Christensen and Eyring (2011) The Innovative University

At times, The Innovative University reads more like an extended panegyric to Harvard than an analysis of the impact of Disruptive Technologies and Disruptive Innovation (1997, 2003) on higher education. However, Christensen and Eyring do reiterate some of the points made in Christensen’s earlier writings on disruption, or the lack of it, in learning and teaching: ‘Since the time that universities first gathered students into classrooms, the learning technologies… have remained largely the same. Even when computers were introduced into the classroom, they were used to enhance the existing instructional approaches, rather than to supplant them. Lectures, for example, were augmented with computer graphics, but the lecture itself persisted in its fundamental form’ (2011, p.18). New tools have arisen to facilitate and potentially enhance learning and teaching, but they have been located within an existing activity system (Engestrom, 1987), rather than prompting new learning and teaching paradigms.

However, and given the rapidly changing economics of higher education (most noticeably the substantial increase in fees), there is a possibility that expectations of higher education will alter, and that the provision of higher education will diversify. Christensen and Eyring point out a core fact of higher education in the US, ‘Since the late 1980s, college tuition and fees have risen 440 percent, four times faster than inflation’ (2011, p.202), a phenomenon which, in other circumstances, would look like a bubble.

In common with other commentators, therefore, Christensen and Eyring are interested in the purposes of higher education in a rapidly shifting economic context: ‘Now, with a college education becoming simultaneously more expensive and a precondition to earning a living wage, there is a temptation for students and policymakers to focus on making the fundamental product – a degree – more affordable; in the face of today’s wrenching economic and social pressures it is natural for not only marketers of higher education but also customers to become myopic’ (2011, p. 332). Hence, a utilitarian outlook on higher education is understandable, and defensible. However, they see higher education having other possibilities, and obligations: ‘Yet the job that students and policymakers need done is the bestowal of the insights and skills necessary not to just make a living but to make the most of life. A college degree creates its significant wage-earning advantage because it is designed with more than mere economic goals in mind’ (2011, p. 332).

Higher education has a job to do which extends beyond simple economics. Therefore, universities need to be responsive to wider social contexts. One significant aspect of changing contexts over the last twenty years has been the emergence and rapid embedding of the internet. Moreover, the internet is now a core aspect of students’ learning lives and may, furthermore, be diluting previous demarcations between learning, work and leisure, demarcations which have been in place since the industrialisation of western societies in the eighteenth and nineteenth centuries. Therefore, universities need to find new ways of working with the technologies with which learners and teachers interact on a daily basis: ‘Universities have grown larger, more complex, and more expensive, but their basic character still reflects decisions made in the late nineteenth and early twentieth centuries’ (2011, p. 379). According to Christensen and Eyring, this can’t continue.  

References

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth,CambridgeMA, Harvard University Press.

Christensen, C. M. and Eyring, H. J. (2011) The Innovative University: Changing the DNA of Higher Education from the Inside Out, San Francisco, Jossey-Bass.

Engeström, Y. (1987) Learning by expanding: an activity-theoretical approach to developmental research,Helsinki, Orienta-Konsultit Oy. http://lchc.ucsd.edu/MCA/Paper/Engestrom/expanding/toc.htm (accessed 15 March 2012).

Activity Theory round up

Kaptelinin et al. (1999) give synoptic definitions of what Activity theory is and how it can be applied. In common with Disruptive Technology and Disruptive Innovation (1997, 2003), Activity theory is not predictive (1999, p. 28).

Kaptelinin et al. assert the dynamic role of tools in activity systems, as tools acquire usage through meaning ,and influence the thought and conduct of users:   ‘… a tool comes fully into being when it is used and… knowing how to use it is a crucial part of the tool. So, the use of tools is an evolutionary accumulation and transmission of social knowledge, which influences the nature of not only external behaviour but also the mental functioning of individuals’ (p.32).

Moreover, as meaning evolves from usage it is relevant to observe usage over time and thus observe the construction of meaning within an activity system; ‘It is important to understand how tools are not used in a single instant of trying them out in a laboratory (for example) but as usage unfolds over time. In that time, development may occur making the tool more useful and efficient than might be seen in a single observation’ (1999, p. 32). 

Whitworth (2005) argues that ‘Conflict within organisations is inevitable, but without conflict there would be no creativity, and hence no innovation’ (p. 690). However, Benson and Whitworth (2007) challenge an understanding of activity systems, namely that all contradictions therein need to be removed. Instead, they argue, ‘… tensions within activity systems are not inherently divisive… “best practice” may entail understanding the tensions within activity systems, rather than believing them to be troublesome variables, better eradicated’ (2007, p.79). Subsequently, Benson et al. (2008) draw attention to nodes within Engestrom’s (1987) representation of the activity system, arguing that ‘Rules, roles and tools are as much the territory of centralised economic and political forces as they are for learning and teaching’ (2008, p.466).  Hence, activity systems are not hermetic, as individual nodes within the activity system are shaped by wider economic, political and social factors.

