Archive for the ‘ed tech’ Category

Online teaching is big business. Very big business. Online language teaching is a significant part of it, expected to be worth over $5 billion by 2025. Within this market, the biggest demand is for English and the lion’s share of the demand comes from individual learners. And a sizable number of them are Chinese kids.

There are a number of service providers, and the competition between them is hot. To give you an idea of the scale of this business, here are a few details taken from a report in USA Today. VIPKid, is valued at over $3 billion, attracts celebrity investors, and has around 70,000 tutors who live in the US and Canada. 51Talk has 14,800 English teachers from a variety of English-speaking countries. BlingABC gets over 1,000 American applicants a month for its online tutoring jobs. There are many, many others.

Demand for English teachers in China is huge. The Pie News, citing a Chinese state media announcement, reported in September of last year that there were approximately 400,000 foreign citizens working in China as English language teachers, two-thirds of whom were working illegally. Recruitment problems, exacerbated by quotas and more stringent official requirements for qualifications, along with a very restricted desired teacher profile (white, native-speakers from a few countries like the US and the UK), have led more providers to look towards online solutions. Eric Yang, founder of the Shanghai-based iTutorGroup, which operates under a number of different brands and claims to be the ‘largest English-language learning institution in the world’, said that he had been expecting online tutoring to surpass F2F classes within a few years. With coronavirus, he now thinks it will come ‘much earlier’.

Typically, the work does not require much, if anything, in the way of training (besides familiarity with the platform), although a 40-hour TEFL course is usually preferred. Teachers deliver pre-packaged lessons. According to the USA Today report, Chinese students pay between $49 and $80 dollars an hour for the classes.

It’s a highly profitable business and the biggest cost to the platform providers is the rates they pay the tutors. If you google “Teaching TEFL jobs online”, you’ll quickly find claims that teachers can earn $40 / hour and up. Such claims are invariably found on the sites of recruitment agencies, who are competing for attention. However, although it’s possible that a small number of people might make this kind of money, the reality is that most will get nowhere near it. Scroll down the pages a little and you’ll discover that a more generally quoted and accepted figure is between $14 and $20 / hour. These tutors are, of course, freelancers, so the wages are before tax, and there is no health coverage or pension plan.

Reed job advertVIPKid, for example, considered to be one of the better companies, offers payment in the $14 – $22 / hour range. Others offer considerably less, especially if you are not a white, graduate US citizen. Current rates advertised on OETJobs include work for Ziktalk ($10 – 15 / hour), NiceTalk ($10 – 11 / hour), 247MyTutor ($5 – 8 / hour) and Weblio ($5 – 6 / hour). The number of hours that you get is rarely fixed and tutors need to build up a client base by getting good reviews. They will often need to upload short introductory videos, selling their skills. They are in direct competition with other tutors.

They also need to make themselves available when demand for their services is highest. Peak hours for VIPKid, for example, are between 2 and 8 in the morning, depending on where you live in the US. Weekends, too, are popular. With VIPKid, classes are scheduled in advance, but this is not always the case with other companies, where you log on to show that you are available and hope someone wants you. This is the case with, for example, Cambly (which pays $10.20 / hour … or rather $0.17 / minute) and NiceTalk. According to one review, Cambly has a ‘priority hours system [which] allows teachers who book their teaching slots in advance to feature higher on the teacher list than those who have just logged in, meaning that they will receive more calls’. Teachers have to commit to a set schedule and any changes are heavily penalised. The review states that ‘new tutors on the platform should expect to receive calls for about 50% of the time they’re logged on’.

 

Taking the gig economy to its logical conclusion, there are other companies where tutors can fix their own rates. SkimaTalk, for example, offers a deal where tutors first teach three unpaid lessons (‘to understand how the system works and build up their initial reputation on the platform’), then the system sets $16 / hour as a default rate, but tutors can change this to anything they wish. With another, Palfish, where tutors set their own rate, the typical rate is $10 – 18 / hour, and the company takes a 20% commission. With Preply, here is the deal on offer:

Your earnings depend on the hourly rate you set in your profile and how often you can provide lessons. Preply takes a 100% commission fee of your first lesson payment with every new student. For all subsequent lessons, the commission varies from 33 to 18% and depends on the number of completed lesson hours with students. The more tutoring you do through Preply, the less commission you pay.

Not one to miss a trick, Ziktalk (‘currently focusing on language learning and building global audience’) encourages teachers ‘to upload educational videos in order to attract more students’. Or, to put it another way, teachers provide free content in order to have more chance of earning $10 – 15 / hour. Ah, the joys of digital labour!

And, then, coronavirus came along. With schools shutting down, first in China and then elsewhere, tens of millions of students are migrating online. In Hong Kong, for example, the South China Morning Post reports that schools will remain closed until April 20, at the earliest, but university entrance exams will be going ahead as planned in late March. CNBC reported yesterday that classes are being cancelled across the US, and the same is happening, or is likely to happen, in many other countries.

Shares in the big online providers soared in February, with Forbes reporting that $3.2 billion had been added to the share value of China’s e-Learning leaders. Stock in New Oriental (owners of BlingABC, mentioned above) ‘rose 7.3% last month, adding $190 million to the wealth of its founder Yu Minhong [whose] current net worth is estimated at $3.4 billion’.

DingTalk, a communication and management app owned by Alibaba (and the most downloaded free app in China’s iOS App Store), has been adapted to offer online services for schools, reports Xinhua, the official state-run Chinese news agency. The scale of operations is enormous: more than 10,000 new cloud servers were deployed within just two hours.

Current impacts are likely to be dwarfed by what happens in the future. According to Terry Weng, a Shenzhen-based analyst, ‘The gradual exit of smaller education firms means there are more opportunities for TAL and New Oriental. […] Investors are more keen for their future performance.’ Zhu Hong, CTO of DingTalk, observes ‘the epidemic is like a catalyst for many enterprises and schools to adopt digital technology platforms and products’.

For edtech investors, things look rosy. Smaller, F2F providers are in danger of going under. In an attempt to mop up this market and gain overall market share, many elearning providers are offering weighty discounts and free services. Profits can come later.

For the hundreds of thousands of illegal or semi-legal English language teachers in China, things look doubly bleak. Their situation is likely to become even more precarious, with the online gig economy their obvious fall-back path. But English language teachers everywhere are likely to be affected one way or another, as will the whole world of TEFL.

Now seems like a pretty good time to find out more about precarity (see the Teachers as Workers website) and native-speakerism (see TEFL Equity Advocates).

Google search resultsUnconditional calls for language teachers to incorporate digital technology into their teaching are common. The reasons that are given are many and typically include the fact that (1) our students are ‘digital natives’ and expect technology to be integrated into their learning, (2) and digital technology is ubiquitous and has so many affordances for learning. Writing on the topic is almost invariably enthusiastic and the general conclusion is that the integration of technology is necessary and essential. Here’s a fairly typical example: digital technology is ‘an essential multisensory extension to the textbook’ (Torben Schmidt and Thomas Strasser in Surkamp & Viebrock, 2018: 221).

 

Teachers who are reluctant or fail to embrace technology are often ‘characterised as technophobic, or too traditional in their teaching style, or reluctant to adopt change’ (Watson, 2001: 253). (It’s those pesky teachers again.)

Claims for the importance of digital technology are often backed up by vague references to research. Michael Carrier, for example, in his introductory chapter to ‘Digital Language Learning and Teaching’ (Carrier et al. 2017: 3) writes that ‘research results […] seem to show conclusively that the use of educational technology adds certain degrees of richness to the learning and teaching process […] at the very least, digital learning seems to provide enhanced motivation for learners’.

Unfortunately, this is simply not true. Neither in language learning / teaching, nor in education more generally, is there any clear evidence of the necessary benefits of introducing educational technology. In the broader context, the ‘PISA analysis of the impact of Information Communication Technology (ICT) on reading, mathematics, and science (OECD, 2015: 3) in countries heavily invested in educational technology showed mixed effects and “no appreciable improvements”’ (Herodotou et al., 2019). Educational technology can or might  ‘add certain degrees of richness’ or ‘provide enhanced motivation’, but that is not the same as saying that it does or will. The shift from can to will, a piece of modal legerdemain used to advocate for educational technology, is neatly illustrated in a quote from the MIT’s Office of Digital Learning, whose remit is to improve learning and teaching across the university via digital learning: ‘Digital Learning technologies can enable students to grasp concepts more quickly [etc….] Digital technologies will enable this in new and better ways and create possibilities beyond the limits of our current imagination’ (quoted by Carrier, 2017: 1).

Before moving on, here’s another example. The introduction to Li Li’s ‘New Technologies and Language Learning’ (Li, 2017: x) states, with a cautious can, that one of the objectives of the book is ‘to provide examples of how technologies can be used in assisting language education’. In the next paragraph, however, caution is thrown to the wind and we are told, unequivocally, that ‘technology is beneficial for language learning’.

Pedagogy before technology

Examples of gratuitous technology use are not hard to find. Mark Warschauer (who, as the founding director of the Digital Learning Lab at the University of California, Irvine, could be fairly described as an edtech enthusiast) describes one example: ‘I remember observing a beginners’ French class a number of years ago, the teacher bragged about how engaged the learners were in creating multimedia in French. However, the students were spending most of their time and energy talking with each other in English about how to make PowerPoints, when, as beginning learners, they really needed to be spending time hearing as much French as possible’ (quoted in the Guardian, May 2014).

As a result, no doubt, of having similar experiences, it seems that many people are becoming a little more circumspect in their enthusiasm for edtech. In the same Guardian article as Warschauer’s recollections, Russell Stannard ‘says the trick is to put the pedagogy first, not the technology. “You’ve got to know why you’re using it. Teachers do need to learn to use new technology, but the driving force should always be the pedagogy behind it’. Nicky Hockly, Gavin Dudeney and Mark Pegrum (Hockly et al., 2013: 45) concur: ‘Content and pedagogy come before technology. We must decide on our content and pedagogical aims before determining whether our students should use pens or keyboards, write essays or blogs, or design posters or videos’. And Graham Stanley (2013: 1) in the introduction to his ‘Language Learning With Technology’ states that his ‘book makes a point of putting pedagogy at the forefront of the lesson, which is why content has been organised around specific learning content goals rather than specific technologies’.

But, Axel Krommer, of the Friedrich-Alexander University of Erlangen-Nürnberg, has argued that the principle of ‘pedagogy before technology’ is ‘trivial at best’. In a piece for the Goethe Institute he writes ‘a theory with which everyone agrees and whose opposite no-one believes true is meaningless’, although he adds that it may be useful as ‘an admonitory wake-up call when educational institutions risk being blinded by technological possibilities that cause them to neglect pedagogical principles that should really be taken for granted’. It was this piece that set me thinking more about ‘pedagogy before technology’.

Pedagogy before technology (on condition that there is technology)

Another person to lament the placing of technology before pedagogy is Nik Peachey. In an opinion piece for the Guardian, entitled ‘Technology can sometimes be wasted on English language teaching’, he complains about how teachers are left to sort out how to use technology ‘in a pedagogically effective way, often with very little training or support’. He appears to take it as given that technology is a positive force, and argues that it shouldn’t be wasted. The issue, he says, is that better teacher training is needed so that teachers’ ‘digital literacies’ are improved and to ensure that technological potential is fulfilled.

His position, therefore, cannot really be said to be one of ‘pedagogy before technology’. Like the other writers mentioned above, he comes to the pedagogy through and after an interest in the technology. The educational use of digital technology per se is never seriously questioned. The same holds true for almost the entirety of the world of CALL research.

confer

A Canadian conference ‘Pedagogy b4 Technology’ illustrates my point beautifully.

