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New, emerging or disruptive


What is an ‘emergent technology’?

Definitions proposed by a number of studies overlap, but also point to different characteristics. For example, certain definitions emphasise the potential impact emerging technologies are capable of exerting on the economy and society (e.g. Porter et al., 2002), especially when they are of a more 'generic' nature (Martin, 1995), while others give great importance to the uncertainty associated with the emergence process (e.g. Boon and Moors, 2008) or to the characteristics of novelty and growth (e.g. Small et al., 2014). The understanding of emerging technologies also depends on the analyst's perspective. An analyst may consider a technology emergent because of its novelty and expected socio-economic impact, while others may see the same technology as a natural extension of an existing technology. Also, emerging technologies are often grouped together under 'general labels' (e.g. nanotechnology,

synthetic biology), when they might be better treated separately given their dierent socio-technical features (e.g. technical difficulties, involved actors, applications, uncertainties) (Rotolo, Hicks & Martin, 2015, p.2).

The result is the delineation of five key attributes that qualify a technology as emerging.

These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. Specifically, we conceive of an emerging technology as a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the composition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous (Rotolo, Hicks & Martin, 2015, p.4).

Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology?. Research Policy, 44(10), 1827-1843.

What is a ‘disruptive’ technology?

A disruptive innovation is not a breakthrough improvement. Instead of sustaining the traditional improvement trajectory in the established plane of competition, it disrupts that trajectory by bringing to the market a product or service that actually is not as good as what companies historically had been selling. Because it is not as good, the existing customers in the back plane … cannot use it. But by making the product affordable and simple to use, the disruptive innovation benefits the people who had been unable to consume the back-plane product – people we call “non-consumers”. Disruptive innovations take root in simple, undemanding applications in what, as depicted in the front of Figure 2.1, is a new plane of competition – where the very definition of what constitutes quality, and therefore what improvement means, is different from what quality and improvement meant in the back plane (Christensen, Horn & Johnson, 2008, p. 47).


In the years when the companies must commit to the innovation, disruptions are unattractive to the leaders because their best customers can’t use them, and they promise lower profit margins… One factor that makes it so hard for the incumbent leader to pursue a disruptive innovation is that the way the product performance is defined in the disruptive market is antithetical to the sorts of improvements that are required to succeed in the original market (Christensen, Horn & Johnson, 2008, pp. 50-51).

To succeed, disruptive technologies must be applied in applications where the alternative is nothing. Indeed, selecting these applications is far more important for the successful implementation of the technology than is the technology itself (Christensen, Horn & Johnson, 2008, p. 74).

In almost every case, when disruptive innovations emerge, the industry leaders see the disruptive change coming … But their instinct was to utilize their existing business infrastructure and sell the disruptive products to their existing customers …

Disruptive markets are by definition small at the outset because disruptive products compete against non consumption (Christensen, Horn & Johnson, 2008: 79).

Public education enrollments in online classes … are exhibiting the classic signs of disruption as they have skyrocketed from 45, 000 in 2000 to roughly 1 million today …If the history of these types of innovation can serve as a guide, the disruptive transition from teacher-delivered to software –delivered instruction is likely to proceed in two stages. We call the first of these stages computer-based learning. In this stage, the software will be proprietary and relatively expensive to develop; and it will be monolithic, with respect to students’ types on intelligence and learning styles. .. The second phase of this disruption we term student-centric technology, in which software has been developed that can help students learn each subject in a manner that is consistent with their type of intelligence and learning style (Christensen, Horn & Johnson, 2008, p. 91-92).

Four factors will drive the substitution. First, computer-based learning will keep improving, as all successful disruptions do… A second driver of this transition will be the ability for students, teachers, and parents to select a learning pathway through each body of material that fits each of the types of learners – the transition from computer-based to student-centric technology… The third factor that will likely fuel the substitution is a looming teacher shortage … The fourth factor is that costs will fall significantly as the market scales up (Christensen, Horn & Johnson, 2008, p. 100-101).

A profession whose work primarily was in tutoring students one on one was hijacked into one where some of the teacher’s most important skills became keeping order and commanding attention (Christensen, Horn & Johnson, 2008, p. 111).

If we indeed want to begin teaching subjects to students in ways that correspond to how their minds are wired to learn, it means that the science of assessment will need to evolve significantly (Christensen, Horn & Johnson, 2008, p. 112).

Christensen, C., Horn, M., & Johnson, C. (2008). Disrupting class. How disruptive innovation will change the way the world learns. New York: McGraw Hill.


Can technology be separated from society at large in any meaningful way?

We are often tempted to see technology as something that can be considered in isolation from its uses in real-world contexts, such as education. The relationship between technology and society at large is a good deal more complex than that. One aspect of this is the relationship between technology and culture - it is often very difficult to think of ‘Western Culture’ without the integral technologies that co-define it.

