CONCLUSIONS


Reclaiming Ed Tech

Having surveyed one particular take on 25 years of ed tech, it is now possible to synthesize some generalities. In this chapter, several themes arising from the analysis of this history will be proposed, and then some suggestions regarding what this means for the next 25 years of ed tech will be proffered.

The first of the general themes is that in ed tech, the tech part of the phrase “walks taller.” Throughout this book, most of the innovations that appear are technologies. Sometimes these are underpinned with strong accompanying educational frameworks, such as the original cMOOC, but also there are cases of a technology seeking an application, as seen with blockchain. The prominence of technology is undoubtedly a function of the time span of the book, which covers the early phases of the digital revolution. A set of ed tech developments 25 years from now may be better balanced with conceptual frameworks, pedagogies, and social movements. The initial few chapters were, in effect, putting into place the technical infrastructure that would facilitate the ed tech developments to come. The web, CMC, e-learning, wikis, blogs, and so on, can be seen as fundamental tools that allowed the social and educational aspects, both positive and negative, to develop. Thus, the initial focus in this book is on the enabling technologies, but as the chapters progress, the focus is increasingly on the impact of these.

A corollary of this is that some of the innocence and optimism invested in new technologies is no longer a valid stance, as chapter 25 highlighted. In each of the technologies, from the web to analytics, there are negative social consequences. There is sufficient experience of these now to predict at least some of these undesirable outcomes. Their possibility, even inevitability, is not necessarily a reason to refuse to engage with any technology. For example, blogs provide as many, if not more, positive examples as they do negative ones. It is in the nature of an open, unfiltered system that people will publish content many find disagreeable (although not outside of existing laws on defamation, threats, and the incitement of hatred). But these negative consequences are not unexpected or unknowable.

Algorithms shape behaviour, and in the seeds of each technology lies the possibility for future dystopian outcomes. For example, Harwell (2018) discussed the spread of “deepfake” videos, where a video can be created by gathering some facial images (such as those posted on social media) and pasting them onto an existing video. This has led to the weaponization of this technology by misogynists to create realistic fake pornography videos of women they seek to undermine, harass, or humiliate. This use of the technology is entirely (and sadly) predictable, and the claim that technology is neutral is not really sustainable. Harwell quoted Hany Farid, a Dartmouth College computer-science professor who specializes in examining manipulated photos and videos who put it in terms of an analogy:

If a biologist said, ‘Here’s a really cool virus; let’s see what happens when the public gets their hands on it,’ that would not be acceptable. And yet it’s what Silicon Valley does all the time,” he said. “It’s indicative of a very immature industry. We have to understand the harm and slow down on how we deploy technology like this. (“Identity Theft,” para. 5)

Therefore, although technology has been the dominant force in ed tech, its prevalence in society now means that the educational component needs to come to the fore.

A second theme is that we see ideas recurring, sometimes with increasing success in their adoption. For example, learning objects were the first attempt at making teaching content reusable, and even though they weren’t successful, the ideas they generated led to OER, which begat open textbooks. Partly this is a result of historical amnesia, which I cited as one of the motivations for writing this book. If there is no shared history, then there is a tendency, seen repeatedly over these 25 years, for ideas to be rediscovered. A consequence of this is that it sees every development as operating in isolation instead of building on the theoretical, financial, and administrative research of previous work. In examining the different subcommunities that have evolved under the broad heading of “open education” Weller, Jordan, DeVries, and Rolfe (2018), using a citation analysis method, discovered eight distinct communities. The published papers in these areas rarely cross over and reference work from other communities, which is symptomatic of the year-zero mentality. This is also reinforced by the commercial pressures of ed tech start-ups to position themselves as revolutionary and ground-breaking, and particularly “disruptive” as this promises a sector-wide monopoly.

The recurrence of ideas is also a result of what we might term “techno-optimism” — the belief that “this time it really will work.” This can be a consequence of overenthusiastic initial claims, which the technology then takes 10 years or so to realize. For example, while intelligent tutoring systems were woefully inadequate for the claims made for them in the 1980s and 1990s, some of that is justifiable in 2018 (although, equally, some of the claims are still overblown). It is also the case that, conceptually, an idea needs several iterations before it is widely accepted. This is influenced by changes in social attitudes towards the use of technology. When mobile learning required specialist devices or relied on text-based quizzes, its uptake was limited, but the arrival and widespread usage of smartphones and apps fundamentally altered the relation of people to learning in different contexts. The shift has become one of push to pull, from providers trying to encourage learners to use mobile learning approaches to learners expecting it.