There are occasional exceptions. A recent example which I found interesting was an article by Herodotou et al (2019), in which the authors take as their starting point a set of OECD educational goals (quality of life, including health, civic engagement, social connections, education, security, life satisfaction and the environment), and then investigate the extent to which a variety of learning approaches (formative analytics, teachback, place-based learning, learning with robots, learning with drones, citizen inquiry) – not all of which involve technology – might contribute to the realisation of these goals.

Technology before pedagogy as policy

Some of the high school English teachers I work with have to use tablets in one lesson a week. Some welcome it, some accept it (they can catch up with other duties while the kids are busy with exercises on the tablet), others just roll their eyes at the mention of this policy. In the same school system, English language learning materials can only be bought if they come in digital versions (even if it is the paper versions that are actually used). The digital versions are mostly used for projecting pages onto the IWBs. Meanwhile, budgets and the time available for in-service training have been cut.

Elsewhere, a chain of universities decides that a certain proportion of all courses must be taught online. English language courses, being less prestigious than major subjects, are one of the first to be migrated to platforms. The staff, few of whom have tenure or time to spare, cope as best as they can, with some support from a department head. Training is provided in the mechanics of operating the platform, and, hopefully before too long, more training will become available to optimize the use of the platform for pedagogical purposes. An adequate budget has yet to be agreed.

The reasons why so many educational authorities introduce such policies are, at best, only superficially related to pedagogy. There is a belief, widely held, that technology cannot fail to make things better. In the words of Tony Blair: ‘Technology has revolutionised the way we work and is now set to transform education. Children cannot be effective in tomorrow’s world if they are trained in yesterday’s skills’. But there is also the potential of education technology to scale education up (i.e. increase student numbers), to reduce long-term costs, to facilitate accountability, to increase productivity, to restrict the power of teachers (and their unions), and so on.

In such circumstances, which are not uncommon, it seems to me that there are more pressing things to worry about than teachers who are not sufficiently thinking about the pedagogical uses to which they put the technology that they have to use. Working conditions, pay and hours, are all affected by the digitalisation of education. These things do get talked about (see, for example, Walsh, 2019), but only rarely.

Technology as pedagogy

Blended learning, described by Pete Sharma in 2010 as a ‘buzz word’ in ELT, remains a popular pedagogical approach. In a recent article (2019), he enthuses about the possibilities of blended learning, suggesting that teachers should use it all the time: ‘teaching in this new digital age should use the technologies which students meet in their everyday lives, such as the Internet, laptop, smartphone and tablet’. It’s also, he claims, time-efficient, but other pedagogical justifications are scant: ‘some language areas are really suited to be studied outside the classroom. Extensive reading and practising difficult phonemes, for instance’.

Blended learning and digital technology are inseparable. Hockley (2018) explains the spread of blended learning in ELT as being driven primarily by ‘the twin drivers of economics (i.e. lower costs) and increasingly accessible and affordable hardware and software’. It might be nice to believe that ‘it is pedagogy, rather than technology, that should underpin the design of blended learning programmes’ (McCarthy, 2016, back cover), but the technology is the pedagogy here. Precisely how it is used is almost inevitably an afterthought.

Which pedagogy, anyway?

We can talk about putting pedagogy before technology, but this raises the question of which particular pedagogy we want to put in the driving seat. Presumably not all pedagogies are of equal value.

One of the most common uses of digital technology that has been designed specifically for language learning is the IWB- or platform-delivered coursebook and its accompanying digital workbook. We know that a majority of teachers using online coursebook packages direct their students more readily to tasks with clear right / wrong answers (e.g. drag-and-drop or gap-fill grammar exercises) than they do to the forum facilities where communicative language use is possible. Here, technology is merely replicating and, perhaps (because of its ease of use), encouraging established pedagogical practices. The pedagogy precedes the technology, but it’s probably not the best pedagogy in the world. Nor does it make best use of the technology’s potential. Would the affordances of the technology make a better starting point for course design?

Graham Stanley’s book (2013) offers suggestions for using technology for a variety of purposes, ranging from deliberate practice of grammar and vocabulary to ways of facilitating opportunities for skills practice. It’s an eclectic mix, similar to the range of activities on offer in the average coursebook for adults or teenagers. It is pedagogy-neutral in the sense that it does not offer a set of principles of language learning or teaching, and from these derive a set of practices for using the technology. It is a recipe book for using technological tools and, like all recipe books, prioritises activities over principles. I like the book and I don’t intend these comments as criticism. My point is simply that it’s not easy to take pedagogical principles as a starting point. Does the world of ELT even have generally agreed pedagogical principles?

And what is it that we’re teaching?

One final thought … If we consider how learners are likely to be using the English they are learning in their real-world futures, technology will not be far away: reading online, listening to / watching online material, writing and speaking with messaging apps, writing with text, email or Google Docs … If, in designing pedagogical approaches, we wish to include features of authentic language use, it’s hard to see how we can avoid placing technology fairly near the centre of the stage. Technologically-mediated language use is inseparable from pedagogy: one does not precede the other.

Similarly, if we believe that it is part of the English teacher’s job to develop the digital literacy (e.g. Hockly et al., 2013), visual literacy (e.g. Donaghy, 2015) or multimodal literacy of their students – not, incidentally, a belief that I share – then, again, technology cannot be separated from pedagogy.

Pedagogy before technology, OK??

So, I ask myself what precisely it is that people mean when they say that pedagogy should come before technology. The locutionary force, or referential meaning, usually remains unclear: in the absence of a particular pedagogy and particular contexts, what exactly is being said? The illocutionary force, likewise, is difficult to understand in the absence of a particular addressee: is the message only intended for teachers suffering from Everest Syndrome? And the perlocutionary force is equally intriguing: how are people who make the statement positioning themselves, and in relation to which addressee? Along the lines of green-washing and woke-washing, are we sometimes seeing cases of pedagogy-washing?

REFERENCES

Carrier, M., Damerow, R. M. & Bailey, K. M. (2017) Digital Language Learning and Teaching: Research, theory, and practice. New York: Routledge

Donaghy, K. (2015) Film in Action. Peaslake, Surrey: DELTA Publishing

Herodotou, C., Sharples, M., Gaved, M., Kukulska-Hulme, A., Rienties, B., Scanlon, E. & Whitelock, D. (2019) Innovative Pedagogies of the Future: An Evidence-Based Selection. Frontiers in Education, 4 (113)

Hockly, N. (2018) Blended Learning. ELT Journal 72 (1): pp. 97 – 101

Hockly, N., Dudeney, G. & Pegrum, M. (2013) Digital Literacies. Harlow: Pearson

Li, L. (2017) New Technologies and Language Learning. London: Palgrave

McCarthy, M. (Ed.) (2016) The Cambridge Guide to Blended Learning for Language Teaching. Cambridge: Cambridge University Press

OECD (2015) Students, Computers and Learning: Making the Connection, PISA. Paris: OECD Publishing

Sharma, P. (2010) Blended Learning. ELT Journal, 64 (4): pp. 456 – 458

Sharma, P. (2019) The Complete Guide to Running a Blended Learning Course. Oxford University Press English Language Teaching Global Blog 17 October 2019. Available at: https://oupeltglobalblog.com/2019/10/17/complete-guidagogyde-blended-learning/

Stanley, G. (2013) Language Learning with Technology. Cambridge: Cambridge University Press

Surkamp, C. & Viebrock, B. (Eds.) (2018) Teaching English as a Foreign Language: An Introduction. Stuttgart: J. B. Metzler

Walsh, P. (2019) Precarity. ELT Journal, 73 (4): pp. 459–462

Watson, D. M. (2001) Pedagogy before Technology: Re-thinking the Relationship between ICT and Teaching. Education and Information Technologies 6:4: pp.251–26

From time to time, I have mentioned Programmed Learning (or Programmed Instruction) in this blog (here and here, for example). It felt like time to go into a little more detail about what Programmed Instruction was (and is) and why I think it’s important to know about it.

A brief description

The basic idea behind Programmed Instruction was that subject matter could be broken down into very small parts, which could be organised into an optimal path for presentation to students. Students worked, at their own speed, through a series of micro-tasks, building their mastery of each nugget of learning that was presented, not progressing from one to the next until they had demonstrated they could respond accurately to the previous task.

There were two main types of Programmed Instruction: linear programming and branching programming. In the former, every student would follow the same path, the same sequence of frames. This could be used in classrooms for whole-class instruction and I tracked down a book (illustrated below) called ‘Programmed English Course Student’s Book 1’ (Hill, 1966), which was an attempt to transfer the ideas behind Programmed Instruction to a zero-tech, class environment. This is very similar in approach to the material I had to use when working at an Inlingua school in the 1980s.

Programmed English Course

Comparatives strip

An example of how self-paced programming worked is illustrated here, with a section on comparatives.

With branching programming, ‘extra frames (or branches) are provided for students who do not get the correct answer’ (Kay et al., 1968: 19). This was only suitable for self-study, but it was clearly preferable, as it allowed for self-pacing and some personalization. The material could be presented in books (which meant that students had to flick back and forth in their books) or with special ‘teaching machines’, but the latter were preferred.

In the words of an early enthusiast, Programmed Instruction was essentially ‘a device to control a student’s behaviour and help him to learn without the supervision of a teacher’ (Kay et al.,1968: 58). The approach was inspired by the work of Skinner and it was first used as part of a university course in behavioural psychology taught by Skinner at Harvard University in 1957. It moved into secondary schools for teaching mathematics in 1959 (Saettler, 2004: 297).

Enthusiasm and uptake

The parallels between current enthusiasm for the power of digital technology to transform education and the excitement about Programmed Instruction and teaching machines in the 1960s are very striking (McDonald et al., 2005: 90). In 1967, it was reported that ‘we are today on the verge of what promises to be a revolution in education’ (Goodman, 1967: 3) and that ‘tremors of excitement ran through professional journals and conferences and department meetings from coast to coast’ (Kennedy, 1967: 871). The following year, another commentator referred to the way that the field of education had been stirred ‘with an almost Messianic promise of a breakthrough’ (Ornstein, 1968: 401). Programmed instruction was also seen as an exciting business opportunity: ‘an entire industry is just coming into being and significant sales and profits should not be too long in coming’, wrote one hopeful financial analyst as early as 1961 (Kozlowski, 1967: 47).

The new technology seemed to offer a solution to the ‘problems of education’. Media reports in 1963 in Germany, for example, discussed a shortage of teachers, large classes and inadequate learning progress … ‘an ‘urgent pedagogical emergency’ that traditional teaching methods could not resolve’ (Hof, 2018). Individualised learning, through Programmed Instruction, would equalise educational opportunity and if you weren’t part of it, you would be left behind. In the US, two billion dollars were spent on educational technology by the government in the decade following the passing of the National Defense Education Act, and this was added to by grants from private foundations. As a result, ‘the production of teaching machines began to flourish, accompanied by the marketing of numerous ‘teaching units’ stamped into punch cards as well as less expensive didactic programme books and index cards. The market grew dramatically in a short time’ (Hof, 2018).

In the field of language learning, however, enthusiasm was more muted. In the year in which he completed his doctoral studies[1], the eminent linguist, Bernard Spolsky noted that ‘little use is actually being made of the new technique’ (Spolsky, 1966). A year later, a survey of over 600 foreign language teachers at US colleges and universities reported that only about 10% of them had programmed materials in their departments (Valdman, 1968: 1). In most of these cases, the materials ‘were being tried out on an experimental basis under the direction of their developers’. And two years after that, it was reported that ‘programming has not yet been used to any very great extent in language teaching, so there is no substantial body of experience from which to draw detailed, water-tight conclusions’ (Howatt, 1969: 164).