 

References

Benson, A. D., and Whitworth, A. (2007) ‘Technology at the planning table: Activity theory, negotiation and course management systems,’ Journal of Organisational Transformation and Social Change, vol. 4, no. 1, pp. 75-92.

 

Benson, A., Lawler, C. And Whitworth, A. (2008) ‘Rules, roles and tools: Activity theory and the comparative study of e-learning,’ British journal of Educational Technology, vol. 39, no. 3, pp. 456-467.

 

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

 

Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth, Cambridge MA, Harvard University Press.

 

Kaptelinin, V., Nardi, B., and Macaulay, C. (1999) ‘the Activity Checklist: A Tool for Representing the “Space” of Context,’ Interaction, vol. 6, no. 4, pp. 27-39.

 

Whitworth, A. (2005) ‘Colloqium’ British Journal of Educational Technology, vol. 36, no. 4, pp. 685-691.

Leadbeater, C. (2011) ‘The Civic Long Tail’

Leadbeater (2011) writes about ‘The Civic Long Tail,’ whereby social networking has the potential to revivify government’s relationship with the citizen. However, the examples he gives, and the proposals he offers, intersect with Christensen’s work on Disruptive Technology (1997) and Disruptive Innovation (2003, 2011).

Leadbeater’s example of the success of the London Fire Brigade is telling, because their success did not depend on a full-frontal solution (bigger, faster, more efficient fire engines) but on a targeted campaign to increase the use of smoke alarms (pp. 19-20). This tallies with Christensen’s analysis of the success of disruptive innovations:  ‘A major lesson from our studies of innovation is that disruptive innovation does not take root through a direct attack on the existing system. Instead, it must go around and underneath the system’ (2011, p. 243). As Christensen writes, elsewhere, of disruptive technologies: ‘Products based on disruptive technologies are typically cheaper, simpler, smaller, and, frequently, more convenient to use’ (Christensen 1997, p. xv).

Leadbeater also cites Charles Armstrong’s work on emergent democracy, whereby Armstrong argues that democracy is a bottom-up phenomenon, ‘a means to scale local traditions of self-government to the much larger societies, cities and nations created by industrialisation and urbanization. That meant self-government had to become more formal and structured, following clear  rules and procedures but at the cost of becoming more rigid  and less agile’ (2011, p. 22). Hence, democracy is disruptive initially, but as democracy becomes more structured and formalised its disruptive potential weakens, perhaps necessitating further disruption to revivify democracy itself.  

Christenson (2003) argues it is not the case that a new provider has mastery over a technology, whereas established providers don’t. Instead, the established provider finds that the innovation does not fit within its strategy; it is undesirable, not unattainable. Meanwhile, the disruptive provider gains a foothold and builds support. Leadbeater argues that established providers are unable to perceive the potential of disruption: ‘it is also almost inevitable that powerful incumbents heavily invested in established ways of doing things fail to recognise new needs and the potential of disruptive new technologies…. However, when technologies, consumer expectations and organizational possibilities all shift at the same time – as they are now – it often becomes difficult for established companies to continue to control their industries. New entrants emerge to pioneer new business models, which meet emerging customer needs in more effective ways. Often these new approaches come from upstarts and outsiders carrying little baggage’ (2011, p. 23). 

Leadbeater’s core argument is that innovations evolve into systems and thus lose their innovative qualities, thereby creating the innovation vacuum for a new provider. To align this argument with Christensen’s theories of disruption, the disruptive innovation displaces the sustaining innovation, but in so doing becomes the sustaining innovation over time. Hence, Sony’s transistor radio of the mid-19050s displaced the valve radio, but now people access radio via networked devices; the mid-1950s disruptive technology became the norm, whereafter it became a sustaining technology, improving its performance along established lines. Thus, in turn, it was displaced by a new disruption, which has now shifted the terms of broadcasting resulting in innovations like BBC i-Player.

Linking Leadbeater’s report, and Christensen’s work, for the enhancement of learning and teaching in H.E., it is apparent from Cann and Badge (2011) that students, too, have trusted brands, such as YouTube and Google. Therefore, H.E.I.s would be better off working with the trusted brands as platforms for their own learning material, rather than relying solely on institutional Virtual Learning Environments. Learning is less of an institutionally contained activity than it was a generation ago, and institutions are still coming to terms with the heavily diluted boundaries of learning and teaching in the twenty first century.

