I was asked to offer some perspective on the wider idea of edtech – what follows covers investment management, theories of learning, education reform politics, innovation theory and around 80 years of history. Some may be surprised at the scope – I would argue that it is not enough to understand how, to truly make an intelligent decision we need to at least consider why.
I should note that I was asked to give a personal and idiosyncratic view, so just to be absolutely clear these are my own opinions only.
As an investment category, defined perhaps by the breathless coverage of EdSurge and TechCrunch, EdTech is old news. The last boom years, such as they were, largely sit between 2012 and 2015, with the latter year seeing $18bn of investment attracted into the sector. Those with longer memories may recall a similar bear market at the turn of the century, aligned to the wider “dot com” boom. (and fans of TechCrunch may be interested to learn of the FinTech boom that immediately followed it)
The boundaries of the category are variously drawn, but generally encompass teaching and administrative adoption of technology and infrastructure. There is a smaller, but separate, market segment encompassing research technology with links to commercial R&D, cloud storage and big data analytics and metrics (which you could trace back, if you wanted, to ISI). Academic research infrastructure and support in itself is too small a market to consider separately for most mainstream investors – and is primarily supported by government funding.
Investors of the sort that cover EdTech are operating with a high appetite for risk, and will expect a low number of their investments to offer significant returns. This plays into the fail-fast ethos in wider Silicon Valley, but tends to favour vivid ideas rather than well-considered interventions, and incremental innovation rather than revolutionary ideas (which would have a longer-term return). Very few “EdTechs” are actually making a return on their investments, a scant few (online course provider Udacity, for instance) are even turning a working profit. The model for funders is to grow mindshare and a user base, before being acquired by a larger tech company (Google, Microsoft, Blackboard…) – again, as in wider Silicon Valley.
As a historic project, your modern edtech (in the sense of mechanical or digital aids to the process of education) sits very much on a line drawing from a behaviourist (Skinnerian) model of learning. Drawing on ideas of repetition and reward, it underpins drill-and-kill learning tools such as Duolingo, and many test preparation or content delivery packages.
A later strand drawing on constructivist and social constructivist theories of learning (Durkheim, Illich, Papert through perhaps to someone like George Siemens) emphasised the agency of the learner to make sense of the world around them, drawing on networks of peers. The rise of social media around 2008 spurred the development of “connectivism”, a postulated theory concerning the way networks comprising human and non-human members interact, grow and learn (rhizomatically).
Cognitive learning theories (Piaget, also Badderly, Chomsky) are the basis of the “personalisation” agenda wherein technology can “adapt” within bounded states to suit individual learner needs – much of what is described as “AI” in learning, and indeed many of the models of learning that define AI research – are cognitivist.
And outside of learning theories all together, you have the same drives around efficient management of information that define the wider tech-boom. Administrative technology also has the advantage that the burden of proof is seldom asked for – access to information is an axiomic good.
You could connect these trends together to explain something like the MOOC, which started with an explicitly connectivist underpinning but pivoted quickly (with the pressure of growth and massification) to a behaviourist model, though with a cognitive science gloss via the collection and use of administrative user data.
But why would you? Simply put, these ideas underpin the majority of edtech development. Despite the neo-mania of EdTech as narrative (as Audrey Watters notes “the best way to predict the future is to write a press release”, and I would agree), it is a surprisingly conservative field in terms of approach, although an army of silicon valley patent lawyers would love to convince you otherwise.
Part of the leverage that the field has on education policy makers comes from the wider narrative of Education Reform. Joining parents and educators with genuine concerns about the quality of education with investors and politicians looking to improve the profitability of education, this narrative – which I love to characterise as “Education is broken” – underpins many of the machinery of education (Charter school, free school, challenger institutions…) changes that open up education to “disruptive innovation”.
Harvard Business Administration researcher Clayton Christensen first postulated that idea of disruption, and he applied it to education in his 2008 book ‘Disrupting Class‘. Simply put, the concept of low-end disruptive innovation suggests that any established market can be destabilised by the entry of a new actor offering a similar but inferior product at a vastly lower price. This new actor initially serves a niche interest and does not provide the features of premium products in the marketplace but through repeated innovation it expands and improves to serve wider needs and increases profitability.
However, this theory has been debunked specifically within education (by none less than Christiansen himself in 2013), and more generally as a fundamental narrative of innovation (Jill Lapore in 2014 is flat-out superb). As attractive as the idea of low cost innovation may be to investors, it has not and does not explain innovation as it actually happens.
Entrepreneurial state theory – as described by Mariana Mazzucato in her book of the same name, sees a role for the long term, stable nature of state funding in supporting and developing innovation. An example would be the support in defence spending for early cybernetics projects that became VR, networked communication, responsive software (and also pigeon-guided bombs – courtesy of one BF Skinner… but not every experiment is a success…) and underpin much of what became Edtech.
There are people better qualified than me to talk about theories of innovation, but I will content myself to mentioning Von Hippell’s lead user theory – broadly watching the working practices of expert practitioners, identifying where existing processes or technologies are shortcutted, then working with practitioners to design tools to simplify these short-cuts.
So what is “an EdTech”? Despite overweening claims around innovation, the easiest way is to characterise their intended mechanism. An EdTech uses one or more of the three educational theories above (either knowingly or, more commonly, implicitly) to either sell into existing education providers, or to attempt to disrupt these providers by establishing alternate providers and selling to learners. As hype around the central category has grown, more generally applicable administrative interventions have been branded as edtech.
Actual sales (in terms of money being exchanged for goods or services) are rare, as the focus is on growing a user-base and associated hype in order to be acquired by a larger enterprise. (This is just mainstream Silicon Valley business practice).
But do “EdTechs” improve education? It is difficult to say. Certainly to read the press releases that have flooded the inboxes of education or technology journalists – very few cover both, so it has been possible to exploit gaps in knowledge (see Audrey Watters “What every techie should know about education“) – would indicate that we now live in a golden age of cheap, ubiquitous, personalised and effective learning. And yet.
Certainly the things that do improve education as a wider are often far removed from the mythologised moment of learning – administrative system interoperability, open licensing for academic content – solving, in other words, known problems as reported by expert practitioners.