By the early 1970s, Programmed Instruction was already beginning to seem like yesterday’s technology, even though the principles behind it are still very much alive today (Thornbury (2017) refers to Duolingo as ‘Programmed Instruction’). It would be nice to think that language teachers of the day were more sceptical than, for example, their counterparts teaching mathematics. It would be nice to think that, like Spolsky, they had taken on board Chomsky’s (1959) demolition of Skinner. But the widespread popularity of Audiolingual methods suggests otherwise. Audiolingualism, based essentially on the same Skinnerian principles as Programmed Instruction, needed less outlay on technology. The machines (a slide projector and a record or tape player) were cheaper than the teaching machines, could be used for other purposes and did not become obsolete so quickly. The method also lent itself more readily to established school systems (i.e. whole-class teaching) and the skills sets of teachers of the day. Significantly, too, there was relatively little investment in Programmed Instruction for language teaching (compared to, say, mathematics), since this was a smallish and more localized market. There was no global market for English language learning as there is today.

Lessons to be learned

1 Shaping attitudes

It was not hard to persuade some educational authorities of the value of Programmed Instruction. As discussed above, it offered a solution to the problem of ‘the chronic shortage of adequately trained and competent teachers at all levels in our schools, colleges and universities’, wrote Goodman (1967: 3), who added, there is growing realisation of the need to give special individual attention to handicapped children and to those apparently or actually retarded’. The new teaching machines ‘could simulate the human teacher and carry out at least some of his functions quite efficiently’ (Goodman, 1967: 4). This wasn’t quite the same thing as saying that the machines could replace teachers, although some might have hoped for this. The official line was more often that the machines could ‘be used as devices, actively co-operating with the human teacher as adaptive systems and not just merely as aids’ (Goodman, 1967: 37). But this more nuanced message did not always get through, and ‘the Press soon stated that robots would replace teachers and conjured up pictures of classrooms of students with little iron men in front of them’ (Kay et al., 1968: 161).

For teachers, though, it was one thing to be told that the machines would free their time to perform more meaningful tasks, but harder to believe when this was accompanied by a ‘rhetoric of the instructional inadequacies of the teacher’ (McDonald, et al., 2005: 88). Many teachers felt threatened. They ‘reacted against the ‘unfeeling machine’ as a poor substitute for the warm, responsive environment provided by a real, live teacher. Others have seemed to take it more personally, viewing the advent of programmed instruction as the end of their professional career as teachers. To these, even the mention of programmed instruction produces a momentary look of panic followed by the appearance of determination to stave off the ominous onslaught somehow’ (Tucker, 1972: 63).

Some of those who were pushing for Programmed Instruction had a bigger agenda, with their sights set firmly on broader school reform made possible through technology (Hof, 2018). Individualised learning and Programmed Instruction were not just ends in themselves: they were ways of facilitating bigger changes. The trouble was that teachers were necessary for Programmed Instruction to work. On the practical level, it became apparent that a blend of teaching machines and classroom teaching was more effective than the machines alone (Saettler, 2004: 299). But the teachers’ attitudes were crucial: a research study involving over 6000 students of Spanish showed that ‘the more enthusiastic the teacher was about programmed instruction, the better the work the students did, even though they worked independently’ (Saettler, 2004: 299). In other researched cases, too, ‘teacher attitudes proved to be a critical factor in the success of programmed instruction’ (Saettler, 2004: 301).

2 Returns on investment

Pricing a hyped edtech product is a delicate matter. Vendors need to see a relatively quick return on their investment, before a newer technology knocks them out of the market. Developments in computing were fast in the late 1960s, and the first commercially successful personal computer, the Altair 8800, appeared in 1974. But too high a price carried obvious risks. In 1967, the cheapest teaching machine in the UK, the Tutorpack (from Packham Research Ltd), cost £7 12s (equivalent to about £126 today), but machines like these were disparagingly referred to as ‘page-turners’ (Higgins, 1983: 4). A higher-end linear programming machine cost twice this amount. Branching programme machines cost a lot more. The Mark II AutoTutor (from USI Great Britain Limited), for example, cost £31 per month (equivalent to £558), with eight reels of programmes thrown in (Goodman, 1967: 26). A lower-end branching machine, the Grundytutor, could be bought for £ 230 (worth about £4140 today).

Teaching machines (from Goodman)AutoTutor Mk II (from Goodman)

This was serious money, and any institution splashing out on teaching machines needed to be confident that they would be well used for a long period of time (Nordberg, 1965). The programmes (the software) were specific to individual machines and the content could not be updated easily. At the same time, other technological developments (cine projectors, tape recorders, record players) were arriving in classrooms, and schools found themselves having to pay for technical assistance and maintenance. The average teacher was ‘unable to avail himself fully of existing aids because, to put it bluntly, he is expected to teach for too many hours a day and simply has not the time, with all the administrative chores he is expected to perform, either to maintain equipment, to experiment with it, let alone keeping up with developments in his own and wider fields. The advent of teaching machines which can free the teacher to fulfil his role as an educator will intensify and not diminish the problem’ (Goodman, 1967: 44). Teaching machines, in short, were ‘oversold and underused’ (Cuban, 2001).

3 Research and theory

Looking back twenty years later, B. F. Skinner conceded that ‘the machines were crude, [and] the programs were untested’ (Skinner, 1986: 105). The documentary record suggests that the second part of this statement is not entirely true. Herrick (1966: 695) reported that ‘an overwhelming amount of research time has been invested in attempts to determine the relative merits of programmed instruction when compared to ‘traditional’ or ‘conventional’ methods of instruction. The results have been almost equally overwhelming in showing no significant differences’. In 1968, Kay et al (1968: 96) noted that ‘there has been a definite effort to examine programmed instruction’. A later meta-analysis of research in secondary education (Kulik et al.: 1982) confirmed that ‘Programmed Instruction did not typically raise student achievement […] nor did it make students feel more positively about the subjects they were studying’.

It was not, therefore, the case that research was not being done. It was that many people were preferring not to look at it. The same holds true for theoretical critiques. In relation to language learning, Spolsky (1966) referred to Chomsky’s (1959) rebuttal of Skinner’s arguments, adding that ‘there should be no need to rehearse these inadequacies, but as some psychologists and even applied linguists appear to ignore their existence it might be as well to remind readers of a few’. Programmed Instruction might have had a limited role to play in language learning, but vendors’ claims went further than that and some people believed them: ‘Rather than addressing themselves to limited and carefully specified FL tasks – for example the teaching of spelling, the teaching of grammatical concepts, training in pronunciation, the acquisition of limited proficiency within a restricted number of vocabulary items and grammatical features – most programmers aimed at self-sufficient courses designed to lead to near-native speaking proficiency’ (Valdman, 1968: 2).

4 Content

When learning is conceptualised as purely the acquisition of knowledge, technological optimists tend to believe that machines can convey it more effectively and more efficiently than teachers (Hof, 2018). The corollary of this is the belief that, if you get the materials right (plus the order in which they are presented and appropriate feedback), you can ‘to a great extent control and engineer the quality and quantity of learning’ (Post, 1972: 14). Learning, in other words, becomes an engineering problem, and technology is its solution.

One of the problems was that technology vendors were, first and foremost, technology specialists. Content was almost an afterthought. Materials writers needed to be familiar with the technology and, if not, they were unlikely to be employed. Writers needed to believe in the potential of the technology, so those familiar with current theory and research would clearly not fit in. The result was unsurprising. Kennedy (1967: 872) reported that ‘there are hundreds of programs now available. Many more will be published in the next few years. Watch for them. Examine them critically. They are not all of high quality’. He was being polite.

5 Motivation

As is usually the case with new technologies, there was a positive novelty effect with Programmed Instruction. And, as is always the case, the novelty effect wears off: ‘students quickly tired of, and eventually came to dislike, programmed instruction’ (McDonald et al.: 89). It could not really have been otherwise: ‘human learning and intrinsic motivation are optimized when persons experience a sense of autonomy, competence, and relatedness in their activity. Self-determination theorists have also studied factors that tend to occlude healthy functioning and motivation, including, among others, controlling environments, rewards contingent on task performance, the lack of secure connection and care by teachers, and situations that do not promote curiosity and challenge’ (McDonald et al.: 93). The demotivating experience of using these machines was particularly acute with younger and ‘less able’ students, as was noted at the time (Valdman, 1968: 9).

The unlearned lessons

I hope that you’ll now understand why I think the history of Programmed Instruction is so relevant to us today. In the words of my favourite Yogi-ism, it’s like deja vu all over again. I have quoted repeatedly from the article by McDonald et al (2005) and I would highly recommend it – available here. Hopefully, too, Audrey Watters’ forthcoming book, ‘Teaching Machines’, will appear before too long, and she will, no doubt, have much more of interest to say on this topic.

References

Chomsky N. 1959. ‘Review of Skinner’s Verbal Behavior’. Language, 35:26–58.

Cuban, L. 2001. Oversold & Underused: Computers in the Classroom. (Cambridge, MA: Harvard University Press)

Goodman, R. 1967. Programmed Learning and Teaching Machines 3rd edition. (London: English Universities Press)

Herrick, M. 1966. ‘Programmed Instruction: A critical appraisal’ The American Biology Teacher, 28 (9), 695 -698

Higgins, J. 1983. ‘Can computers teach?’ CALICO Journal, 1 (2)

Hill, L. A. 1966. Programmed English Course Student’s Book 1. (Oxford: Oxford University Press)

Hof, B. 2018. ‘From Harvard via Moscow to West Berlin: educational technology, programmed instruction and the commercialisation of learning after 1957’ History of Education, 47:4, 445-465

Howatt, A. P. R. 1969. Programmed Learning and the Language Teacher. (London: Longmans)

Kay, H., Dodd, B. & Sime, M. 1968. Teaching Machines and Programmed Instruction. (Harmondsworth: Penguin)

Kennedy, R.H. 1967. ‘Before using Programmed Instruction’ The English Journal, 56 (6), 871 – 873

Kozlowski, T. 1961. ‘Programmed Teaching’ Financial Analysts Journal, 17 / 6, 47 – 54

Kulik, C.-L., Schwalb, B. & Kulik, J. 1982. ‘Programmed Instruction in Secondary Education: A Meta-analysis of Evaluation Findings’ Journal of Educational Research, 75: 133 – 138

McDonald, J. K., Yanchar, S. C. & Osguthorpe, R.T. 2005. ‘Learning from Programmed Instruction: Examining Implications for Modern Instructional Technology’ Educational Technology Research and Development, 53 / 2, 84 – 98

Nordberg, R. B. 1965. Teaching machines-six dangers and one advantage. In J. S. Roucek (Ed.), Programmed teaching: A symposium on automation in education (pp. 1–8). (New York: Philosophical Library)

Ornstein, J. 1968. ‘Programmed Instruction and Educational Technology in the Language Field: Boon or Failure?’ The Modern Language Journal, 52 / 7, 401 – 410

Post, D. 1972. ‘Up the programmer: How to stop PI from boring learners and strangling results’. Educational Technology, 12(8), 14–1

Saettler, P. 2004. The Evolution of American Educational Technology. (Greenwich, Conn.: Information Age Publishing)

Skinner, B. F. 1986. ‘Programmed Instruction Revisited’ The Phi Delta Kappan, 68 (2), 103 – 110

Spolsky, B. 1966. ‘A psycholinguistic critique of programmed foreign language instruction’ International Review of Applied Linguistics in Language Teaching, Volume 4, Issue 1-4: 119–130

Thornbury, S. 2017. Scott Thornbury’s 30 Language Teaching Methods. (Cambridge: Cambridge University Press)

Tucker, C. 1972. ‘Programmed Dictation: An Example of the P.I. Process in the Classroom’. TESOL Quarterly, 6(1), 61-70

Valdman, A. 1968. ‘Programmed Instruction versus Guided Learning in Foreign Language Acquisition’ Die Unterrichtspraxis / Teaching German, 1 (2), 1 – 14

 

 

 

[1] Spolsky’ doctoral thesis for the University of Montreal was entitled ‘The psycholinguistic basis of programmed foreign language instruction’.