References

Cann, A.J. and Badge, J.L. (2011) ‘Reflective Social Portfolios for Feedback and Peer Mentoring,’ Schoolof Biological Sciences, University of Leicester, UK, http://hdl.handle.net/2381/9704 (accessed 6 October 2011).

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth, Cambridge MA, Harvard University Press.

Christensen, C. M., Horn, M. B., and Johnson, C. W. (2011) Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns, New York, McGraw Hill.

Leadbeater, C. (2011) ‘The Civic Long Tail,’ London, Demos.

 

Cann, A.J. and Badge, J.L. (2011) ‘Reflective Social Portfolios for Feedback and Peer Mentoring’

This internal paper from the University of Leicesterwould benefit from a stronger narrative (who did what and when); it is probably clear to colleagues at the university, but it’s less easy for an outsider to know what was going on, and what was researched. That said, the article does make interesting points about how students use technologies, what attracts or repels students when it comes to learning technologies, and how technologies may be blurring the lines between different activities.

One of the interesting features of the research is that it shows students gravitating towards their trusted brands, such as Google and YouTube (p. 7). Hence, seeking to attract students to Virtual Learning Environments (VLEs) with content alone may be difficult, as students already have engrained perceptions of specific providers. It may be more beneficial to H.E.I.s to make their content available through popular channels (witness the amount of universities with their own YouTube channels).   

The research looks at an e-portfolio that students had to compile for an assessment. The exercise was not a success, given that less than 1% of the portfolios were updated after the module ended, and given the rebarbative nature of some of the student feedback (p.8).  An alternative approach at the university, more reminiscent of Facebook in its appearance, appeared to be more successful, especially as contributions did not tail off during the module, and that 15-20% of the students carried on using it after the assessment (p.21).

The researchers argue, ‘we anticipate that there will be a student-led trickle down effect arising from the introduction of these tools into teaching, eventually changing academic practices, much as the introduction of the institutional VLE changed teaching practices a few years ago’ (p. 21). However, it is debatable whether the VLE has changed teaching practices; research by Blin and Munro (2008) at one university showed that the introduction of a VLE led to existing teaching materials (Word documents and Powerpoint presentations) being relocated online, but did not lead to changes in learning and teaching practices in terms of rethinking teaching to suit the new medium.

The researchers conclude that students prefer easy to use technologies, ‘with a shallow initial learning curve’ (p. 22). The latter point ties-in with Christensen’s theory of Disruption (1997, 2003, 2011), with ease of use being a significant factor in the uptake of a technology (incidentally, a lot of the hype about Second Life, a technology with a steeper learning curve, appears to have receded). Institutional VLEs, therefore, benefit from ease of use in order to attract and maintain student interest. Links to popular technologies, such as relevant YouTube videos, may be one means of aligning VLEs with student preferences. The metaphor the researchers use for big social networks, the Swiss Army Knife (p.8) may be applied usefully to VLEs. Hence, the VLE becomes a hub, providing access to learning materials plus a range of enhancements from popular channels, available through a VLE link.   

References

Blin, F. and Munro, M. (2008) ‘Why hasn’t technology disrupted academics’ teaching practices? Understanding resistance to change through the lens of activity theory’, Computers and Education, vol. 50, pp. 475-490.

Cann, A.J. and Badge, J.L. (2011) ‘Reflective Social Portfolios for Feedback and Peer Mentoring,’ Schoolof Biological Sciences, University of Leicester, UK, http://hdl.handle.net/2381/9704 (accessed 22 September 2011).

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth,CambridgeMA, Harvard University Press.

Christensen, C. M., Horn, M. B., and Johnson, C. W. (2011) Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns,New York, McGraw Hill.

Sharples (2002), ‘Disruptive devices…’

Sharples’s (2002) main focus is on school learning, but his discussion of technologies in education has wider relevance. Having surveyed popular mobile technologies and their prevalence, he argues, ‘the response of educational institutions to such powerful technologies has, almost universally, been to treat them as a threat to be countered’ (p. 2). Part of the reason for this is structural: ‘Institutional learning depends on the classroom being a sealed environment, with all outside interventions being carefully regulated by the teacher’ (p. 3). Sharples’s analysis echoes Foucault’s (1977) in this respect, as education becomes more about control than the creative exploration of learning.

Sharples recognises that the increasing prevalence of technology in society will exert pressure on our existing pedagogical models: ‘the tensions between personal technology and institutional education will increase as students breach the sealed world of the classroom by bringing in computers that are capable of communicating with the internet’ (p. 6). Although Sharples was only writing in 2002, there is something archaic about the last part of the sentence as, in practice, the majority of students now possess phones/handheld devices with net access. In addition, Sharples’s analysis overlaps with Engestrom’s (1987, 2001) in the sense that new tools disrupt the existing activity system. The division of labour in the classroom becomes less pyramidical, with implications for the teacher and their position of authority.