Another aspect more relevant to our purposes, is the relationship between technology and learning. In the extracts below, George Veletsianos (2016) grapples with the idea that it is virtually impossible to separate ‘emerging technologies’ from ‘emerging educational practices’. Although educationalists advise learning designers to consider learning outcomes before they consider the technologies that will enable these outcomes, learning design in the real world is often quite different. Often, learning designers ‘appropriate’ new technologies for their own educational purposes. For example, when video-conferencing software (think Google Hangout, Skype, etc.) first became available, it offered learning designers the opportunity of designing online, real-time virtual classrooms. This was an affordance of the technology that was ‘appropriated’ by learning designers for educational purposes. In this sense technology determines innovative learning practices at least as much as innovative learning practices demand emerging technologies - the processes are ‘mutually constitutive’.

As noted earlier, this chapter argues that what makes technologies and practices emerging are not specific technologies or practices, but the environments in which particular technologies or practices operate. This definition recognizes that learning, teaching, and scholarship are sociocultural phenomena situated in specific contexts and influenced by the cultures in which they take place (Brown, Collins, & Duguid, 1989; Vygotsky, 1978). This perspective is particularly appropriate for digital learning situated on the contemporary Web which has social and co-producing capabilities and practices. According to this view, technology is itself socially shaped. It embeds its developers’ worldviews, values, beliefs, and assumptions into its design and the activities it encourages (Oliver, 2013). (Veletsianos, 2016, p.6)

To provide an example of why it makes sense to consider technologies and practices as emerging, consider online journals and social media such as Facebook, Twitter, and YouTube. These technologies have become an integral part of open scholarship, which is often seen as a major breakthrough in radically rethinking the ways in which knowledge is created and shared (Nielsen, 2012; Weller, 2011). Much of the existing literature argues that scholars can amplify and transform their scholarly endeavors by adopting open practices supported by technology, and a multitude of ways to do so have been developed (Veletsianos, 2013). For instance, a cultural anthropologist might share draft versions of her research on her blog, a geographer might post his syllabus on a document-sharing website, a World War II historian might enlist the help of online crowds to obtain digital copies of letters to examine personal communication during the era, and a political scientist might use social media data to investigate political campaigns during elections. These are examples of the emerging practice of Networked Participatory Scholarship (Veletsianos & Kimmons, 2012; Veletsianos, 2016), which refers to the use of participatory technologies, online social networks, and other emerging technologies to share, reflect upon, improve, validate, and further scholarship … After an extensive examination of these emerging educational phenomena and the literature about them, all appear to share these four characteristics: not defined by newness; coming into being; not-yetness; and, unfulfilled but promising potential. (Veletsianos, 2016, p.7).

Veletsianos, G. (2016). The Defining Characteristics of Emerging Technologies and Emerging Practices in Digital Education. In G. Veletsianos (Ed.), Emergence and Innovation in Digital Education (pp. 3-16). Edmonton: AU Press. Retrieved from:

Net- aware theories of learning

The question is: what is it about current and emerging technologies that enables innovative learning practices - and how do these practices relate to existing and emerging theories of learning? This question is addressed by Terry Anderson (2016) in the following extracts and in the associated chapter:

The Internet (or net) context created an environment that is radically different from pre-net contexts, but carries with it evolutionary genes from previous cultures and technologies. There are three affordances of the web that define its value for teaching and learning (Anderson & Whitelock, 2004).

First, the net offers the capacity for powerful, yet very low-cost, communications…. Second, the net created a context of information abundance…. The third affordance is the development of active and autonomous agents, which are free to gather, aggregate, synthesize, and filter the net for content and communications relevant to individuals and groups of learners and teachers (Anderson, 2016, pp. 40-41).


The self-determinism that defines heutagogical approaches to teaching and learning is seen as critical to life in the rapidly changing economy and cultures that characterize postmodern times. As Hase and Kenyon (“Heutagogy,” 2000, para. 6) note, “heutagogy looks to the future in which knowing how to learn will be a fundamental skill given the pace of innovation and the changing structure of communities and workplaces.” This future demands that education move beyond instructing and testing for learner competencies, and toward supporting learners in a journey to capacity rather than competency. Capacity includes being able to learn in new and unfamiliar contexts. Older models of competence test only the time-dependent achievement of the past. Instructional design for heutagogical learning veers away from prescriptive content to an exploration of problems that are relevant to the learner (chapter 8 and 9). The instructor becomes a facilitator and a guide in learners’ interactions with varied resources to resolve problems and to gain personal understanding. Heutagogy thus emphasizes self-direction and focuses on the development of efficacy in utilizing the online tools and information available.(Anderson p. 42).