 

 

 

 

 

Book_coverIn my last post, I looked at shortcomings in edtech research, mostly from outside the world of ELT. I made a series of recommendations of ways in which such research could become more useful. In this post, I look at two very recent collections of ELT edtech research. The first of these is Digital Innovations and Research in Language Learning, edited by Mavridi and Saumell, and published this February by the Learning Technologies SIG of IATEFL. I’ll refer to it here as DIRLL. It’s available free to IATEFL LT SIG members, and can be bought for $10.97 as an ebook on Amazon (US). The second is the most recent edition (February 2020) of the Language Learning & Technology journal, which is open access and available here. I’ll refer to it here as LLTJ.

In both of these collections, the focus is not on ‘technology per se, but rather issues related to language learning and language teaching, and how they are affected or enhanced by the use of digital technologies’. However, they are very different kinds of publication. Nobody involved in the production of DIRLL got paid in any way (to the best of my knowledge) and, in keeping with its provenance from a teachers’ association, has ‘a focus on the practitioner as teacher-researcher’. Almost all of the contributing authors are university-based, but they are typically involved more in language teaching than in research. With one exception (a grant from the EU), their work was unfunded.

The triannual LLTJ is funded by two American universities and published by the University of Hawaii Press. The editors and associate editors are well-known scholars in their fields. The journal’s impact factor is high, close to the impact factor of the paywalled reCALL (published by the University of Cambridge), which is the highest-ranking journal in the field of CALL. The contributing authors are all university-based, many with a string of published articles (in prestige journals), chapters or books behind them. At least six of the studies were funded by national grant-awarding bodies.

I should begin by making clear that there was much in both collections that I found interesting. However, it was not usually the research itself that I found informative, but the literature review that preceded it. Two of the chapters in DIRLL were not really research, anyway. One was the development of a template for evaluating ICT-mediated tasks in CLIL, another was an advocacy of comics as a resource for language teaching. Both of these were new, useful and interesting to me. LLTJ included a valuable literature review of research into VR in FL learning (but no actual new research). With some exceptions in both collections, though, I felt that I would have been better off curtailing my reading after the reviews. Admittedly, there wouldn’t be much in the way of literature reviews if there were no previous research to report …

It was no surprise to see the learners who were the subjects of this research were overwhelmingly university students. In fact, only one article (about a high-school project in Israel, reported in DIRLL) was not about university students. The research areas focused on reflected this bias towards tertiary contexts: online academic reading skills, academic writing, online reflective practices in teacher training programmes, etc.

In a couple of cases, the selection of experimental subjects seemed plain bizarre. Why, if you want to find out about the extent to which Moodle use can help EAP students become better academic readers (in DIRLL), would you investigate this with a small volunteer cohort of postgraduate students of linguistics, with previous experience of using Moodle and experience of teaching? Is a less representative sample imaginable? Why, if you want to investigate the learning potential of the English File Pronunciation app (reported in LLTJ), which is clearly most appropriate for A1 – B1 levels, would you do this with a group of C1-level undergraduates following a course in phonetics as part of an English Studies programme?

More problematic, in my view, was the small sample size in many of the research projects. The Israeli virtual high school project (DIRLL), previously referred to, started out with only 11 students, but 7 dropped out, primarily, it seems, because of institutional incompetence: ‘the project was probably doomed […] to failure from the start’, according to the author. Interesting as this was as an account of how not to set up a project of this kind, it is simply impossible to draw any conclusions from 4 students about the potential of a VLE for ‘interaction, focus and self-paced learning’. The questionnaire investigating experience of and attitudes towards VR (in DIRLL) was completed by only 7 (out of 36 possible) students and 7 (out of 70+ possible) teachers. As the author acknowledges, ‘no great claims can be made’, but then goes on to note the generally ‘positive attitudes to VR’. Perhaps those who did not volunteer had different attitudes? We will never know. The study of motivational videos in tertiary education (DIRLL) started off with 15 subjects, but 5 did not complete the necessary tasks. The research into L1 use in videoconferencing (LLTJ) started off with 10 experimental subjects, all with the same L1 and similar cultural backgrounds, but there was no data available from 4 of them (because they never switched into L1). The author claims that the paper demonstrates ‘how L1 is used by language learners in videoconferencing as a social semiotic resource to support social presence’ – something which, after reading the literature review, we already knew. But the paper also demonstrates quite clearly how L1 is not used by language learners in videoconferencing as a social semiotic resource to support social presence. In all these cases, it is the participants who did not complete or the potential participants who did not want to take part that have the greatest interest for me.

Unsurprisingly, the LLTJ articles had larger sample sizes than those in DIRLL, but in both collections the length of the research was limited. The production of one motivational video (DIRLL) does not really allow us to draw any conclusions about the development of students’ critical thinking skills. Two four-week interventions do not really seem long enough to me to discover anything about learner autonomy and Moodle (DIRLL). An experiment looking at different feedback modes needs more than two written assignments to reach any conclusions about student preferences (LLTJ).

More research might well be needed to compensate for the short-term projects with small sample sizes, but I’m not convinced that this is always the case. Lacking sufficient information about the content of the technologically-mediated tools being used, I was often unable to reach any conclusions. A gamified Twitter environment was developed in one project (DIRLL), using principles derived from contemporary literature on gamification. The authors concluded that the game design ‘failed to generate interaction among students’, but without knowing a lot more about the specific details of the activity, it is impossible to say whether the problem was the principles or the particular instantiation of those principles. Another project, looking at the development of pronunciation materials for online learning (LLTJ), came to the conclusion that online pronunciation training was helpful – better than none at all. Claims are then made about the value of the method used (called ‘innovative Cued Pronunciation Readings’), but this is not compared to any other method / materials, and only a very small selection of these materials are illustrated. Basically, the reader of this research has no choice but to take things on trust. The study looking at the use of Alexa to help listening comprehension and speaking fluency (LLTJ) cannot really tell us anything about IPAs unless we know more about the particular way that Alexa is being used. Here, it seems that the students were using Alexa in an interactive storytelling exercise, but so little information is given about the exercise itself that I didn’t actually learn anything at all. The author’s own conclusion is that the results, such as they are, need to be treated with caution. Nevertheless, he adds ‘the current study illustrates that IPAs may have some value to foreign language learners’.

This brings me onto my final gripe. To be told that IPAs like Alexa may have some value to foreign language learners is to be told something that I already know. This wasn’t the only time this happened during my reading of these collections. I appreciate that research cannot always tell us something new and interesting, but a little more often would be nice. I ‘learnt’ that goal-setting plays an important role in motivation and that gamification can boost short-term motivation. I ‘learnt’ that reflective journals can take a long time for teachers to look at, and that reflective video journals are also very time-consuming. I ‘learnt’ that peer feedback can be very useful. I ‘learnt’ from two papers that intercultural difficulties may be exacerbated by online communication. I ‘learnt’ that text-to-speech software is pretty good these days. I ‘learnt’ that multimodal literacy can, most frequently, be divided up into visual and auditory forms.

With the exception of a piece about online safety issues (DIRLL), I did not once encounter anything which hinted that there may be problems in using technology. No mention of the use to which student data might be put. No mention of the costs involved (except for the observation that many students would not be happy to spend money on the English File Pronunciation app) or the cost-effectiveness of digital ‘solutions’. No consideration of the institutional (or other) pressures (or the reasons behind them) that may be applied to encourage teachers to ‘leverage’ edtech. No suggestion that a zero-tech option might actually be preferable. In both collections, the language used is invariably positive, or, at least, technology is associated with positive things: uncovering the possibilities, promoting autonomy, etc. Even if the focus of these publications is not on technology per se (although I think this claim doesn’t really stand up to close examination), it’s a little disingenuous to claim (as LLTJ does) that the interest is in how language learning and language teaching is ‘affected or enhanced by the use of digital technologies’. The reality is that the overwhelming interest is in potential enhancements, not potential negative effects.

I have deliberately not mentioned any names in referring to the articles I have discussed. I would, though, like to take my hat off to the editors of DIRLL, Sophia Mavridi and Vicky Saumell, for attempting to do something a little different. I think that Alicia Artusi and Graham Stanley’s article (DIRLL) about CPD for ‘remote’ teachers was very good and should interest the huge number of teachers working online. Chryssa Themelis and Julie-Ann Sime have kindled my interest in the potential of comics as a learning resource (DIRLL). Yu-Ju Lan’s article about VR (LLTJ) is surely the most up-to-date, go-to article on this topic. There were other pieces, or parts of pieces, that I liked, too. But, to me, it’s clear that ‘more research is needed’ … much less than (1) better and more critical research, and (2) more digestible summaries of research.

Colloquium

At the beginning of March, I’ll be going to Cambridge to take part in a Digital Learning Colloquium (for more information about the event, see here ). One of the questions that will be explored is how research might contribute to the development of digital language learning. In this, the first of two posts on the subject, I’ll be taking a broad overview of the current state of play in edtech research.

I try my best to keep up to date with research. Of the main journals, there are Language Learning and Technology, which is open access; CALICO, which offers quite a lot of open access material; and reCALL, which is the most restricted in terms of access of the three. But there is something deeply frustrating about most of this research, and this is what I want to explore in these posts. More often than not, research articles end with a call for more research. And more often than not, I find myself saying ‘Please, no, not more research like this!’

First, though, I would like to turn to a more reader-friendly source of research findings. Systematic reviews are, basically literature reviews which can save people like me from having to plough through endless papers on similar subjects, all of which contain the same (or similar) literature review in the opening sections. If only there were more of them. Others agree with me: the conclusion of one systematic review of learning and teaching with technology in higher education (Lillejord et al., 2018) was that more systematic reviews were needed.

Last year saw the publication of a systematic review of research on artificial intelligence applications in higher education (Zawacki-Richter, et al., 2019) which caught my eye. The first thing that struck me about this review was that ‘out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis’. In other words, only just over 5% of the research was considered worthy of inclusion.

The review did not paint a very pretty picture of the current state of AIEd research. As the second part of the title of this review (‘Where are the educators?’) makes clear, the research, taken as a whole, showed a ‘weak connection to theoretical pedagogical perspectives’. This is not entirely surprising. As Bates (2019) has noted: ‘since AI tends to be developed by computer scientists, they tend to use models of learning based on how computers or computer networks work (since of course it will be a computer that has to operate the AI). As a result, such AI applications tend to adopt a very behaviourist model of learning: present / test / feedback.’ More generally, it is clear that technology adoption (and research) is being driven by technology enthusiasts, with insufficient expertise in education. The danger is that edtech developers ‘will simply ‘discover’ new ways to teach poorly and perpetuate erroneous ideas about teaching and learning’ (Lynch, 2017).