Sharples realises the implications of allowing the net into the classroom: ‘we can welcome students who bring their own personal communicators and computers, but in the full knowledge that they will disrupt traditional learning and that this disruption needs to be managed’ (p. 7). Sharples’s research focuses on a prototype device used to support schoolchildren’s learning, and in this sense his approach is, oddly, more akin to the sustaining technology approach (Christensen 1997): ‘another possibility is for future mobile devices to be designed so that they provide just the tools that are required or allowed in different contexts’ (p. 14). There is still a desire to contort technology to serve the existing pedagogical model, rather than using the technology to construct a distinct pedagogy. Sharples correctly identifies the tension to existing learning and teaching caused by technology, but does not follow through fully with the implications of the tension which, in Engestrom’s (2001) analysis, creates the conditions for new knowledge to be constructed: ‘When an activity system adopts a new element from the outside …, it often leads to an aggravated secondary contradiction where some old element (for example, the rules or the division of labor [sic]) collides with the new one. Such contradictions generate disturbances and conflicts, but also innovative attempts to change the activity’ (p. 137).

References
Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Engeström, Y. (1987) Learning by expanding: an activity-theoretical approach to developmental research. Helsinki, Orienta-Konsultit Oy. http://lchc.ucsd.edu/MCA/Paper/Engestrom/expanding/toc.htm (accessed 28 April 2011).

Engestrom, Y. (2001) ‘Expansive Learning at Work: toward an activity theoretical reconceptualization’, Journal of Education and Work, vol. 14, no. 1, pp. 133-156.

Sharples, M. (2002) ‘Disruptive Devices: Mobile Technology for Conversational learning,’ Kodak/Royal Academy of Engineering Educational Technology Research Group, University of Birmingham, Birmingham.

Hamel and Prahalad (1991) ‘Corporate Imagination and Expeditionary Marketing’

Hamel and Prahalad (1991) give an example of how a disruptive technology, the video recorder, developed as a commercially successful product: ‘Sony introduced a reel-to-reel video tape player aimed at the consumer market as early as 1965. And its U-Matic VCR, launched in 1971, was also intended for the consumer market… Matsushita, JVC’s parent, likewise made several attempts to crack open a consumer market for video tape players before finally blanketing the world with its VHS standard’ (p. 87). The example suggests disruptive innovations do not simply emerge, prosper and come to dominate. Instead, they are characterised by trial and error, and by repeated iterations. If this approach comprises a pattern for success then the ability of any organisation to reinvent becomes crucial for its success. The authors illustrate the point, ‘Speed of iteration refers to the time it takes a company to develop and launch a product, accumulate insights from the marketplace, and then recalibrate and relaunch. Other things being equal, a company with a 12-month iteration cycle will be able to close in on a potential market faster than one with a 36-month cycle’ (pp. 87-88), and then supply an example in the form of Toshiba’s conduct in the laptop computer industry: ‘by 1991 Toshiba had discontinued more laptop models than some of its flat-footed competitors had launched’ (p. 88).

The success of a disruptive innovation, therefore, is determined as much by market conduct as by technological innovation. Christensen changed his own terminology from disruptive technology (1997) to disruptive innovation (2003) to foreground the idea that disruption is not a technological phenomenon, but an environmental one, in the sense that, if the innovation finds a receptive environment in which to grow and adapt, then it is more likely to succeed. Moreover, argues Christensen, the innovation is most likely to succeed when the innovation is competing against non-consumption.

In terms of how to manage an innovation, therefore, adaptation is crucial, as is a willingness to persist if initial attempts are not successful. Hamel and Prahalad argue, ‘New markets are seldom created by some mysterious process of spontaneous generation’ (p. 82). In addition, a willingness to take an imaginative approach to understanding specific markets is an advantage: ‘In one Japanese company, senior technical officers spend as many as 30 days a year outside Japan talking to customers. The goal is not to solve technical problems nor to close a sale but to observe customers and absorb their thinking… the objective was… to blur organizational and career boundaries’ (p. 86). Setting aside the unclear nature of what is meant by ‘absorb’ here, there is a sense that customers can be trusted to have good ideas and input of their own, and if this bottom-up approach is brought to product development, it is more likely to results in products that engage constructively with customers. Furthermore, the blurring of boundaries enables new insights to be formed, as people adapt the tools at their disposal, and adopt new practices.