A consequence of this iterative approach is that those who have been in the ed tech field for a while should be wary of dismissing an idea by saying “We tried that — it didn’t work.” Virtual reality and immersive worlds may be a good example of this, the first attempts typified by Second Life failed to realize the claims made for it, but there have been sufficient changes since then to make versions of this a viable ed tech, particularly in specific domains. Technology and attitudes can change quietly, and what seemed difficult five years ago is now feasible. Conversely, for those proposing a new idea, there is a need to understand why previous attempts failed and to learn from that experience. This is not to suggest that all ideas will inevitably succeed; some of the claims made for AI, for example, are as far-fetched (and as undesirable) now as they were in the 1980s.

A third emergent theme is how technology outside of education has consistently been co-opted for educational purposes. This has met with varying degrees of success. Blogs, for instance, are an ideal educational technology, whereas blockchain has something of the air of a technology in search of an educational application. The popularity of — or the number of Wired headlines about — a technology does not automatically make it a contender as a useful technology for education. More subtly, this adoption means that technology which has not been designed specifically for education is deployed in a context where some requirements may be different. For instance, the adoption of Facebook to create course-specific groups that are a formal component of study — i.e., students cannot complete their studies, or are severely disadvantaged, if they do not use it — provides both benefits and challenges for an educator. It immediately provides a well-structured platform with many desirable tools and features and is one that is familiar to many students.

This can effectively encourage dialogue since the initial barrier to technology adoption is lessened — students don’t need to learn or remember to go to a new platform. This means the type of conversations an educator may wish to encourage can be boot-strapped and may start earlier. However, as we have covered elsewhere, the use of commercial social media platforms, such as Facebook, carries several issues, including privacy, data surveillance, and the forcing of students onto a platform they may have consciously chosen to avoid. The convenience of the third-party choice is heavily compromised by it not being a technology designed specifically for educational purposes.

In a global survey of universities, Orr, Weller, and Farrow (2018) reported that the technologies that are most widely adopted and deeply embedded in higher education institutions tend to correlate closely with core university functions, which are broadly categorized as content, delivery, and recognition (Agarwal, 2016). For example, OER, LMS, and e-portfolios from the selection in this book are all widely deployed, and these types of technology relate very closely to these core functions. They are also technologies designed specifically for education, even if their roots can be found in other technologies.

This preference for technologies that are education-specific emphasizes that higher education is a complex, highly interdependent system. It is not like the banking, music, or media industries; rather, while it has some similarities with those sectors, it has many more differences. The simple transfer of technology from other sectors often fails to appreciate the socio-cultural context in which education operates. Generally, only those technologies that directly offer an improved, or alternative, means of addressing the core functions of education achieve widespread adoption.

The cautious adoption of technology can be seen as a further theme. Contrary to some of the rhetoric about higher education’s inability to change, the coverage in this book highlights that innovation does indeed arise frequently and across a wide range of educational contexts. Taken as a whole, this review of the last 25 years in ed tech reveals a rich history of innovation: MOOC, Web 2.0, BBS, PLE, connectivism — these all saw periods of exciting innovation and, even if they were not always successful, they posed fundamental questions regarding what education is for and how best to realize it. Accusations that education is fundamentally unchanged from 100 years ago (e.g., Parr, 2012) are mistaken and demonstrate a lack of knowledge about the sector.

However, it is also true that change is not always rapid. One of the complaints, particularly from outsiders, is that higher education is resistant, and slow, to change. This is true, but this can also be framed as a strength. Universities have been around longer than Google, after all, and part of their appeal is their longevity. This entails a certain conservatism regarding current trends, so as institutions they resist abandoning all existing practice in favour of the latest technology. Libraries weren’t closed and replaced with LaserDiscs in the 1990s, partly because the timeframes that universities operate over are longer (and because it would have been a bad idea). This is one of the major, and often misunderstood, differences between higher education and the other sectors that it is frequently implored to learn from — they are operating over different frequencies.

The language of start-ups and technology companies pervades much of the ed tech world, but these phrases are used in very different contexts. Unless a university principal is being required to save a university from imminent collapse, the kind of high-pressure, rapid institutional transformation often seen in tech companies is disruptive (in its original sense) and harmful to the functioning of a university. Universities operate over long timeframes and have often existed for over 50 or 100 years. Their very function is based on their longevity and adherence to core principles rather than rapid changes and then obsolescence. This is perhaps analogous to different sound frequencies. Universities operate like a low-frequency sound, such as a bass drum, whereas technology companies are a high-frequency sound, like a whistle. Over the same time period, there will be waves in both, but far more peaks and troughs will occur in the high-frequency one. Ed tech, then, is operating in a fundamentally different context to other tech companies, and this is perfectly valid and appropriate. Ed tech is not a game for the impatient.