This, then, is the first of my checklist of things that, collectively, researchers need to do to improve the value of their work. The rest of this list is drawn from observations mostly, but not exclusively, from the authors of systematic reviews, and mostly come from reviews of general edtech research. In the next blog post, I’ll look more closely at a recent collection of ELT edtech research (Mavridi & Saumell, 2020) to see how it measures up.

1 Make sure your research is adequately informed by educational research outside the field of edtech

Unproblematised behaviourist assumptions about the nature of learning are all too frequent. References to learning styles are still fairly common. The most frequently investigated skill that is considered in the context of edtech is critical thinking (Sosa Neira, et al., 2017), but this is rarely defined and almost never problematized, despite a broad literature that questions the construct.

2 Adopt a sceptical attitude from the outset

Know your history. Decades of technological innovation in education have shown precious little in the way of educational gains and, more than anything else, have taught us that we need to be sceptical from the outset. ‘Enthusiasm and praise that are directed towards ‘virtual education, ‘school 2.0’, ‘e-learning and the like’ (Selwyn, 2014: vii) are indications that the lessons of the past have not been sufficiently absorbed (Levy, 2016: 102). The phrase ‘exciting potential’, for example, should be banned from all edtech research. See, for example, a ‘state-of-the-art analysis of chatbots in education’ (Winkler & Söllner, 2018), which has nothing to conclude but ‘exciting potential’. Potential is fine (indeed, it is perhaps the only thing that research can unambiguously demonstrate – see section 3 below), but can we try to be a little more grown-up about things?

3 Know what you are measuring

Measuring learning outcomes is tricky, to say the least, but it’s understandable that researchers should try to focus on them. Unfortunately, ‘the vast array of literature involving learning technology evaluation makes it challenging to acquire an accurate sense of the different aspects of learning that are evaluated, and the possible approaches that can be used to evaluate them’ (Lai & Bower, 2019). Metrics such as student grades are hard to interpret, not least because of the large number of variables and the danger of many things being conflated in one score. Equally, or possibly even more, problematic, are self-reporting measures which are rarely robust. It seems that surveys are the most widely used instrument in qualitative research (Sosa Neira, et al., 2017), but these will tell us little or nothing when used for short-term interventions (see point 5 below).

4 Ensure that the sample size is big enough to mean something

In most of the research into digital technology in education that was analysed in a literature review carried out for the Scottish government (ICF Consulting Services Ltd, 2015), there were only ‘small numbers of learners or teachers or schools’.

5 Privilege longitudinal studies over short-term projects

The Scottish government literature review (ICF Consulting Services Ltd, 2015), also noted that ‘most studies that attempt to measure any outcomes focus on short and medium term outcomes’. The fact that the use of a particular technology has some sort of impact over the short or medium term tells us very little of value. Unless there is very good reason to suspect the contrary, we should assume that it is a novelty effect that has been captured (Levy, 2016: 102).

6 Don’t forget the content

The starting point of much edtech research is the technology, but most edtech, whether it’s a flashcard app or a full-blown Moodle course, has content. Research reports rarely give details of this content, assuming perhaps that it’s just fine, and all that’s needed is a little tech to ‘present learners with the ‘right’ content at the ‘right’ time’ (Lynch, 2017). It’s a foolish assumption. Take a random educational app from the Play Store, a random MOOC or whatever, and the chances are you’ll find it’s crap.

7 Avoid anecdotal accounts of technology use in quasi-experiments as the basis of a ‘research article’

Control (i.e technology-free) groups may not always be possible but without them, we’re unlikely to learn much from a single study. What would, however, be extremely useful would be a large, collated collection of such action-research projects, using the same or similar technology, in a variety of settings. There is a marked absence of this kind of work.

8 Enough already of higher education contexts

Researchers typically work in universities where they have captive students who they can carry out research on. But we have a problem here. The systematic review of Lundin et al (2018), for example, found that ‘studies on flipped classrooms are dominated by studies in the higher education sector’ (besides lacking anchors in learning theory or instructional design). With some urgency, primary and secondary contexts need to be investigated in more detail, not just regarding flipped learning.

9 Be critical

Very little edtech research considers the downsides of edtech adoption. Online safety, privacy and data security are hardly peripheral issues, especially with younger learners. Ignoring them won’t make them go away.

More research?

So do we need more research? For me, two things stand out. We might benefit more from, firstly, a different kind of research, and, secondly, more syntheses of the work that has already been done. Although I will probably continue to dip into the pot-pourri of articles published in the main CALL journals, I’m looking forward to a change at the CALICO journal. From September of this year, one issue a year will be thematic, with a lead article written by established researchers which will ‘first discuss in broad terms what has been accomplished in the relevant subfield of CALL. It should then outline which questions have been answered to our satisfaction and what evidence there is to support these conclusions. Finally, this article should pose a “soft” research agenda that can guide researchers interested in pursuing empirical work in this area’. This will be followed by two or three empirical pieces that ‘specifically reflect the research agenda, methodologies, and other suggestions laid out in the lead article’.

But I think I’ll still have a soft spot for some of the other journals that are coyer about their impact factor and that can be freely accessed. How else would I discover (it would be too mean to give the references here) that ‘the effective use of new technologies improves learners’ language learning skills’? Presumably, the ineffective use of new technologies has the opposite effect? Or that ‘the application of modern technology represents a significant advance in contemporary English language teaching methods’?

References

Bates, A. W. (2019). Teaching in a Digital Age Second Edition. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from https://pressbooks.bccampus.ca/teachinginadigitalagev2/

ICF Consulting Services Ltd (2015). Literature Review on the Impact of Digital Technology on Learning and Teaching. Edinburgh: The Scottish Government. https://dera.ioe.ac.uk/24843/1/00489224.pdf

Lai, J.W.M. & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133(1), 27-42. Elsevier Ltd. Retrieved January 14, 2020 from https://www.learntechlib.org/p/207137/

Levy, M. 2016. Researching in language learning and technology. In Farr, F. & Murray, L. (Eds.) The Routledge Handbook of Language Learning and Technology. Abingdon, Oxon.: Routledge. pp.101 – 114

Lillejord S., Børte K., Nesje K. & Ruud E. (2018). Learning and teaching with technology in higher education – a systematic review. Oslo: Knowledge Centre for Education https://www.forskningsradet.no/siteassets/publikasjoner/1254035532334.pdf

Lundin, M., Bergviken Rensfeldt, A., Hillman, T. et al. (2018). Higher education dominance and siloed knowledge: a systematic review of flipped classroom research. International Journal of Educational Technology in Higher Education 15, 20 (2018) doi:10.1186/s41239-018-0101-6

Lynch, J. (2017). How AI Will Destroy Education. Medium, November 13, 2017. https://buzzrobot.com/how-ai-will-destroy-education-20053b7b88a6

Mavridi, S. & Saumell, V. (Eds.) (2020). Digital Innovations and Research in Language Learning. Faversham, Kent: IATEFL

Selwyn, N. (2014). Distrusting Educational Technology. New York: Routledge

Sosa Neira, E. A., Salinas, J. and de Benito Crosetti, B. (2017). Emerging Technologies (ETs) in Education: A Systematic Review of the Literature Published between 2006 and 2016. International Journal of Emerging Technologies in Education, 12 (5). https://online-journals.org/index.php/i-jet/article/view/6939

Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA. https://www.alexandria.unisg.ch/254848/1/JML_699.pdf

Zawacki-Richter, O., Bond, M., Marin, V. I. And Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education 2019

Screenshot_20191011-200743_ChromeOver the last week, the Guardian has been running a series of articles on the global corporations that contribute most to climate change and the way that these vested interests lobby against changes to the law which might protect the planet. Beginning in the 1990s, an alliance of fossil fuel and automobile corporations, along with conservative think tanks and politicians, developed a ‘denial machine’ which sought to undermine the scientific consensus on climate change. Between 2003 and 2010, it has been estimated that over $550 million was received from a variety of sources to support this campaign. The Guardian’s current series is an update and reminder of the research into climate change denial that has been carried out in recent years.

In the past, it was easier to trace where the money came from (e.g. ExxonMobil or Koch Industries), but it appears that the cash is now being channelled through foundations like Donors Trust and Donors Capital, who, in turn, pass it on to other foundations and think tanks (see below) that promote the denial of climate change.

The connection between climate change denial and edtech becomes clear when you look at the organisations behind the ‘denial machine’. I have written about some of these organisations before (see this post ) so when I read the reports in the Guardian, there were some familiar names.

Besides their scepticism about climate change, all of the organisations believe that education should be market-driven, free from governmental interference, and characterised by consumer choice. These aims are facilitated by the deployment of educational technology. Here are some examples.

State Policy Network

The State Policy Network (SPN) is an American umbrella organization for a large group of conservative and libertarian think tanks that seek to influence national and global policies. Among other libertarian causes, it opposes climate change regulations and supports the privatisation of education, in particular the expansion of ‘digital education’.

The Cato Institute

The mission of the Cato Institute, a member of the SPN, ‘is to originate, disseminate, and increase understanding of public policies based on the principles of individual liberty, limited government, free markets, and peace. Our vision is to create free, open, and civil societies founded on libertarian principles’. The Institute has said that it had never been in the business of “promoting climate science denial”; it did not dispute human activity’s impact on the climate, but believed it was minimal. Turning to education, it believes that ‘states should institute school choice on a broad scale, moving toward a competitive education market. The only way to transform the system is to break up the long-standing government monopoly and use the dynamics of the market to create innovations, better methods, and new schools’. Innovations and better methods will, of course, be driven by technology.

FreedomWorks

FreedomWorks, another member of the SPN and another conservative and libertarian advocacy group, is widely associated with the Tea Party Movement . Recent posts on its blog have been entitled ‘The Climate Crisis that Wasn’t: Scientists Agree there is “No Cause for Alarm”’, ‘Climate Protesters: If You Want to Save the Planet, You Should Support Capitalism Not Socialism’ and ‘Electric Vehicle Tax Credit: Nothing But Regressive Cronyism’. Its approach to education is equally uncompromising. It seeks to abolish the US Department of Education, describes American schools as ‘failing’, wants market-driven educational provision and absolute parental choice . Technology will play a fundamental role in bringing about the desired changes: ‘just as computers and the Internet have fundamentally reshaped the way we do business, they will also soon reshape education’ .

The Heritage Foundation

The Heritage Foundation, the last of the SPN members that I’ll mention here, is yet another conservative American think tank which rejects the scientific consensus on climate change . Its line on education is neatly summed up in this extract from a blog post by a Heritage senior policy analyst: ‘Virtual or online learning is revolutionizing American education. It has the potential to dramatically expand the educational opportunities of American students, largely overcoming the geographic and demographic restrictions. Virtual learning also has the potential to improve the quality of instruction, while increasing productivity and lowering costs, ultimately reducing the burden on taxpayers‘.

The Institute of Economic Affairs

Just to show that the ‘denial machine’ isn’t an exclusively American phenomenon, I include ‘the UK’s most influential conservative think tank [which] has published at least four books, as well as multiple articles and papers, over two decades suggesting manmade climate change may be uncertain or exaggerated. In recent years the group has focused more on free-market solutions to reducing carbon emissions’ . It is an ‘associate member of the SPN’ . No surprise to discover that a member of the advisory council of the IEA is James Tooley, a close associate of Michael Barber, formerly Chief Education Advisor at Pearson. Tooley’s articles for the IEA include ‘Education without the State’  and ‘Transforming incentives will unleash the power of entrepreneurship in the education sector’ .