Transferring this understanding of disruption to technology-enhanced learning in higher education, the failure of traditional Virtual Learning Environments to generate new approaches to learning and teaching (Blin and Munro, 2008) does not comprise the failure of Technology-Enhanced Learning (TEL). Instead, developers of TEL need to learn from students’ practices and be willing to try out, adapt and reinvent a range of technologies to support and enhance learning and teaching. Moreover, this process of experimentation can be informed by pedagogies suited to TEL, and, in turn, inform the development of such pedagogies. For example, in Engestrom’s expansive learning (2001), the introduction of new technology tools alters the division of labour within a learning community, as students may well have greater proficiency in technologies than their lecturers (Prensky, 2001; Scanlon and Issoff, 2005). Equally, if lecturers can work with the shift from a vertical to a horizontal relationship with their students (from exposition to support), then appropriate pedagogies can emerge to utilise fully the learning and teaching potential of new technologies, rather than cramming new technologies to existing and often uninspiring pedagogies (Christensen, 2011).

References
Blin, F. and Munro, M. (2008) ‘Why hasn’t technology disrupted academics’ teaching practices? Understanding resistance to change through the lens of activity theory’, Computers and Education, vol. 50, pp. 475-490.

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth, Cambridge MA, Harvard University Press.

Christensen, C. M., Horn, M. B., and Johnson, C. W. (2011) Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns, New York, McGraw Hill.

Engestrom, Y. (2001) ‘Expansive Learning at Work: toward an activity theoretical reconceptualization’, Journal of Education and Work, vol. 14, no. 1, pp. 133-156.

Hamel, G. and Prahalad, C. K. (1991) ‘Corporate Imagination and Expeditionary Marketing’ Harvard Business Review, July-August 1991, pp. 81-92

Prensky, M. (2001) ‘Digital natives, digital immigrants’ On the horizon, vol. 9, no. 5, pp. 1-6.

Scanlon, E. and Issoff, K. (2005) ‘Activity Theory and Higher Education: evaluating learning technologies’, Journal of Computer Assisted Learning, vol. 21, pp. 430-439.

Koszalka and Ntloedibe-Kuswani (2010), ‘Literature on the safe and disruptive learning potential of mobile technologies.’

Koszalka and Ntloedibe-Kuswani survey a range of research, defining mobile technologies (m-technologies) as networked, portable devices that can be used to support learning (which, in turn, becomes m-learning). The significant shift is away from office or classroom bound desktop computers towards small, easily-portable apparatus.

Applying Christensen’s (1997) distinction between sustaining and disruptive technologies, part of what m-technologies enable is sustaining, bringing a range of resources into the classroom, as networked computers do, and thereby offering a marginal enhancement without altering the existing pedagogy. However, part of what m-technologies enable is disruptive, taking learning out of the classroom. Learning per se has never been limited solely to the classroom, but m-technologies make the plastic nature of learning explicit.

In m-learning, informal and incidental learning is enhanced by having classroom materials on tap; the learner can learn when it is most efficacious for them rather than when it is most convenient for their H.E.I.. The authors cite research undertaken by Vavoula (2005), which found that nearly half of all learning episodes within the research sample happened outside the formal learning environment (2010, p. 146).

Christensen (2011) argues that disruption succeeds most when it is positioned against non-consumption. Hence, there is a particular role for m-learning in enabling access to higher education for communities and individuals to whom higher education has previously been unattainable. The authors argue: ‘M-learning presents itself as another form of inclusive education as it widens access to many who have traditionally been excluded from formal education, for example, those unable to attend university due to distance or some other circumstance; those with different types of learning preferences or needs; those without the means to own or access personal computers’ (2010, p. 143). Furthermore, a study by Attewell (2005) of 128 marginalised learners found that ‘m-technologies removed formal appearances of learning that distract hard-to-reach learners, helped raise learner self-confidence and self-esteem, enabled discreet learning in sensitive areas of literacy, and helped combat resistance to the use of technologies by providing a bridge between phone literacy and computer literacy’ (2010, p. 147).

A challenge for m-learning is that it requires the redesign of learning materials to suit the different environment, such as the smaller screen size. This could be viewed as a strictly technical process, but it can also be seen as a pedagogical opportunity, reconfiguring resources to utilise the potential of the learning medium. Furthermore, mobile devices are, primarily, communication devices, rather than content delivery tools (Taylor et al., 2005). Koszalka and Ntloedibe-Kuswani argue, ‘mobile devices should not be used to deliver large amounts of content but rather should be used to provide small amounts of summarized and synthesized information. Learners should be directed to computers and laptops for larger amounts of information as required’ (2010, p. 152). Thus, the growth of m-learning does not mean the death of the classroom but, rather, the clear recognition that learning is not classroom bound, and, moreover, the recognition of the value of learning taking place outside of time and space cordoned off by an H.E.I..