An underlying factor for some of this dissonance is the dominance of the disruption theory we encountered in various places throughout this history (Christensen, 1997). The original application of the term was a useful means of framing how digital technology could create new markets and overtake existing ones, the way digital photography, say, disrupted the traditional camera market. However, it has acquired the status of myth in the technology industry (Watters, 2013a), to the extent that it is both a specified aim compared with an unintended outcome, and indeed is the only desirable outcome for many investments. It is a term frequently associated with ed tech or with innovators to emphasize their independence from the conventional modes of working. For instance, Richard Branson organized an event labelled “Disruptors 2015 — The Future of Education: Does the Current Model Make the Grade?” (Virgin.com, 2015), which featured many ed tech start-ups but few academics or universities.

What the above consideration of different frequencies illustrates is that, given its dominance in much of ed tech discourse, disruption is simply not a very useful theory to apply to the education sector. One of the defining characteristics of higher education is its longevity, while disruption theory relies on the destruction of a sector. Even if we accept that disruption does occur elsewhere, although this is refuted by many (Dvorak, 2004), it is an inappropriate model or explanation to apply to higher education, like using a description of changes at the cellular level to explain a psychological phenomenon — it might be inveigled in to service, but it is not an effective means of predicting, describing, modelling, or adjusting. There are lots of other reasons to be skeptical of those who promote the idea of disruption, but in higher education, at least, it is simply not a very productive tool to work with.

Phipps (2018) reveals how ed tech vendors seek to ensure that academics are absent when they are pitching technology solutions to universities. He states that vendors “don’t want any academics that might end up as users in that room asking difficult questions” (para. 12). Partly this is because vendors will often use powerful but largely meaningless and discredited theories, such as disruption, digital natives, and learning styles. These theories can be effective in creating a narrative of a need for urgent change, underwritten by the Darwinian survival ethos we encountered at the start of this book. However, as I hope this book indicates, an analysis of these motivating factors usually undermines their authority. There is a distinct need for educational technologists to be “in the room” for such pitches, then, and to have an appreciation of both the possible benefits of any technology and the limitations of the associated promises and threats.

The absence of the human impact in much of the discourse around disruption leads to the final theme arising from analysis of the past 25 years, which can be thought of as the role of people in ed tech. Much of the technology covered in these chapters can be seen as representing two distinct ideologies: those that help the educator or those that replace them. Technologies such as wikis, OER, CMC, blogs, and even Second Life have, as their primary aim to find technology that can enhance education, either for a new set of learners, to realize new approaches, or sometimes, just to experiment. Other approaches are oftentimes framed in terms of removing human educators in a bid for improved efficiency and scale: AI, learning analytics, or MOOC. This is not ubiquitous across their associated literature; for example, learning analytics can be used to help human educators better support learners. But often the hype and associated interest is around the large-scale implementation of automated learning. Higher education is most successful when it is framed as a human enterprise, and the technology that is likely to be both impactful and culturally beneficial is that which recognizes this and seeks to work collaboratively with human educators.

This human aspect is also a key component for consideration of the technologies or approaches that are successful. As seen with developments such as e-learning standards and learning objects, a prohibitive factor for adoption is the return on effort. If an educational technology requires excessive effort for low perceived reward, then it will usually fail, or at least require another iteration to be successful. This is the case even if the long-term goal would be beneficial; educators operate in a time-constrained present and need an identifiable benefit. This return on investment paradox is one area where funding from national agencies can be useful in overcoming the initial impetus required to reach a level where the benefits can be identified. Similarly, ed tech exists as part of a socio-cultural system that is decidedly human. For instance, many of the requirements for the successful implementation of e-portfolios and digital badges are not related to the technology, but rather to how people will recognize, use, and, ultimately, require them.

When we look back over the last 25 years, the picture that emerges is a mixed one. Clearly, a considerable shift in higher education practice has taken place, driven by technology adoption. Yet, at the same time, nothing much has changed, and many ed tech developments have failed to have a significant impact. “Everything changes while simultaneously remaining the same,” is perhaps the rather paradoxical conclusion. Accepting this as the framework within which ed tech operates, rather than either extreme, however, is a good piece of advice for anyone entering into this field. And the best way to negotiate this paradox is by understanding the recent history that makes it the case.