The IEA does not disclose its funding, but anyone interested in finding out more should look here ‘Revealed: how the UK’s powerful right-wing think tanks and Conservative MPs work together’ .

Microsoft, Facebook and Google

Let me be clear to start: Microsoft, Facebook and Google are not climate change deniers. However, Facebook and Microsoft are financial backers of the SPN. In a statement, a spokesperson for Microsoft said: “As a large company, Microsoft has great interest in the many policy issues discussed across the country. We have a longstanding record of engaging with a broad assortment of groups on a bipartisan basis, both at the national and local level. In regard to State Policy Network, Microsoft has focused our participation on their technology policy work group because it is valuable forum to hear various perspectives about technology challenges and to share potential solutions” . Google has made substantial contributions to the Competitive Enterprise Institute (a conservative US policy group ‘that was instrumental in convincing the Trump administration to abandon the Paris agreement and has criticised the White House for not dismantling more environmental rules). In the Guardian report, Google ‘defended its contributions, saying that its “collaboration” with organisations such as CEI “does not mean we endorse the organisations’ entire agenda”. “When it comes to regulation of technology, Google has to find friends wherever they can and I think it is wise that the company does not apply litmus tests to who they support,” the source said’ .

You have to wonder what these companies (all of whom support environmental causes in various ways) might consider more important than the future of the planet. Could it be that the libertarian think tanks are important allies in resisting any form of internet governance, in objecting to any constraints on the capture of data?

Back in the middle of the last century, the first interactive machines for language teaching appeared. Previously, there had been phonograph discs and wire recorders (Ornstein, 1968: 401), but these had never really taken off. This time, things were different. Buoyed by a belief in the power of technology, along with the need (following the Soviet Union’s successful Sputnik programme) to demonstrate the pre-eminence of the United States’ technological expertise, the interactive teaching machines that were used in programmed instruction promised to revolutionize language learning (Valdman, 1968: 1). From coast to coast, ‘tremors of excitement ran through professional journals and conferences and department meetings’ (Kennedy, 1967: 871). The new technology was driven by hard science, supported and promoted by the one of the most well-known and respected psychologists and public intellectuals of the day (Skinner, 1961).

In classrooms, the machines acted as powerfully effective triggers in generating situational interest (Hidi & Renninger, 2006). Even more exciting than the mechanical teaching machines were the computers that were appearing on the scene. ‘Lick’ Licklider, a pioneer in interactive computing at the Advanced Research Projects Agency in Arlington, Virginia, developed an automated drill routine for learning German by hooking up a computer, two typewriters, an oscilloscope and a light pen (Noble, 1991: 124). Students loved it, and some would ‘go on and on, learning German words until they were forced by scheduling to cease their efforts’. Researchers called the seductive nature of the technology ‘stimulus trapping’, and Licklider hoped that ‘before [the student] gets out from under the control of the computer’s incentives, [they] will learn enough German words’ (Noble, 1991: 125).

With many of the developed economies of the world facing a critical shortage of teachers, ‘an urgent pedagogical emergency’ (Hof, 2018), the new approach was considered to be extremely efficient and could equalise opportunity in schools across the country. It was ‘here to stay: [it] appears destined to make progress that could well go beyond the fondest dreams of its originators […] an entire industry is just coming into being and significant sales and profits should not be too long in coming’ (Kozlowski, 1961: 47).

Unfortunately, however, researchers and entrepreneurs had massively underestimated the significance of novelty effects. The triggered situational interest of the machines did not lead to intrinsic individual motivation. Students quickly tired of, and eventually came to dislike, programmed instruction and the machines that delivered it (McDonald et al.: 2005: 89). What’s more, the machines were expensive and ‘research studies conducted on its effectiveness showed that the differences in achievement did not constantly or substantially favour programmed instruction over conventional instruction (Saettler, 2004: 303). Newer technologies, with better ‘stimulus trapping’, were appearing. Programmed instruction lost its backing and disappeared, leaving as traces only its interest in clearly defined learning objectives, the measurement of learning outcomes and a concern with the efficiency of learning approaches.

Hot on the heels of programmed instruction came the language laboratory. Futuristic in appearance, not entirely unlike the deck of the starship USS Enterprise which launched at around the same time, language labs captured the public imagination and promised to explore the final frontiers of language learning. As with the earlier teaching machines, students were initially enthusiastic. Even today, when language labs are introduced into contexts where they may be perceived as new technology, they can lead to high levels of initial motivation (e.g. Ramganesh & Janaki, 2017).

Given the huge investments into these labs, it’s unfortunate that initial interest waned fast. By 1969, many of these rooms had turned into ‘“electronic graveyards,” sitting empty and unused, or perhaps somewhat glorified study halls to which students grudgingly repair to don headphones, turn down the volume, and prepare the next period’s history or English lesson, unmolested by any member of the foreign language faculty’ (Turner, 1969: 1, quoted in Roby, 2003: 527). ‘Many second language students shudder[ed] at the thought of entering into the bowels of the “language laboratory” to practice and perfect the acoustical aerobics of proper pronunciation skills. Visions of sterile white-walled, windowless rooms, filled with endless bolted-down rows of claustrophobic metal carrels, and overseen by a humorless, lab director, evoke[d] fear in the hearts of even the most stout-hearted prospective second-language learners (Wiley, 1990: 44).

By the turn of this century, language labs had mostly gone, consigned to oblivion by the appearance of yet newer technology: the internet, laptops and smartphones. Education had been on the brink of being transformed through new learning technologies for decades (Laurillard, 2008: 1), but this time it really was different. It wasn’t just one technology that had appeared, but a whole slew of them: ‘artificial intelligence, learning analytics, predictive analytics, adaptive learning software, school management software, learning management systems (LMS), school clouds. No school was without these and other technologies branded as ‘superintelligent’ by the late 2020s’ (Macgilchrist et al., 2019). The hardware, especially phones, was ubiquitous and, therefore, free. Unlike teaching machines and language laboratories, students were used to using the technology and expected to use their devices in their studies.

A barrage of publicity, mostly paid for by the industry, surrounded the new technologies. These would ‘meet the demands of Generation Z’, the new generation of students, now cast as consumers, who ‘were accustomed to personalizing everything’.  AR, VR, interactive whiteboards, digital projectors and so on made it easier to ‘create engaging, interactive experiences’. The ‘New Age’ technologies made learning fun and easy,  ‘bringing enthusiasm among the students, improving student engagement, enriching the teaching process, and bringing liveliness in the classroom’. On top of that, they allowed huge amounts of data to be captured and sold, whilst tracking progress and attendance. In any case, resistance to digital technology, said more than one language teaching expert, was pointless (Styring, 2015).slide

At the same time, technology companies increasingly took on ‘central roles as advisors to national governments and local districts on educational futures’ and public educational institutions came to be ‘regarded by many as dispensable or even harmful’ (Macgilchrist et al., 2019).

But, as it turned out, the students of Generation Z were not as uniformly enthusiastic about the new technology as had been assumed, and resistance to digital, personalized delivery in education was not long in coming. In November 2018, high school students at Brooklyn’s Secondary School for Journalism staged a walkout in protest at their school’s use of Summit Learning, a web-based platform promoting personalized learning developed by Facebook. They complained that the platform resulted in coursework requiring students to spend much of their day in front of a computer screen, that made it easy to cheat by looking up answers online, and that some of their teachers didn’t have the proper training for the curriculum (Leskin, 2018). Besides, their school was in a deplorable state of disrepair, especially the toilets. There were similar protests in Kansas, where students staged sit-ins, supported by their parents, one of whom complained that ‘we’re allowing the computers to teach and the kids all looked like zombies’ before pulling his son out of the school (Bowles, 2019). In Pennsylvania and Connecticut, some schools stopped using Summit Learning altogether, following protests.

But the resistance did not last. Protesters were accused of being nostalgic conservatives and educationalists kept largely quiet, fearful of losing their funding from the Chan Zuckerberg Initiative (Facebook) and other philanthro-capitalists. The provision of training in grit, growth mindset, positive psychology and mindfulness (also promoted by the technology companies) was ramped up, and eventually the disaffected students became more quiescent. Before long, the data-intensive, personalized approach, relying on the tools, services and data storage of particular platforms had become ‘baked in’ to educational systems around the world (Moore, 2018: 211). There was no going back (except for small numbers of ultra-privileged students in a few private institutions).

By the middle of the century (2155), most students, of all ages, studied with interactive screens in the comfort of their homes. Algorithmically-driven content, with personalized, adaptive tests had become the norm, but the technology occasionally went wrong, leading to some frustration. One day, two young children discovered a book in their attic. Made of paper with yellow, crinkly pages, where ‘the words stood still instead of moving the way they were supposed to’. The book recounted the experience of schools in the distant past, where ‘all the kids from the neighbourhood came’, sitting in the same room with a human teacher, studying the same things ‘so they could help one another on the homework and talk about it’. Margie, the younger of the children at 11 years old, was engrossed in the book when she received a nudge from her personalized learning platform to return to her studies. But Margie was reluctant to go back to her fractions. She ‘was thinking about how the kids must have loved it in the old days. She was thinking about the fun they had’ (Asimov, 1951).

References

Asimov, I. 1951. The Fun They Had. Accessed September 20, 2019. http://web1.nbed.nb.ca/sites/ASD-S/1820/J%20Johnston/Isaac%20Asimov%20-%20The%20fun%20they%20had.pdf

Bowles, N. 2019. ‘Silicon Valley Came to Kansas Schools. That Started a Rebellion’ The New York Times, April 21. Accessed September 20, 2019. https://www.nytimes.com/2019/04/21/technology/silicon-valley-kansas-schools.html

Hidi, S. & Renninger, K.A. 2006. ‘The Four-Phase Model of Interest Development’ Educational Psychologist, 41 (2), 111 – 127

Hof, B. 2018. ‘From Harvard via Moscow to West Berlin: educational technology, programmed instruction and the commercialisation of learning after 1957’ History of Education, 47 (4): 445-465

Kennedy, R.H. 1967. ‘Before using Programmed Instruction’ The English Journal, 56 (6), 871 – 873

Kozlowski, T. 1961. ‘Programmed Teaching’ Financial Analysts Journal, 17 (6): 47 – 54

Laurillard, D. 2008. Digital Technologies and their Role in Achieving our Ambitions for Education. London: Institute for Education.

Leskin, P. 2018. ‘Students in Brooklyn protest their school’s use of a Zuckerberg-backed online curriculum that Facebook engineers helped build’ Business Insider, 12.11.18 Accessed 20 September 2019. https://www.businessinsider.de/summit-learning-school-curriculum-funded-by-zuckerberg-faces-backlash-brooklyn-2018-11?r=US&IR=T

McDonald, J. K., Yanchar, S. C. & Osguthorpe, R.T. 2005. ‘Learning from Programmed Instruction: Examining Implications for Modern Instructional Technology’ Educational Technology Research and Development, 53 (2): 84 – 98

Macgilchrist, F., Allert, H. & Bruch, A. 2019. ‚Students and society in the 2020s. Three future ‘histories’ of education and technology’. Learning, Media and Technology, https://www.tandfonline.com/doi/full/10.1080/17439884.2019.1656235 )

Moore, M. 2018. Democracy Hacked. London: Oneworld

Noble, D. D. 1991. The Classroom Arsenal. London: The Falmer Press

Ornstein, J. 1968. ‘Programmed Instruction and Educational Technology in the Language Field: Boon or Failure?’ The Modern Language Journal, 52 (7), 401 – 410

Ramganesh, E. & Janaki, S. 2017. ‘Attitude of College Teachers towards the Utilization of Language Laboratories for Learning English’ Asian Journal of Social Science Studies; Vol. 2 (1): 103 – 109

Roby, W.B. 2003. ‘Technology in the service of foreign language teaching: The case of the language laboratory’ In D. Jonassen (ed.), Handbook of Research on Educational Communications and Technology, 2nd ed.: 523 – 541. Mahwah, NJ.: Lawrence Erlbaum Associates

Saettler, P. 2004. The Evolution of American Educational Technology. Greenwich, Conn.: Information Age Publishing

Skinner, B. F. 1961. ‘Teaching Machines’ Scientific American, 205(5), 90-107

Styring, J. 2015. Engaging Generation Z. Cambridge English webinar 2015 https://www.youtube.com/watch?time_continue=4&v=XCxl4TqgQZA

Valdman, A. 1968. ‘Programmed Instruction versus Guided Learning in Foreign Language Acquisition’ Die Unterrichtspraxis / Teaching German, 1 (2), 1 – 14.