A further challenge for m-learning is the ownership of the learning resources. The authors cite Barlow-Zambodia’s (2009) research into 22 m-technology projects, which found that most of the information in the projects was supplied by corporate sponsors, and thus related to selling as well as learning and teaching (2010, pp.144-45). M-learning occurs within established commercial frameworks, because devices, while owned personally, are supplied, in general, by large-scale, global, commercial operators. Therefore, learning materials, and access thereto, may be influenced by commercial constraints, and thus the intersections between commerce and learning and teaching will need to be addressed, though the impact of commerce on learning and teaching is not limited to m-learning.

That said, networked, portable devices are primarily the property of the individual rather than the H.E.I., which may exert its own subtle effect, enhancing the learner’s sense of control and ownership of their learning. To make learners active owners of their learning is to prompt enhanced engagement, and thus m-learning via personally owned, networked devices may be able to capitalise on another area of non-consumption. Koszalka and Ntloedibe-Kuswani, citing McNeal and van’t Hooft (2006) suggest ‘portable phones might be the tool that increases inclusiveness and democratizes education by eliminating hierarchy and liberating learning from fixed places, times and resources’ (2010, p. 150). Formal learning has the appearance of placing time and space constraints on learning. M-learning challenges this perception.

Koszalka and Ntloedibe-Kuswani close by citing Stead’s (2006) argument, ‘just use it’ (2010, p. 153). Christensen’s disruption theory suggests that disruption emerges from usage rather than being a feature of design. The learning and teaching potential of mobile devices is likely to emerge from usage, and from users. The role for H.E.I.s is to allow m-technologies and m-learning to find an outlet in formal learning, teaching and assessment.

References
Attewell, J. (2005) Mobile technologies and learning: A technology update and m-learning project summary, London, Learning and Skills Development Agency.

Barlow-Zambodla, A. (2009) ‘Mobile technology for learner support in open schooling’ SAIDE Newsletter, vol. 15, no. 1.

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M., Horn, M. B., and Johnson, C. W. (2011) Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns, New York, McGraw Hill.

Koszalka, T. A. and Ntloedibe-Kuswani, G. S. (2010) ‘Literature on the safe and disruptive learning potential of mobile technologies’ Distance Education, vol. 31, no. 2, pp. 139-157.

McNeal, T. and van’t Hooft, M. (2006) ‘Anywhere, anytime: Using mobile phones for learning,’ Journal of the Research Center for Educational Technology, vol. 2, no. 2, pp. 24-31.

Stead, G. (2006) ‘Mobile technologies: Transforming the future of learning,’ in Pinder, A. (ed.) Emerging technologies for learning, Coventry, BECTA, pp. 6-15.

Taylor, J., Bo. G., Bernazzani, R. and Sharples, M. (2005) ‘Best practices for instructional design and content development for mobile learning’ MOBIlearn, http://www.mobilearn.org/download/results/public_deliverables/MOBIlearn_D4.2_Final.pdf (accessed 9 March 2011).

Vavoula, G. N. (2005) A study of mobile learning practices, MOBIlearn, http://www.mobilearn.org/download/results/public_deliverables/MOBIlearn_D4.4_Final.pdf (accessed 10 March 2011).

Christensen et al. (2011) ‘Disrupting Class…’

Disrupting Class takes Christensen’s Disruptive Innovation theory and applies it to the school system in the U.S.. Christensen offers a synoptic description of disruption as ‘the process by which an innovation transforms a market whose services or products are complicated and expensive into one where simplicity, convenience, accessibility, and affordability characterize the industry’ (p. 11).

Christensen originally formulated his theory as Disruptive Technology (1997). However, in his later work he argued that the disruption is not an intrinsic feature of the technology, but refers more to practice, and how an innovation is responded to, managed or ignored by an incumbent stakeholder in an existing market. Hence, ‘A disruptive innovation is not a breakthrough improvement’ (p. 47).

Christensen argues that schools’ implementation of technology-enhanced learning has followed the Sustaining Innovation path, with technology being added to the existing pedagogic models instead of prompting a fundamental rethink of learning and teaching: ‘Schools have crammed the computers into the existing teaching and classroom models. Teachers have implemented computers in the most commonsense way – to sustain their existing practices and pedagogies rather than to displace them’ (p. 84). In this sense, Christensen’s argument repeats the research findings of Blin and Munro (2008) in their study of technology-enhanced learning in higher education. Where Blin and Munro concluded, ‘although use of the VLE is widespread within the university, little disruption of teaching practices… has occurred’ (2008, p. 488), Christensen concludes, ‘… traditional instructional practices have changed little despite the introduction of computer and other modern technologies’ (2011, p. 83).