The metaphor of the network, whose nodes consist of learning resources, machines to store and generate information, and people, is one that dominates connectivist learning. Learning occurs as individuals discover and build connections between these nodes. Learning environments are created and used by learners to access, process, filter, recommend, and apply information with the aid of machines, peers, and experts within the learning network. In the process, learning expands based on the power of the network to create and personalize knowledge, connections, and artifacts of those within it. Being able to see, navigate, and develop connections between nodes is the goal of connectivist learning. Rather than learning facts and concepts, connectivism stresses learning how to create paths to knowledge when it is needed. Siemens also argues that knowledge, and indeed learning itself, can exist outside the human being — in the databases, devices, tools, and communities within which a learner acts. A goal of connectivist learning is to create new connections, regardless of formal education systems, to expand upon and build learning networks. Connectivist theorists are primarily interested in allowing and stimulating learners to create new learning connections. In the process, learners are expected to increase the pool of expertise and resources that they can draw from, to increase social capital, and to curate valued resources (Anderson, 2016, p.43).

Groups, nets and sets

Dron and Anderson (2014) expanded the discussion of social networks and interactions within formalized education; specifically to differentiate three important but substantively different contexts in which connectivist learning is employed.

The first of these learning contexts is the familiar group. Groups, typically referred to as “classes” in formal education systems, are secure places where students aggregate (face-to-face or online) and proceed through a series of independent and/or collaborative learning activities … A second form of aggregation is called the network. Networked learning activities expand connectivity beyond the learning management system (LMS) to allow learners, alumni, and the general public to engage in formulating networked learning opportunities (see chapter 9). Network membership is much more fluid than that of groups, where leadership is emergent rather than imposed and networks easily expand or contract as learners use the network to solve problems. Networks are less temporally bonded and may continue to exist long after formal study terminates. … The third aggregation we call the set. Sets are created by a shared interest or characteristic, and can be of enormous value in education. For example, when an instructor polls a classroom (using a show of hands or clickers), this method helps determine the set of students who correctly understand a concept. More recently wikis have had the ability to aggregate and extract knowledge from the set of individuals with interest/expertise in any topic. Learning in sets involves aggregating and synthesizing the myriad activities that occur in online environments (Anderson, 2016, pp.44-45).

Threshold concepts

The theory of threshold concepts identifies attributes that impact teaching and learning issues: “Threshold concepts are ‘conceptual gateways’ or ‘portals’ that lead to a previously inaccessible, and initially perhaps ‘troublesome’, way of thinking about something” (Meyer & Land, 2005, p. 373–74).

Of particular interest is the notion that changing one’s approach and behaviour, and thus one’s design, through the application of emerging technology involves instructors wrestling with very significant “threshold concepts”—what Ross and Collier call “messiness” in chapter 2. McGowen (2012) identified two such thresholds that instructors must experience:

First they may have a preconception that technology is merely an add-on, not an integral part, of teaching; and, second, they believe that they should know exactly what they are doing before using new technology in the classroom, resisting a period of experimentation, or even play, that others find helpful when teaching with technology. (p. 25)

Meyer and Land (2005) identified four characteristics of threshold concepts:

Transformational. The ideas of learner centeredness, produsage of content, extensive sharing with peers and other features of the current generation of emerging technologies force a transformation of teachers from source of information to facilitator of learning (chapters 5, 11). The technologies also spill out beyond professional practice to both support and challenge activities in many other social, political and commercial activities.

Integrative. Following from complexity theory, new adopters find that the use of emerging technologies tends to open new possibilities while making others redundant. Only through deeper understanding can educators learn to change parts of their environment to integrate with the changes induced by the use of emerging technologies and practices.

Irreversible. Learning to teach (as we were taught to teach or observed other teachers) forced us across threshold concepts. Teaching effectively with emerging technologies, likewise, forces educators to relearn, to reconceptualize, and to abandon obsolete practices.

Troublesome. Emerging technologies and practices, like any substantive change, challenge older ways of doing things, which are often defended by the vested interests of learners, instructors, and institutions (Anderson, 2016, pp. 46-47).

Anderson, T. (2016). Theories for learning with emerging technologies. In G. Veletsianos (Ed.), Emergence and Innovation in Digital Education (pp. 35-50). Edmonton: AU Press. Retrieved from:

The problem of the vanishing expert?

In the not-too-distant past, if we needed to learn something, we would almost certainly interact with an expert, either directly with an instructor or indirectly through some form of media (such as text documents, documentaries, photos, museum exhibits). In any of those scenarios, the source of information was filtered before it reached the learners (our teachers had to have received a set of credentials, the newspaper or book would have been edited by someone with recognized expertise). Shirky (2008) refers to this idea as the “filter then publish” model. If we eventually acquired enough information and received the appropriate degrees, we were then deemed as recognized experts ourselves, ready to be sought out by others … There is no guarantee, however, that when searching the Web we will find information that has authoritative weight. Is this a problem for us as educators or for education in general? If so, when is it a problem and when does it become a problem? What, if anything, should be done to address it? (Wellburn & Eib, 2016, pp. 65-66).