Wiley, P. D. 1990. ‘Language labs for 1990: User-friendly, expandable and affordable’. Media & Methods, 27(1), 44–47)

jenny-holzer-untitled-protect-me-from-what-i-want-text-displayed-in-times-square-nyc-1982

Jenny Holzer, Protect me from what I want

At a recent ELT conference, a plenary presentation entitled ‘Getting it right with edtech’ (sponsored by a vendor of – increasingly digital – ELT products) began with the speaker suggesting that technology was basically neutral, that what you do with educational technology matters far more than the nature of the technology itself. The idea that technology is a ‘neutral tool’ has a long pedigree and often accompanies exhortations to embrace edtech in one form or another (see for example Fox, 2001). It is an idea that is supported by no less a luminary than Chomsky, who, in a 2012 video entitled ‘The Purpose of Education’ (Chomsky, 2012), said that:

As far as […] technology […] and education is concerned, technology is basically neutral. It’s kind of like a hammer. I mean, […] the hammer doesn’t care whether you use it to build a house or whether a torturer uses it to crush somebody’s skull; a hammer can do either. The same with the modern technology; say, the Internet, and so on.

Womans hammerAlthough hammers are not usually classic examples of educational technology, they are worthy of a short discussion. Hammers come in all shapes and sizes and when you choose one, you need to consider its head weight (usually between 16 and 20 ounces), the length of the handle, the shape of the grip, etc. Appropriate specifications for particular hammering tasks have been calculated in great detail. The data on which these specifications is based on an analysis of the hand size and upper body strength of the typical user. The typical user is a man, and the typical hammer has been designed for a man. The average male hand length is 177.9 mm, that of the average woman is 10 mm shorter (Wang & Cai, 2017). Women typically have about half the upper body strength of men (Miller et al., 1993). It’s possible, but not easy to find hammers designed for women (they are referred to as ‘Ladies hammers’ on Amazon). They have a much lighter head weight, a shorter handle length, and many come in pink or floral designs. Hammers, in other words, are far from neutral: they are highly gendered.

Moving closer to educational purposes and ways in which we might ‘get it right with edtech’, it is useful to look at the smart phone. The average size of these devices has risen in recent years, and is now 5.5 inches, with the market for 6 inch screens growing fast. Why is this an issue? Well, as Caroline Criado Perez (2019: 159) notes, ‘while we’re all admittedly impressed by the size of your screen, it’s a slightly different matter when it comes to fitting into half the population’s hands. The average man can fairly comfortably use his device one-handed – but the average woman’s hand is not much bigger than the handset itself’. This is despite the fact the fact that women are more likely to own an iPhone than men  .

It is not, of course, just technological artefacts that are gendered. Voice-recognition software is also very biased. One researcher (Tatman, 2017) has found that Google’s speech recognition tool is 13% more accurate for men than it is for women. There are also significant biases for race and social class. The reason lies in the dataset that the tool is trained on: the algorithms may be gender- and socio-culturally-neutral, but the dataset is not. It would not be difficult to redress this bias by training the tool on a different dataset.

The same bias can be found in automatic translation software. Because corpora such as the BNC or COCA have twice as many male pronouns as female ones (as a result of the kinds of text that are selected for the corpora), translation software reflects the bias. With Google Translate, a sentence in a language with a gender-neutral pronoun, such as ‘S/he is a doctor’ is rendered into English as ‘He is a doctor’. Meanwhile, ‘S/he is a nurse’ is translated as ‘She is a nurse’ (Criado Perez, 2019: 166).

Datasets, then, are often very far from neutral. Algorithms are not necessarily any more neutral than the datasets, and Cathy O’Neil’s best-seller ‘Weapons of Math Destruction’ catalogues the many, many ways in which algorithms, posing as neutral mathematical tools, can increase racial, social and gender inequalities.

It would not be hard to provide many more examples, but the selection above is probably enough. Technology, as Langdon Winner (Winner, 1980) observed almost forty years ago, is ‘deeply interwoven in the conditions of modern politics’. Technology cannot be neutral: it has politics.

So far, I have focused primarily on the non-neutrality of technology in terms of gender (and, in passing, race and class). Before returning to broader societal issues, I would like to make a relatively brief mention of another kind of non-neutrality: the pedagogic. Language learning materials necessarily contain content of some kind: texts, topics, the choice of values or role models, language examples, and so on. These cannot be value-free. In the early days of educational computer software, one researcher (Biraimah, 1993) found that it was ‘at least, if not more, biased than the printed page it may one day replace’. My own impression is that this remains true today.

Equally interesting to my mind is the fact that all educational technologies, ranging from the writing slate to the blackboard (see Buzbee, 2014), from the overhead projector to the interactive whiteboard, always privilege a particular kind of teaching (and learning). ‘Technologies are inherently biased because they are built to accomplish certain very specific goals which means that some technologies are good for some tasks while not so good for other tasks’ (Zhao et al., 2004: 25). Digital flashcards, for example, inevitably encourage a focus on rote learning. Contemporary LMSs have impressive multi-functionality (i.e. they often could be used in a very wide variety of ways), but, in practice, most teachers use them in very conservative ways (Laanpere et al., 2004). This may be a result of teacher and institutional preferences, but it is almost certainly due, at least in part, to the way that LMSs are designed. They are usually ‘based on traditional approaches to instruction dating from the nineteenth century: presentation and assessment [and] this can be seen in the selection of features which are most accessible in the interface, and easiest to use’ (Lane, 2009).

The argument that educational technology is neutral because it could be put to many different uses, good or bad, is problematic because the likelihood of one particular use is usually much greater than another. There is, however, another way of looking at technological neutrality, and that is to look at its origins. Elsewhere on this blog, in post after post, I have given examples of the ways in which educational technology has been developed, marketed and sold primarily for commercial purposes. Educational values, if indeed there are any, are often an afterthought. The research literature in this area is rich and growing: Stephen Ball, Larry Cuban, Neil Selwyn, Joel Spring, Audrey Watters, etc.

Rather than revisit old ground here, this is an opportunity to look at a slightly different origin of educational technology: the US military. The close connection of the early history of the internet and the Advanced Research Projects Agency (now DARPA) of the United States Department of Defense is fairly well-known. Much less well-known are the very close connections between the US military and educational technologies, which are catalogued in the recently reissued ‘The Classroom Arsenal’ by Douglas D. Noble.

Following the twin shocks of the Soviet Sputnik 1 (in 1957) and Yuri Gagarin (in 1961), the United States launched a massive programme of investment in the development of high-tech weaponry. This included ‘computer systems design, time-sharing, graphics displays, conversational programming languages, heuristic problem-solving, artificial intelligence, and cognitive science’ (Noble, 1991: 55), all of which are now crucial components in educational technology. But it also quickly became clear that more sophisticated weapons required much better trained operators, hence the US military’s huge (and continuing) interest in training. Early interest focused on teaching machines and programmed instruction (branches of the US military were by far the biggest purchasers of programmed instruction products). It was essential that training was effective and efficient, and this led to a wide interest in the mathematical modelling of learning and instruction.

What was then called computer-based education (CBE) was developed as a response to military needs. The first experiments in computer-based training took place at the Systems Research Laboratory of the Air Force’s RAND Corporation think tank (Noble, 1991: 73). Research and development in this area accelerated in the 1960s and 1970s and CBE (which has morphed into the platforms of today) ‘assumed particular forms because of the historical, contingent, military contexts for which and within which it was developed’ (Noble, 1991: 83). It is possible to imagine computer-based education having developed in very different directions. Between the 1960s and 1980s, for example, the PLATO (Programmed Logic for Automatic Teaching Operations) project at the University of Illinois focused heavily on computer-mediated social interaction (forums, message boards, email, chat rooms and multi-player games). PLATO was also significantly funded by a variety of US military agencies, but proved to be of much less interest to the generals than the work taking place in other laboratories. As Noble observes, ‘some technologies get developed while others do not, and those that do are shaped by particular interests and by the historical and political circumstances surrounding their development (Noble, 1991: 4).

According to Noble, however, the influence of the military reached far beyond the development of particular technologies. Alongside the investment in technologies, the military were the prime movers in a campaign to promote computer literacy in schools.

Computer literacy was an ideological campaign rather than an educational initiative – a campaign designed, at bottom, to render people ‘comfortable’ with the ‘inevitable’ new technologies. Its basic intent was to win the reluctant acquiescence of an entire population in a brave new world sculpted in silicon.

The computer campaign also succeeded in getting people in front of that screen and used to having computers around; it made people ‘computer-friendly’, just as computers were being rendered ‘used-friendly’. It also managed to distract the population, suddenly propelled by the urgency of learning about computers, from learning about other things, such as how computers were being used to erode the quality of their working lives, or why they, supposedly the citizens of a democracy, had no say in technological decisions that were determining the shape of their own futures.

Third, it made possible the successful introduction of millions of computers into schools, factories and offices, even homes, with minimal resistance. The nation’s public schools have by now spent over two billion dollars on over a million and a half computers, and this trend still shows no signs of abating. At this time, schools continue to spend one-fifth as much on computers, software, training and staffing as they do on all books and other instructional materials combined. Yet the impact of this enormous expenditure is a stockpile of often idle machines, typically used for quite unimaginative educational applications. Furthermore, the accumulated results of three decades of research on the effectiveness of computer-based instruction remain ‘inconclusive and often contradictory’. (Noble, 1991: x – xi)

Rather than being neutral in any way, it seems more reasonable to argue, along with (I think) most contemporary researchers, that edtech is profoundly value-laden because it has the potential to (i) influence certain values in students; (ii) change educational values in [various] ways; and (iii) change national values (Omotoyinbo & Omotoyinbo, 2016: 173). Most importantly, the growth in the use of educational technology has been accompanied by a change in the way that education itself is viewed: ‘as a tool, a sophisticated supply system of human cognitive resources, in the service of a computerized, technology-driven economy’ (Noble, 1991: 1). These two trends are inextricably linked.

