel phenomena in a new cognitive frame” that constitutes tertiary learning (Bauman and Mazzeo 2016:82). Tertiary learning is ‘re-learning’, that is learning something unpredictable and unknown to both learners and educators (Bauman 2001:123-126; Dahlbeck 2011:78). And this re-learning is synonymous with emerging knowledge, the new and the unexplored that Bauman mentions in the quote above.
Eventually, Bateson also operates with a fourth degree of learning where the social context and ‘the store of learning’, the collective stock of experience and knowledge, and how it is shared and used, is seen as decisive for the process of teaching, learning and ‘re-learning’ (Bauman 2001:121). This fourth degree of learning is culture and thus contains the predictions of the social contexts that cultivate and direct emerging knowledge. It is both the backdrop and the result of the first three forms of learning, but Zygmunt Bauman only touches shortly on this degree of learning in his essay.
No doubt that Bauman supports cultivating tertiary learning in a liquid, rapidly changing world of complexity as quoted earlier, but it is a formative process we don’t have traditions for in education, he says, and a way of learning that can’t be planned or organized for as we traditionally do when designing learning. Whether we like it or not, tertiary learning is necessary in a world of constant change. So the three forms of learning active in Bauman’s analysis of education are as much to be considered at an institutional level as at the level of the individual educator, learner and student:
“These times of ours excel in dismantling frames and liquidizing patterns – all frames and all patterns are random and without advance warning. Under such circumstances ‘tertiary learning’ – learning how to break the regularity, how to get free from habits and prevent habitualization, how to rearrange fragmentary experiences into heretofore unfamiliar patterns while treating all patterns as acceptable solely ‘until further notice’ – far from being a distortion from its true purpose, acquires a supreme adaptational value and fast becomes central to what is indispensable ‘equipment for life’.” (Bauman 2001:125)
So, as I mentioned a little while ago, what for Bateson could be viewed as abnormality is now normal, Bauman states, and this tertiary approach to learning has become common. It is an approach that is echoed in John Moravec’s claim that schools need to provide education for learners who are used to learn, unlearn and adapt to new ideas.
As such, Bateson’s three forms of learning are not identical with Learning 1.0, Learning 2.0 and Learning 3.0, but in some ways they seem to merge in the present historical context of the postmodern, or the late modern. It is three of the metaphors of learning, learning as participation, learning as knowledge creation and learning as computation, that act as specific historical metaphors because they draw on present technological, social and cultural changes. They connect the ideas, the knowledge practices and the concepts of the dynamic changing learning modes in Learning 1.0, Learning 2.0 and Learning 3.0 with the stable forms of learning in Bateson’s theory of learning. And as Bauman has emphasized already, Bateson’s three forms of learning all belong and need to be practiced in education today.
Learning 2.0 has not become obsolete or outdated by the progressing of Learning 3.0 – they are existing as parallel modes of learning that are relevant to different degrees and in different combinations depending on the domain, the discipline or the subject matter. Secondary learning can make learners creative and provide them with agency both in the liberal humanist sense, based on self-cultivation adn Bildung, that Bauman supports, and in Hayles’ posthuman sense. In many ways secondary learning, as framed by Bateson and Bauman, is embedded in Learning 2.0 and the understanding of how pedagogies might look like to align with Martin Weller’s and Caroline Haythornthwaite’s new models of education. But tertiary learning, which challenges ‘old’ frameworks, conceptualizations and understandings of education and learning might also become synonymous with reimagining and rethinking pedagogies and practices for Learning 3.0 – marked by complexity and uncertainty and for a future of the unknown. The pedagogies on my list in The End No 1 involve secondary learning and all have the potential to initiate, integrate and stage tertiary learning as they focus on personalized, participatory and social learning but also on learning something unpredictable and unexplored.
And as mentioned rhizomatic learning is an example of rethinking pedagogy for Learning 3.0 that through its practices opens up to tertiary learning. The way rhizomatic learning promotes non-linearity, heterogeneity, unpredictability and complexity, it places itself as a pedagogy and learning approach that challenges traditional primary learning and implies secondary learning and knowing how to learn while it ideally also initiates tertiary learning in a postmodern context of uncertainty, multiplicity and rapid change. At least that is what Dave Cormier intends with rhizomatic learning as a vision of learning and innovation when he is coupling it with Dave Snowden’s knowledge management model “The Cynefin Framework”. The tertiary learning aspect of educational processes are not related “to one particular curriculum and the setting of one particular educational event” as I quoted Bauman earlier, and this is also what Dave Cormier strongly advocates for in his article “Rhizomatic Education: Community as curriculum” (2008).
The aspects of tertiary learning are not something you teach but it might be the result of a design for learning that gives learners the opportunity to experience, experiment and explore a way of learning and re-learning that is necessary in a world of uncertainty and rapid changes: it is a way of learning that focuses on the variety of criss-crossing and competing curricula and educational events that you might meet in communities, networks and assemblages. But it also aims at developing new knowledge and thus pedagogies must be able to design for learning that challenges the capacity for ‘changing the frames’, works with emerging knowledge and strives to stay open-ended. And eventually they might be inspired by N. Katherine Hayles’ cognitive assemblage as a concept of the computational regime as well as the embodiment of technological, social and cultural practices in a digital age.
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