This paradox of change and seeming unchanging nature has an analogy with books and reading. If you were to look at reading 25 years ago and today, then superficially, nothing much has changed — the classic image is of someone reading a hardback book in quiet solitude. And yet, it doesn’t take much examination to appreciate just how wholly different the context is within which that reading occurs. In terms of technology, there is an abundance of audiobooks and e-books; retail occurs largely through online providers, such as Amazon; publishing has seen a rapid growth in self-publishing and crowdfunding models; and the writing of books sees extensive use of blogs, fan fiction, online research and dissemination that occurs through social media and accompanying material found online. The business of books and the society within which books exist is almost unrecognizable from 25 years ago.

So how to reconcile these two elements of seeming resistance to change and yet large-scale innovation? I would suggest that both books and education have what might be termed a “core of immutability” — that is, there is some aspect at their core that does not alter. Indeed, this essence is part of the reason we hold them in high social value: they echo back through history and evoke generally positive emotions. This core is, for both of them, around the individual focus on a task that is conducted largely in the mind — the indulgence in what is essentially a cognitive art form. They are both fundamentally human: maybe AI can write passable books in the future, and maybe it can provide a reasonable level of learner support, but AI is a long way from capturing that human element of flexibility and creativity that are deeply embedded in books and education and that are a part of their appeal.

Inevitably, any analysis of recent history leads to some conjecture about the future. I will resist a “25 Years in the future of ed tech” conclusion, however, because predicting the future of education is a game to which we never seem to learn the rules. Extrapolating from the themes above, some of the following rules about considering the future can nonetheless be deduced and associated with some general predictions.

The first rule to learn about change in higher education is that, as we have just seen, very little changes while simultaneously everything changes. Therefore, any prediction that highlights just one of these elements underestimates either the core of immutability of the general higher education system or the degree of innovation that occurs within it. So, a prediction would be that the future of education will look not dissimilar on the surface, but closer inspection will reveal significant changes around the use of technology to support learning.

A second rule is that technological change is rarely about the technologies, as discussed above with innovations such as e-portfolios or digital badges. The technologies may be fairly robust and straightforward, but what they require in order to have an impact is a shift in cultural attitudes from employers and learners regarding recognition, the format of learning, and alternative accreditation. A second prediction, then, will be that many existing technologies will still be around, but some of them will have developed the appropriate social structures for broad adoption, whereas others will have withered in face of this task.

The third rule is to recognize the historical amnesia in much of educational technology that this book has highlighted, which arises from people entering the field from elsewhere and from ed tech vendors deliberately seeking to position a technology as new and revolutionary. A related prediction, then, will be that exactly the same technologies we see now will be present in the future, but under different names and with some variations.

The fourth and final rule I would suggest is that, as chapter 25 argued, technology is not ethically or politically neutral. This becomes increasingly significant as technology continues to affect all aspects of society. The prediction here, then, is that awareness of this will continue to grow, with educators and learners viewing technology use in education as much as a political choice as it is an educational one. The development of new technologies will be couched not just in the language of technology but also in terms of political and sociological impact. Ed tech practitioners who ignore these factors may well find themselves trying to explain the negative interpretations of their approach before it has started.

Some aspects in the future use of ed tech will become commonplace as a trajectory of what we have now. For instance, the use of online education will expand as people are increasingly comfortable and adept at operating online. The distinction between face-to-face and online will continue to diminish, such that all university study will be, to an extent, blended. The use of narrow AI focused on particular tasks will increase, but so too will the skepticism around what this means. Similarly, data-driven approaches such as learning analytics will become an increasingly contested ground, between what is possible, what is ethical, what is desirable from a learner perspective, and what is useful for an educator.

In short, the future of ed tech will resemble the present situation pretty closely but with the role of technology becoming ever more pervasive in the educational process. If it is not already true, then in 25 years it certainly will be, that all learning is technology-enhanced learning. This establishes an onus on educators, universities, and learners themselves to critically reflect on the role of that technology. The future of ed tech, then, is likely to be one where the relationship between people and increasingly powerful technology is one that is constantly examined and negotiated. We will probably not see any grand revolution in the higher education space, so don’t expect the type of future often predicted by educational technology entrepreneurs, wherein all existing universities are made redundant by a new technology-centric model. Instead, we will see a continual model of innovation, testing, adaption, and revisiting within the constraints of an existing and robust system. And hopefully, that model will be one that acknowledges, learns from, and remembers its history.