Posts Tagged ‘teaching machines’

ltsigIt’s hype time again. Spurred on, no doubt, by the current spate of books and articles  about AIED (artificial intelligence in education), the IATEFL Learning Technologies SIG is organising an online event on the topic in November of this year. Currently, the most visible online references to AI in language learning are related to Glossika , basically a language learning system that uses spaced repetition, whose marketing department has realised that references to AI might help sell the product. GlossikaThey’re not alone – see, for example, Knowble which I reviewed earlier this year .

In the wider world of education, where AI has made greater inroads than in language teaching, every day brings more stuff: How artificial intelligence is changing teaching , 32 Ways AI is Improving Education , How artificial intelligence could help teachers do a better job , etc., etc. There’s a full-length book by Anthony Seldon, The Fourth Education Revolution: will artificial intelligence liberate or infantilise humanity? (2018, University of Buckingham Press) – one of the most poorly researched and badly edited books on education I’ve ever read, although that won’t stop it selling – and, no surprises here, there’s a Pearson commissioned report called Intelligence Unleashed: An argument for AI in Education (2016) which is available free.

Common to all these publications is the claim that AI will radically change education. When it comes to language teaching, a similar claim has been made by Donald Clark (described by Anthony Seldon as an education guru but perhaps best-known to many in ELT for his demolition of Sugata Mitra). In 2017, Clark wrote a blog post for Cambridge English (now unavailable) entitled How AI will reboot language learning, and a more recent version of this post, called AI has and will change language learning forever (sic) is available on Clark’s own blog. Given the history of the failure of education predictions, Clark is making bold claims. Thomas Edison (1922) believed that movies would revolutionize education. Radios were similarly hyped in the 1940s and in the 1960s it was the turn of TV. In the 1980s, Seymour Papert predicted the end of schools – ‘the computer will blow up the school’, he wrote. Twenty years later, we had the interactive possibilities of Web 2.0. As each technology failed to deliver on the hype, a new generation of enthusiasts found something else to make predictions about.

But is Donald Clark onto something? Developments in AI and computational linguistics have recently resulted in enormous progress in machine translation. Impressive advances in automatic speech recognition and generation, coupled with the power that can be packed into a handheld device, mean that we can expect some re-evaluation of the value of learning another language. Stephen Heppell, a specialist at Bournemouth University in the use of ICT in Education, has said: ‘Simultaneous translation is coming, making language teachers redundant. Modern languages teaching in future may be more about navigating cultural differences’ (quoted by Seldon, p.263). Well, maybe, but this is not Clark’s main interest.

Less a matter of opinion and much closer to the present day is the issue of assessment. AI is becoming ubiquitous in language testing. Cambridge, Pearson, TELC, Babbel and Duolingo are all using or exploring AI in their testing software, and we can expect to see this increase. Current, paper-based systems of testing subject knowledge are, according to Rosemary Luckin and Kristen Weatherby, outdated, ineffective, time-consuming, the cause of great anxiety and can easily be automated (Luckin, R. & Weatherby, K. 2018. ‘Learning analytics, artificial intelligence and the process of assessment’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.253). By capturing data of various kinds throughout a language learner’s course of study and by using AI to analyse learning development, continuous formative assessment becomes possible in ways that were previously unimaginable. ‘Assessment for Learning (AfL)’ or ‘Learning Oriented Assessment (LOA)’ are two terms used by Cambridge English to refer to the potential that AI offers which is described by Luckin (who is also one of the authors of the Pearson paper mentioned earlier). In practical terms, albeit in a still very limited way, this can be seen in the CUP course ‘Empower’, which combines CUP course content with validated LOA from Cambridge Assessment English.