A key feature of disruptive innovations is the environmental context within which they take place: ‘Almost all disruptions take root among nonconsumers’ (p. 60). The question of environment (or, perhaps more precisely, the community confronted by innovation) is therefore central to the success of disruptive innovations: ‘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’ (p. 74). Christensen supplies an example in the form of Sony: ‘… in 1955, Sony introduced the first battery-powered, pocket transistor radio. In comparison with the big RCA tabletop radios, the Sony pocket radio was tinny and static-laced. But Sony chose to sell its transistor radio to nonconsumers – teenagers who could not afford a big tabletop radio… While it made a profit on this simple beachhead application, Sony continued to improve the technology’ (p. 80). Therefore, ‘…because Sony deployed the transistor against nonconsumption, all it had to do was make a product that was better than nothing’ (p. 81). Similarly, if technology can make learning easily available to people who don’t currently have easy access to Higher Education, then the quality and extent of the Higher Education offered will be less significant than the fact that it is being offered at all. Minority communities and developing countries are unlikely to have easy access to Higher Education, but technology can make it available to anyone with access to a networked device. Christensen concludes, ‘Success with disruptive innovations always originates against nonconsumption. Then, from that base, the technology gets better and better until, ultimately, it performs well enough that it supplants the prior approach.’ (p. 85)

Disruption works not by confronting established practice, but by doing something new: ‘A major lesson from our studies of innovation is that disruptive innovation does not take root through a direct attack on the existing system. Instead, it must go around and underneath the system’ (p. 243). Hence, applying technology-enhanced learning within established pedagogic models is a mistake, because the technology gets contorted to suit the existing pedagogy, and thus only a small portion of the learning and teaching potential of the technology is realised. To utilise the full potential of technology-enhanced learning, Higher Education Institutions need to observe what technologies can do, what students and academic community members actually do with technologies, and matching these practices to course content, assessment and delivery.

References
Blin, F. and Munro, M. (2008) ‘Why hasn’t technology disrupted academics’ teaching practices? Understanding resistance to change through the lens of activity theory,’ Computers and Education, vol. 50, pp. 475-490.

Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press.

Christensen, C. M., Horn, M. B., and Johnson, C. W. (2011) Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns, New York, McGraw Hill.

Junco et al. (2010) ‘The effect of Twitter on college student engagement and grades’

Junco et al seek to establish if the use of Twitter has a beneficial impact on student engagement and student attainment. They survey 125 students on a pre-heath professional course at one H.E.I. in the United States.

One feature of Junco et al.’s research is that the students received an hour-long induction in Twitter. By so doing the researchers may have imposed limits on students’ uses of Twitter, by setting implied parameters of usage. They may also have established, implicitly, preferred uses of Twitter. Had the researchers pursued a Disruptive Technology (Christensen 1997) or Expansive Learning (Engestrom 2001) model, they might have introduced the technology but let the students create a purpose for the technology by themselves, without any coaching.

One interesting aspect of the research is that it implies a linkage between online and face-to-face interaction. Rather than online contact removing face-to-face contact, it facilitates it. Hence, ‘Twitter was used to continue conversations begun in class’ (p. 4). The researchers also report students organising a study group, ‘with only a little encouragement from the authors via the Twitter feed’ (p. 4). In common with the initial coaching offered, the encouragement, however modest, comprises some element of instruction, and implies a preferred usage of the technology. The researchers may have missed an opportunity to give the students undirected use of the technology. However, the researches argue that students at this stage in their studies in the specific subject context rarely form study groups. They argue that Twitter enabled students to share their anxieties about assessment, realise that those anxieties were shared by other students, and thereafter form the study group (p. 10).

Twitter is also shown to have a functional value in the research, as the H.E.I. uses it as a de facto bulletin board, posting reminders of assignment and exam dates. Twitter also allows the H.E.I. to promote social functions, and academic enrichment opportunities.

The article details one sample Twitter conversation, the most notable feature of which is the depth of the exchanges. At first glance this seems unlikely, given the 140 character limit placed on individual Tweets, but users are innovative in their abbreviations, and trim the points down to the essential.

Junco et al. found that the students who had used Twitter showed greater levels of engagement, and achieved better grades, than the control group who did not use Twitter. The idea of taking a popular and free technology and integrating it into learning and teaching is a good one, but the coaching, however slight, in usage of the technology means that one of the most interesting possibilities of the research is not realised. To give students a technology with no instruction would be to create a blank canvas on which students construct meaning. The ingenuity with which students can maximise the meanings in a 140 character posting suggests there is no problem with students’ ingenuity.

References
Christensen, C. M. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston, Mass., Harvard Business School Press. Engestrom, Y. (2001) ‘Expansive Learning at Work: toward an activity theoretical reconceptualization’ Journal of Education and Work, vol. 14, no. 1, pp. 133-156.
Junco, R. Heilberger, G. and Lokent, E. (2010) ‘The effect of Twitter on college student engagement and grades’ Journal of Computer Assisted Learning no. doi: 10.1111/j.1365-2729.2010.00387.x .