References

Biraimah, K. 1993. The non-neutrality of educational computer software. Computers and Education 20 / 4: 283 – 290

Buzbee, L. 2014. Blackboard: A Personal History of the Classroom. Minneapolis: Graywolf Press

Chomsky, N. 2012. The Purpose of Education (video). Learning Without Frontiers Conference. https://www.youtube.com/watch?v=DdNAUJWJN08

Criado Perez, C. 2019. Invisible Women. London: Chatto & Windus

Fox, R. 2001. Technological neutrality and practice in higher education. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://clt.curtin.edu.au/events/conferences/tlf/tlf2001/fox.html

Laanpere, M., Poldoja, H. & Kikkas, K. 2004. The second thoughts about pedagogical neutrality of LMS. Proceedings of IEEE International Conference on Advanced Learning Technologies, 2004. https://ieeexplore.ieee.org/abstract/document/1357664

Lane, L. 2009. Insidious pedagogy: How course management systems impact teaching. First Monday, 14(10). https://firstmonday.org/ojs/index.php/fm/article/view/2530/2303Lane

Miller, A.E., MacDougall, J.D., Tarnopolsky, M. A. & Sale, D.G. 1993. ‘Gender differences in strength and muscle fiber characteristics’ European Journal of Applied Physiology and Occupational Physiology. 66(3): 254-62 https://www.ncbi.nlm.nih.gov/pubmed/8477683

Noble, D. D. 1991. The Classroom Arsenal. Abingdon, Oxon.: Routledge

Omotoyinbo, D. W. & Omotoyinbo, F. R. 2016. Educational Technology and Value Neutrality. Societal Studies, 8 / 2: 163 – 179 https://www3.mruni.eu/ojs/societal-studies/article/view/4652/4276

O’Neil, C. 2016. Weapons of Math Destruction. London: Penguin

Sundström, P. Interpreting the Notion that Technology is Value Neutral. Medicine, Health Care and Philosophy 1, 1998: 42-44

Tatman, R. 2017. ‘Gender and Dialect Bias in YouTube’s Automatic Captions’ Proceedings of the First Workshop on Ethics in Natural Language Processing, pp. 53–59 http://www.ethicsinnlp.org/workshop/pdf/EthNLP06.pdf

Wang, C. & Cai, D. 2017. ‘Hand tool handle design based on hand measurements’ MATEC Web of Conferences 119, 01044 (2017) https://www.matec-conferences.org/articles/matecconf/pdf/2017/33/matecconf_imeti2017_01044.pdf

Winner, L. 1980. Do Artifacts have Politics? Daedalus 109 / 1: 121 – 136

Zhao, Y, Alvarez-Torres, M. J., Smith, B. & Tan, H. S. 2004. The Non-neutrality of Technology: a Theoretical Analysis and Empirical Study of Computer Mediated Communication Technologies. Journal of Educational Computing Research 30 (1 &2): 23 – 55

When the startup, AltSchool, was founded in 2013 by Max Ventilla, the former head of personalization at Google, it quickly drew the attention of venture capitalists and within a few years had raised $174 million from the likes of the Zuckerberg Foundation, Peter Thiel, Laurene Powell Jobs and Pierre Omidyar. It garnered gushing articles in a fawning edtech press which enthused about ‘how successful students can be when they learn in small, personalized communities that champion project-based learning, guided by educators who get a say in the technology they use’. It promised ‘a personalized learning approach that would far surpass the standardized education most kids receive’.

altschoolVentilla was an impressive money-raiser who used, and appeared to believe, every cliché in the edTech sales manual. Dressed in regulation jeans, polo shirt and fleece, he claimed that schools in America were ‘stuck in an industrial-age model, [which] has been in steady decline for the last century’ . What he offered, instead, was a learner-centred, project-based curriculum providing real-world lessons. There was a focus on social-emotional learning activities and critical thinking was vital.

The key to the approach was technology. From the start, software developers, engineers and researchers worked alongside teachers everyday, ‘constantly tweaking the Personalized Learning Plan, which shows students their assignments for each day and helps teachers keep track of and assess student’s learning’. There were tablets for pre-schoolers, laptops for older kids and wall-mounted cameras to record the lessons. There were, of course, Khan Academy videos. Ventilla explained that “we start with a representation of each child”, and even though “the vast majority of the learning should happen non-digitally”, the child’s habits and preferences gets converted into data, “a digital representation of the important things that relate to that child’s learning, not just their academic learning but also their non-academic learning. Everything logistic that goes into setting up the experience for them, whether it’s who has permission to pick them up or their allergy information. You name it.” And just like Netflix matches us to TV shows, “If you have that accurate and actionable representation for each child, now you can start to personalize the whole experience for that child. You can create that kind of loop you described where because we can represent a child well, we can match them to the right experiences.”

AltSchool seemed to offer the possibility of doing something noble, of transforming education, ‘bringing it into the digital age’, and, at the same time, a healthy return on investors’ money. Expanding rapidly, nine AltSchool microschools were opened in New York and the Bay Area, and plans were afoot for further expansion in Chicago. But, by then, it was already clear that something was going wrong. Five of the schools were closed before they had really got started and the attrition rate in some classrooms had reached about 30%. Revenue in 2018 was only $7 million and there were few buyers for the AltSchool platform. Quoting once more from the edTech bible, Ventilla explained the situation: ‘Our whole strategy is to spend more than we make,’ he says. Since software is expensive to develop and cheap to distribute, the losses, he believes, will turn into steep profits once AltSchool refines its product and lands enough customers.

The problems were many and apparent. Some of the buildings were simply not appropriate for schools, with no playgrounds or gyms, malfunctioning toilets, among other issues. Parents were becoming unhappy and accused AltSchool of putting ‘its ambitions as a tech company above its responsibility to teach their children. […] We kind of came to the conclusion that, really, AltSchool as a school was kind of a front for what Max really wants to do, which is develop software that he’s selling,’ a parent of a former AltSchool student told Business Insider. ‘We had really mediocre educators using technology as a crutch,’ said one father who transferred his child to a different private school after two years at AltSchool. […] We learned that it’s almost impossible to really customize the learning experience for each kid.’ Some parents began to wonder whether AltSchool had enticed families into its program merely to extract data from their children, then toss them aside?

With the benefit of hindsight, it would seem that the accusations were hardly unfair. In June of this year, AltSchool announced that its four remaining schools would be operated by a new partner, Higher Ground Education (a well-funded startup founded in 2016 which promotes and ‘modernises’ Montessori education). Meanwhile, AltSchool has been rebranded as Altitude Learning, focusing its ‘resources on the development and expansion of its personalized learning platform’ for licensing to other schools across the country.

Quoting once more from the edTech sales manual, Ventilla has said that education should drive the tech, not the other way round. Not so many years earlier, before starting AltSchool, Ventilla also said that he had read two dozen books on education and emerged a fan of Sir Ken Robinson. He had no experience as a teacher or as an educational administrator. Instead, he had ‘extensive knowledge of networks, and he understood the kinds of insights that can be gleaned from big data’.

Jargon buster

Posted: January 18, 2019 in Discourse, ed tech
Tags:

With the 2019 educational conference show season about to start, here’s a handy guide to gaining a REAL understanding of the words you’re likely to come across. Please feel free to add in the comments anything I’ve omitted.

iatefl conference

accountability

Keeping the money-people happy.

AI (artificial intelligence)

Ooh! Aah! Yes, please.

analytics (as in learning analytics)

The analysis of student data to reveal crucial insights such as the fact that students who work more, make more progress. Cf. data

AR (augmented reality)

Out-of-date interactive technology with no convincing classroom value. cf. interactive

benchmark

A word for standard that makes you sound like you know what you’re talking about.

blended (as in blended learning)

Homework. Or, if you want to sound more knowledgeable, the way e-learning is being combined with traditional classroom methods and independent study to create a new, hybrid teaching methodology that is shown by research to facilitate better learning outcomes.

bot

A non-unionized, cheap teacher for the masses.

brain-friendly

A word used by people who haven’t read enough neuro-science.

collaborative

Getting other people to help you, and getting praised for doing so.

CPD (continuous professional development)

Unpaid training.

creativity

A good excuse to get out your guitar, recite a few poems and show how sensitive you are. Cf. 21st century skills

curated (as in curated learning content)

Stuff nicked from other websites. A way of getting more personalization for less investment.

customer

The correct way to refer to students. Cf. markets

data

Information about students that can be sold to advertising companies.

design (as in learning design)

Used to mean curriculum by people selling edtech products who aren’t sure what curriculum means.

discovery learning

A myth with a long-gone expiry date.

disruptive (as in disruptive innovation in education)

A word used in utter seriousness by people who dream of getting rich from the privatisation of education.

drones

Handy for speaking and writing exercises, according to elearningindustry.com. They open up a new set of opportunities to make classes more relevant and engaging for students. They can in fact enrich students’ imagination and get them more involved into the learning process.

ecosystem (as in learning ecosystem)

All the different ways that data about learners can be captured, sold or hacked.

EdSurge

The go-to site for ‘news’ about edtech. The company’s goal is ‘to promote the smart adoption of education technology through impartial reporting’ … much of which is paid for by investors in edtech start-ups.

edutainment

PowerPoint, for example.

efficacy

A fancy word for efficiency that nobody bothers with much any more.

empowerment

Not connected to power in any way at all.

engagement

Sticking with something.

flipped (as in flipped classrooms)

Watching educational videos at home.

formative assessment

A critically important tool in the iterative process of maximizing the learning environment and customizing instruction to meet students’ needs. Also known as testing.

gamification

Persuading people to push buttons.

global citizens

Nice people.

immersive

Used to describe a learning activity that is less boring than other learning activities.

inclusive (as in inclusive practices)

Not to be confused with virtue-signalling.

innovative

A meaningless word that sounds good to some people. Interchangeable with cutting-edge and state-of-the-art

interactive

With buttons that can be pushed.

interactive whiteboard

A term you won’t hear this year, except when accompanied with a scoff, because everyone has forgotten it and wants to move on. Cf. 60% of the other terms in this glossary by 2025

(the) knowledge economy

Platform capitalism.

leadership

A smokescreen for poor pay and conditions. Cf. 21st century skills

literacy (as in critical literacy, digital literacy, emotional literacy, media literacy, visual literacy)

A jargon word used to mean that someone can do something.

MALL (Mobile assisted language learning)

Chatting or playing games with your phone in class.

markets

Another contemporary way of referring to students. Cf. customer

mediation

Translating, interpreting and things like that.

mindfulness

An ever-growing industry.

motivation

U.S. education technology companies raised $1.45 billion from venture capitalists and private-equity investors in 2018 (according to EdSurge).

outcomes (as in learning outcomes)

‘Learning’, or whatever, that can be measured.

personalized

A meaningless word useful for selling edtech stuff. Interchangeable with differentiated and individualized.

providers

A euphemism for sellers. Cf. solutions

publisher

An obsolete word for providers of educational learning solutions. Cf. solutions

quality

A bit of management jargon from the last century. It doesn’t really matter if you don’t know exactly what it means – you can define it yourself.

research

A slippery word that is meant to elicit a positive response.

resilience

Also known as grit, the ability to suspend your better judgment and plough on.

scaffolding

Something to do with Vygotsky, but it probably doesn’t matter what exactly. It’s a ‘good thing’.

SEL (Social-Emotional Learning)

A VA (value-added) experience needed by students who spend too long in CAL in a VLE with poor UX.

skills (as in 21st century skills)

The abilities that young people will need for an imagined future workplace. These are to be paid for by the state, rather than the companies that might employ a small number of them on zero-hour contracts.

soft skills

Everything you need to be a compliant employee.

solutions (as in learning solutions)

A euphemism for stuff that someone is trying to sell to schools.

teacherpreneur

A teacher in need of a reality check.

thought leaders (as in educational thought leaders)

Effective self-promoters, usually with no background in education.

transformative

Nothing to do with Transformative Learning Theory (Mezirow) … just another buzz word.

VR

Technology that makes you dizzy.