Will this reboot or revolutionise language teaching? Probably not and here’s why. AIED systems need to operate with what is called a ‘domain knowledge model’. This specifies what is to be learnt and includes an analysis of the steps that must be taken to reach that learning goal. Some subjects (especially STEM subjects) ‘lend themselves much more readily to having their domains represented in ways that can be automatically reasoned about’ (du Boulay, D. et al., 2018. ‘Artificial intelligences and big data technologies to close the achievement gap’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.258). This is why most AIED systems have been built to teach these areas. Language are rather different. We simply do not have a domain knowledge model, except perhaps for the very lowest levels of language learning (and even that is highly questionable). Language learning is probably not, or not primarily, about acquiring subject knowledge. Debate still rages about the relationship between explicit language knowledge and language competence. AI-driven formative assessment will likely focus most on explicit language knowledge, as does most current language teaching. This will not reboot or revolutionise anything. It will more likely reinforce what is already happening: a model of language learning that assumes there is a strong interface between explicit knowledge and language competence. It is not a model that is shared by most SLA researchers.

So, one thing that AI can do (and is doing) for language learning is to improve the algorithms that determine the way that grammar and vocabulary are presented to individual learners in online programs. AI-optimised delivery of ‘English Grammar in Use’ may lead to some learning gains, but they are unlikely to be significant. It is not, in any case, what language learners need.

AI, Donald Clark suggests, can offer personalised learning. Precisely what kind of personalised learning this might be, and whether or not this is a good thing, remains unclear. A 2015 report funded by the Gates Foundation found that we currently lack evidence about the effectiveness of personalised learning. We do not know which aspects of personalised learning (learner autonomy, individualised learning pathways and instructional approaches, etc.) or which combinations of these will lead to gains in language learning. The complexity of the issues means that we may never have a satisfactory explanation. You can read my own exploration of the problems of personalised learning starting here .

What’s left? Clark suggests that chatbots are one area with ‘huge potential’. I beg to differ and I explained my reasons eighteen months ago . Chatbots work fine in very specific domains. As Clark says, they can be used for ‘controlled practice’, but ‘controlled practice’ means practice of specific language knowledge, the practice of limited conversational routines, for example. It could certainly be useful, but more than that? Taking things a stage further, Clark then suggests more holistic speaking and listening practice with Amazon Echo, Alexa or Google Home. If and when the day comes that we have general, as opposed to domain-specific, AI, chatting with one of these tools would open up vast new possibilities. Unfortunately, general AI does not exist, and until then Alexa and co will remain a poor substitute for human-human interaction (which is readily available online, anyway). Incidentally, AI could be used to form groups of online language learners to carry out communicative tasks – ‘the aim might be to design a grouping of students all at a similar cognitive level and of similar interests, or one where the participants bring different but complementary knowledge and skills’ (Luckin, R., Holmes, W., Griffiths, M. & Forceir, L.B. 2016. Intelligence Unleashed: An argument for AI in Education. London: Pearson, p.26).

Predictions about the impact of technology on education have a tendency to be made by people with a vested interest in the technologies. Edison was a businessman who had invested heavily in motion pictures. Donald Clark is an edtech entrepreneur whose company, Wildfire, uses AI in online learning programs. Stephen Heppell is executive chairman of LP+ who are currently developing a Chinese language learning community for 20 million Chinese school students. The reporting of AIED is almost invariably in websites that are paid for, in one way or another, by edtech companies. Predictions need, therefore, to be treated sceptically. Indeed, the safest prediction we can make about hyped educational technologies is that inflated expectations will be followed by disillusionment, before the technology finds a smaller niche.

 

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Introduction

Allowing learners to determine the amount of time they spend studying, and, therefore (in theory at least) the speed of their progress is a key feature of most personalized learning programs. In cases where learners follow a linear path of pre-determined learning items, it is often the only element of personalization that the programs offer. In the Duolingo program that I am using, there are basically only two things that can be personalized: the amount of time I spend studying each day, and the possibility of jumping a number of learning items by ‘testing out’.