The Journal of Product Innovation Management, vol. 23, no. 1, (2)

One issue of The Journal of Product Innovation Management was given over to the theme of disruption, with particular reference to the work of Clayton Christensen. The arguments explored in the Journal are applicable to other areas.

Govindarajan and Kopalle (2006) start from the position that ‘there is no appropriate measure for the disruptiveness of innovations per se’ (p. 12). They demonstrate the difficulty by citing the case of AT&T, who hired a consultancy firm in the mid-80s to assess the commercial viability of cell phones. The firm concluded that the worldwide market for cell phones would amount to around 900,000 whereas, as the authors note, ‘900,000 new subscribers typically join the world’s mobile phone services every three days’ (p. 14).

The authors draw a distinction between radical innovations, and disruptive innovations. The former ‘refers to the extent an innovation is based on a substantially new technology relative to existing practice’, whereas ‘the disruptiveness of innovations refers to the extent an emerging customer segment… sees value in the innovation at the time of introduction, which over time disrupts the products mainstream customers use.’ Hence, ‘The radicalness is a technology-based dimension of innovations, and the disruptiveness is a market-based dimension’ (pp. 13-14).

Govindarajan and Kopalle conclude by reaffirming that disruption cannot be predicted. Market size offers one potential method of assessment but, ‘Although market size is a quantifiable measure, it is still highly unreliable because market evolution for disruptive innovations is unpredictable’ (p. 16). Hence, as the case of cell phones shows, while an existing market size can be estimated, the size of the market is unlikely to remain static. Moreover, ‘developing disruptive innovations may require a high degree of risk taking and experimentation, and such experimentation always involves some failure’ (pp. 16-17). Hence, disruptive innovations are observed rather than created. The article as a whole therefore suggests that disruption is experienced, not predicted. Disruption emerges out of usage. Consequently, identifying disruption is primarily a passive activity, monitoring usage trends to identify the meanings that users create for themselves.

Markides (2006) is similarly interested in sub-dividing disruption. He is particularly interested in business model innovation: ‘It is important to note that business model innovators do not discover new products or services; they simply redefine what an existing product or service is and how it is provided to the customer. For example, Amazon did not discover bookselling; it redefined what the service is all about, what the customer gets out of it, and how the service is provided to the customer. Similarly, Swatch did not discover the watch; it redefined what this product is and why the customer should buy it’ (p. 20). Therefore, for Markides, disruption can be created by astute marketing redefining practices.

Markides’s second major category is radical innovation, ‘which creates new-to-the-world products (e.g., the car, television, personal computers, VCRs, mobile phones). Radical innovations are disruptive to consumers because they introduce products and value propositions that disturb prevailing consumer habits and behaviors in a major way… these innovations are rarely driven by demand’ (p. 22). Therefore, certain technologies jolt practices.

Both articles discuss disruptions caused by new technologies making new activities possible. Disruptions can also arise from changing practices, which can be shaped by a range of factors, including marketing. In Higher Education, new technologies have arisen, principally Virtual Learning Environments, but they have largely replicated existing practices (Blin and Munro 2008). Outside Higher Education, popular social networking technologies have made communication faster and easier, but they may also have made new activities possible, by blurring the demarcations between work, learning and socialising (Conole et al 2008).

The articles suggest that disruption in technology-enhanced learning is partly caused by the emergence of new technologies, yet is also shaped by more amorphous, less predictable factors, relating to changing patterns of practice within and between social groups. In identifying how technologies and practices are blended in the disruptive process, the emergence of the internet did prompt the creation of substantially new technologies, and thus disruption is primarily a technological phenomenon.

References
Blin, F. and Munro, M. (2008) ‘Why hasn’t technology disrupted academics’ teaching practices? Understanding resistance to change through the lens of activity theory,’ Computers and Education, vol. 50, p. 475-490.

Conole, G., Laat, Maarten de, Dillon, T. and Darby, J. (2008) ‘“Disruptive technolgies”, “pedagogical innovation”: What’s new? Findings from an in-depth study of students’ use and perception of technology’ Computers and Education, vol. 50, pp. 511-524.

Govindarajan, V. and Kopalle P. K. (2006), ‘The Usefulness of Measuring Disruptiveness of Innovations Ex Post in Making Ex Ante Predictions’ The Journal of Product Innovation Management, vol. 23, no. 1, pp. 12-18.

Markides, C. (2006) ‘Disruptive Innovation: In need of Better Theory’ The Journal of Product Innovation Management, vol. 23, no. 1, pp. 19-25.