Prior to the participatory web, there was a clear distinction between an audience and a recognized author. The author was the rare individual who had enough information or talent to make it worthwhile financially to create an expensive publication; the audience was the rest of us who received that publication (or film, play, etc.). Authors who were rejected by traditional publishing houses could self-publish but this was an expensive proposition. Today, a writer can self-publish an e-book or buy specific services from companies who assist self-publishers (Wellburn & Eib, 2016, 67).

Like expert/amateur and audience/author, the roles of learner and educator are increasingly becoming intermingled in the participatory web. Teachers have typically felt the pressure to keep up-to-date in their field, but it is a profound change that both the learner and the teacher have identical access to the same vast set of resources. Students are spending more time on the Internet than in the classroom as they increasingly look to it for information and news (Johnson et al., 2014, p. 32). Even more of a dilemma is the possibility that the learner may have a potential advantage by being more familiar with digital skills acquired through online participation (such as image manipulation, keyword refinement, etc.).

Such digital literacy can also lead learners to engage with information in new ways. Downes (2008) discusses how web technologies have fostered a more informal type of learning “based on a student’s individual needs, rather than as predefined in a formal class, and based on a student’s schedule, rather than that set by the institution.” He goes on to describe how such informal learning involves “no boundaries; people drift into and out of the conversation as their knowledge and interests change” (Downes, 2008), and this concept has been integrated into the learning design he favours for massive open online courses (MOOCs), wherein learners participate in connectivist-oriented MOOCs (see chapters 2 and 9) in a similar fashion, drifting in and out as needed (Wellburn & Eib, p.69).

Emerging approaches to education that are sometimes informed by such attitudes, such as MOOCs and competency-based models, are attracting a lot of attention—both positive and negative. What are the roles of teacher and student in a course with 30,000 students enrolled? What are the roles in self-paced courses with no instructor? Can MOOCs offer an effective way to move from formal education to personal learning (chapters 8 and 9)? Alternative assessment methods are being explored in an effort to recognize informal learning through badges and other micro-credentials … One of the forms this apprehensiveness might take relates to concerns that the breadth and immediacy of informational access that new technologies facilitate could replace depth and analysis. A new responsibility seems to be upon us: to ensure that our learners have the opportunity to develop skills and literacies that are appropriate for deep learning from (or in spite of) the published but unfiltered information they are currently encountering (Wellburn & Eib, p.70).

The question then becomes, are we fully exploring the affordances of the web with appropriate pedagogies and ways of thinking about education and learning in investigating and embracing emerging models of distance education? Media literacy is an important key to effective education in a participatory learning environment. Wesch states, “There are no natives” (2008a). Given that the online environment is largely new to both educators and learners (and that it is changing constantly), we must not assume students are media literate (Wesch, 2008a). As an example, Wesch mentions that a large proportion of his students did not know that Wikipedia was editable and many had never edited a wiki of any sort. And since new tools are appearing nearly every day, media literacy strategies are more important than specific details about specific platforms (Wellburn & Eib, p.74).

Shirky (2008c) counters Carr’s (2008) argument that we are not reading as deeply in the era of abundance by declaring, “every past technology I know of that has increased the number of producers and consumers of written material, from the alphabet and papyrus to the telegraph and the paperback, has been good for humanity.” Although emerging technologies provide increased opportunities to solve problems, Keen (2007) worries we will falter by having too much freedom and too much access to information not created by recognized experts. Shirky agrees that Keen (2007) poses a hard question that must be answered and Carvin (2008) asks educators to avoid the “wide-eyed cheerleader” point of view and recognize the challenges.

Part of the solution may come from the emerging technologies themselves, and the emerging practices that they make available. In the near future, there may well be technologies that evolve to provide authority to certain information. For example, Internet founding father Tim Berners-Lee (2008, interviewed by Ghosh) is working on a project to provide scientific websites with reliability ratings, something he sees as being crucial for particular types of content (e.g., medical information/ advice). But in general, as Keohane (2008) notes about Wikipedia, and by association Web 2.0, user-generated content is largely self-correcting.

What is required are ways to ensure that user self-correction is ongoing and that users keep track of where any particular piece of information might be in that self-correction process (the first iteration of a Wikipedia article may be suspect; after a thousand edits, it may well be a highly reliable source). In many ways this reflects what critics have always been calling for: critical thinking and a type of virtual “street smartness.” Without that awareness, the perils are indeed real. With awareness, the potential, in the view of all but the harshest critics, is truly amazing. Can we move forward, with a spirit of adventure, applying our imagination and inventiveness to authentic questions (Wellburn & Eib, p.76).

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