Self-regulated learning or self-pacing, as this is commonly referred to, has enormous intuitive appeal. It is clear that different people learn different things at different rates. We’ve known for a long time that ‘the developmental stages of child growth and the individual differences among learners make it impossible to impose a single and ‘correct’ sequence on all curricula’ (Stern, 1983: 439). It therefore follows that it makes even less sense for a group of students (typically determined by age) to be obliged to follow the same curriculum at the same pace in a one-size-fits-all approach. We have probably all experienced, as students, the frustration of being behind, or ahead of, the rest of our colleagues in a class. One student who suffered from the lockstep approach was Sal Khan, founder of the Khan Academy. He has described how he was fed up with having to follow an educational path dictated by his age and how, as a result, individual pacing became an important element in his educational approach (Ferster, 2014: 132-133). As teachers, we have all experienced the challenges of teaching a piece of material that is too hard or too easy for many of the students in the class.

Historical attempts to facilitate self-paced learning

Charles_W__Eliot_cph_3a02149An interest in self-paced learning can be traced back to the growth of mass schooling and age-graded classes in the 19th century. In fact, the ‘factory model’ of education has never existed without critics who saw the inherent problems of imposing uniformity on groups of individuals. These critics were not marginal characters. Charles Eliot (president of Harvard from 1869 – 1909), for example, described uniformity as ‘the curse of American schools’ and argued that ‘the process of instructing students in large groups is a quite sufficient school evil without clinging to its twin evil, an inflexible program of studies’ (Grittner, 1975: 324).

Attempts to develop practical solutions were not uncommon and these are reasonably well-documented. One of the earliest, which ran from 1884 to 1894, was launched in Pueblo, Colorado and was ‘a self-paced plan that required each student to complete a sequence of lessons on an individual basis’ (Januszewski, 2001: 58-59). More ambitious was the Burk Plan (at its peak between 1912 and 1915), named after Frederick Burk of the San Francisco State Normal School, which aimed to allow students to progress through materials (including language instruction materials) at their own pace with only a limited amount of teacher presentations (Januszewski, ibid.). Then, there was the Winnetka Plan (1920s), developed by Carlton Washburne, an associate of Frederick Burk and the superintendent of public schools in Winnetka, Illinois, which also ‘allowed learners to proceed at different rates, but also recognised that learners proceed at different rates in different subjects’ (Saettler, 1990: 65). The Winnetka Plan is especially interesting in the way it presaged contemporary attempts to facilitate individualized, self-paced learning. It was described by its developers in the following terms:

A general technique [consisting] of (a) breaking up the common essentials curriculum into very definite units of achievement, (b) using complete diagnostic tests to determine whether a child has mastered each of these units, and, if not, just where his difficulties lie and, (c) the full use of self-instructive, self corrective practice materials. (Washburne, C., Vogel, M. & W.S. Gray. 1926. A Survey of the Winnetka Public Schools. Bloomington, IL: Public School Press)

Not dissimilar was the Dalton (Massachusetts) Plan in the 1920s which also used a self-paced program to accommodate the different ability levels of the children and deployed contractual agreements between students and teachers (something that remains common educational practice around the world). There were many others, both in the U.S. and other parts of the world.

The personalization of learning through self-pacing was not, therefore, a minor interest. Between 1910 and 1924, nearly 500 articles can be documented on the subject of individualization (Grittner, 1975: 328). In just three years (1929 – 1932) of one publication, The Education Digest, there were fifty-one articles dealing with individual instruction and sixty-three entries treating individual differences (Chastain, 1975: 334). Foreign language teaching did not feature significantly in these early attempts to facilitate self-pacing, but see the Burk Plan described above. Only a handful of references to language learning and self-pacing appeared in articles between 1916 and 1924 (Grittner, 1975: 328).

Disappointingly, none of these initiatives lasted long. Both costs and management issues had been significantly underestimated. Plans such as those described above were seen as progress, but not the hoped-for solution. Problems included the fact that the materials themselves were not individualized and instructional methods were too rigid (Pendleton, 1930: 199). However, concomitant with the interest in individualization (mostly, self-pacing), came the advent of educational technology.

Sidney L. Pressey, the inventor of what was arguably the first teaching machine, was inspired by his experiences with schoolchildren in rural Indiana in the 1920s where he ‘was struck by the tremendous variation in their academic abilities and how they were forced to progress together at a slow, lockstep pace that did not serve all students well’ (Ferster, 2014: 52). Although Pressey failed in his attempts to promote his teaching machines, he laid the foundation stones in the synthesizing of individualization and technology.Pressey machine

Pressey may be seen as the direct precursor of programmed instruction, now closely associated with B. F. Skinner (see my post on Behaviourism and Adaptive Learning). It is a quintessentially self-paced approach and is described by John Hattie as follows:

Programmed instruction is a teaching method of presenting new subject matter to students in graded sequence of controlled steps. A book version, for example, presents a problem or issue, then, depending on the student’s answer to a question about the material, the student chooses from optional answers which refers them to particular pages of the book to find out why they were correct or incorrect – and then proceed to the next part of the problem or issue. (Hattie, 2009: 231)

Programmed instruction was mostly used for the teaching of mathematics, but it is estimated that 4% of programmed instruction programs were for foreign languages (Saettler, 1990: 297). It flourished in the 1960s and 1970s, but even by 1968 foreign language instructors were sceptical (Valdman, 1968). A survey carried out by the Center for Applied Linguistics revealed then that only about 10% of foreign language teachers at college and university reported the use of programmed materials in their departments. (Valdman, 1968: 1).grolier min max

Research studies had failed to demonstrate the effectiveness of programmed instruction (Saettler, 1990: 303). Teachers were often resistant and students were often bored, finding ‘ingenious ways to circumvent the program, including the destruction of their teaching machines!’ (Saettler, ibid.).

In the case of language learning, there were other problems. For programmed instruction to have any chance of working, it was necessary to specify rigorously the initial and terminal behaviours of the learner so that the intermediate steps leading from the former to the latter could be programmed. As Valdman (1968: 4) pointed out, this is highly problematic when it comes to languages (a point that I have made repeatedly in this blog). In addition, students missed the personal interaction that conventional instruction offered, got bored and lacked motivation (Valdman, 1968: 10).

Programmed instruction worked best when teachers were very enthusiastic, but perhaps the most significant lesson to be learned from the experiments was that it was ‘a difficult, time-consuming task to introduce programmed instruction’ (Saettler, 1990: 299). It entailed changes to well-established practices and attitudes, and for such changes to succeed there must be consideration of the social, political, and economic contexts. As Saettler (1990: 306), notes, ‘without the support of the community and the entire teaching staff, sustained innovation is unlikely’. In this light, Hattie’s research finding that ‘when comparisons are made between many methods, programmed instruction often comes near the bottom’ (Hattie, 2009: 231) comes as no great surprise.

Just as programmed instruction was in its death throes, the world of language teaching discovered individualization. Launched as a deliberate movement in the early 1970s at the Stanford Conference (Altman & Politzer, 1971), it was a ‘systematic attempt to allow for individual differences in language learning’ (Stern, 1983: 387). Inspired, in part, by the work of Carl Rogers, this ‘humanistic turn’ was a recognition that ‘each learner is unique in personality, abilities, and needs. Education must be personalized to fit the individual; the individual must not be dehumanized in order to meet the needs of an impersonal school system’ (Disick, 1975:38). In ELT, this movement found many adherents and remains extremely influential to this day.

In language teaching more generally, the movement lost impetus after a few years, ‘probably because its advocates had underestimated the magnitude of the task they had set themselves in trying to match individual learner characteristics with appropriate teaching techniques’ (Stern, 1983: 387). What precisely was meant by individualization was never adequately defined or agreed (a problem that remains to the present time). What was left was self-pacing. In 1975, it was reported that ‘to date the majority of the programs in second-language education have been characterized by a self-pacing format […]. Practice seems to indicate that ‘individualized’ instruction is being defined in the class room as students studying individually’ (Chastain, 1975: 344).

Lessons to be learned

This brief account shows that historical attempts to facilitate self-pacing have largely been characterised by failure. The starting point of all these attempts remains as valid as ever, but it is clear that practical solutions are less than simple. To avoid the insanity of doing the same thing over and over again and expecting different results, we should perhaps try to learn from the past.

One of the greatest challenges that teachers face is dealing with different levels of ability in their classes. In any blended scenario where the online component has an element of self-pacing, the challenge will be magnified as ability differentials are likely to grow rather than decrease as a result of the self-pacing. Bart Simpson hit the nail on the head in a memorable line: ‘Let me get this straight. We’re behind the rest of the class and we’re going to catch up to them by going slower than they are? Coo coo!’ Self-pacing runs into immediate difficulties when it comes up against standardised tests and national or state curriculum requirements. As Ferster observes, ‘the notion of individual pacing [remains] antithetical to […] a graded classroom system, which has been the model of schools for the past century. Schools are just not equipped to deal with students who do not learn in age-processed groups, even if this system is clearly one that consistently fails its students (Ferster, 2014: 90-91).bart_simpson

Ability differences are less problematic if the teacher focusses primarily on communicative tasks in F2F time (as opposed to more teaching of language items), but this is a big ‘if’. Many teachers are unsure of how to move towards a more communicative style of teaching, not least in large classes in compulsory schooling. Since there are strong arguments that students would benefit from a more communicative, less transmission-oriented approach anyway, it makes sense to focus institutional resources on equipping teachers with the necessary skills, as well as providing support, before a shift to a blended, more self-paced approach is implemented.

Such issues are less important in private institutions, which are not age-graded, and in self-study contexts. However, even here there may be reasons to proceed cautiously before buying into self-paced approaches. Self-pacing is closely tied to autonomous goal-setting (which I will look at in more detail in another post). Both require a degree of self-awareness at a cognitive and emotional level (McMahon & Oliver, 2001), but not all students have such self-awareness (Magill, 2008). If students do not have the appropriate self-regulatory strategies and are simply left to pace themselves, there is a chance that they will ‘misregulate their learning, exerting control in a misguided or counterproductive fashion and not achieving the desired result’ (Kirschner & van Merriënboer, 2013: 177). Before launching students on a path of self-paced language study, ‘thought needs to be given to the process involved in users becoming aware of themselves and their own understandings’ (McMahon & Oliver, 2001: 1304). Without training and support provided both before and during the self-paced study, the chances of dropping out are high (as we see from the very high attrition rate in language apps).

However well-intentioned, many past attempts to facilitate self-pacing have also suffered from the poor quality of the learning materials. The focus was more on the technology of delivery, and this remains the case today, as many posts on this blog illustrate. Contemporary companies offering language learning programmes show relatively little interest in the content of the learning (take Duolingo as an example). Few app developers show signs of investing in experienced curriculum specialists or materials writers. Glossy photos, contemporary videos, good UX and clever gamification, all of which become dull and repetitive after a while, do not compensate for poorly designed materials.

Over forty years ago, a review of self-paced learning concluded that the evidence on its benefits was inconclusive (Allison, 1975: 5). Nothing has changed since. For some people, in some contexts, for some of the time, self-paced learning may work. Claims that go beyond that cannot be substantiated.

References

Allison, E. 1975. ‘Self-Paced Instruction: A Review’ The Journal of Economic Education 7 / 1: 5 – 12

Altman, H.B. & Politzer, R.L. (eds.) 1971. Individualizing Foreign Language Instruction: Proceedings of the Stanford Conference, May 6 – 8, 1971. Washington, D.C.: Office of Education, U.S. Department of Health, Education, and Welfare

Chastain, K. 1975. ‘An Examination of the Basic Assumptions of “Individualized” Instruction’ The Modern Language Journal 59 / 7: 334 – 344

Disick, R.S. 1975 Individualizing Language Instruction: Strategies and Methods. New York: Harcourt Brace Jovanovich

Ferster, B. 2014. Teaching Machines. Baltimore: John Hopkins University Press

Grittner, F. M. 1975. ‘Individualized Instruction: An Historical Perspective’ The Modern Language Journal 59 / 7: 323 – 333

Hattie, J. 2009. Visible Learning. Abingdon, Oxon.: Routledge

Januszewski, A. 2001. Educational Technology: The Development of a Concept. Englewood, Colorado: Libraries Unlimited

Kirschner, P. A. & van Merriënboer, J. J. G. 2013. ‘Do Learners Really Know Best? Urban Legends in Education’ Educational Psychologist, 48:3, 169-183

Magill, D. S. 2008. ‘What Part of Self-Paced Don’t You Understand?’ University of Wisconsin 24th Annual Conference on Distance Teaching & Learning Conference Proceedings.

McMahon, M. & Oliver, R. 2001. ‘Promoting self-regulated learning in an on-line environment’ in C. Montgomerie & J. Viteli (eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2001 (pp. 1299-1305). Chesapeake, VA: AACE

Pendleton, C. S. 1930. ‘Personalizing English Teaching’ Peabody Journal of Education 7 / 4: 195 – 200

Saettler, P. 1990. The Evolution of American Educational Technology. Denver: Libraries Unlimited

Stern, H.H. 1983. Fundamental Concepts of Language Teaching. Oxford: Oxford University Press

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

 

In ELT circles, ‘behaviourism’ is a boo word. In the standard history of approaches to language teaching (characterised as a ‘procession of methods’ by Hunter & Smith 2012: 432[1]), there were the bad old days of behaviourism until Chomsky came along, savaged the theory in his review of Skinner’s ‘Verbal Behavior’, and we were all able to see the light. In reality, of course, things weren’t quite like that. The debate between Chomsky and the behaviourists is far from over, behaviourism was not the driving force behind the development of audiolingual approaches to language teaching, and audiolingualism is far from dead. For an entertaining and eye-opening account of something much closer to reality, I would thoroughly recommend a post on Russ Mayne’s Evidence Based ELT blog, along with the discussion which follows it. For anyone who would like to understand what behaviourism is, was, and is not (before they throw the term around as an insult), I’d recommend John A. Mills’ ‘Control: A History of Behavioral Psychology’ (New York University Press, 1998) and John Staddon’s ‘The New Behaviorism 2nd edition’ (Psychology Press, 2014).

There is a close connection between behaviourism and adaptive learning. Audrey Watters, no fan of adaptive technology, suggests that ‘any company touting adaptive learning software’ has been influenced by Skinner. In a more extended piece, ‘Education Technology and Skinner’s Box, Watters explores further her problems with Skinner and the educational technology that has been inspired by behaviourism. But writers much more sympathetic to adaptive learning, also see close connections to behaviourism. ‘The development of adaptive learning systems can be considered as a transformation of teaching machines,’ write Kara & Sevim[2] (2013: 114 – 117), although they go on to point out the differences between the two. Vendors of adaptive learning products, like DreamBox Learning©, are not shy of associating themselves with behaviourism: ‘Adaptive learning has been with us for a while, with its history of adaptive learning rooted in cognitive psychology, beginning with the work of behaviorist B.F. Skinner in the 1950s, and continuing through the artificial intelligence movement of the 1970s.’

That there is a strong connection between adaptive learning and behaviourism is indisputable, but I am not interested in attempting to establish the strength of that connection. This would, in any case, be an impossible task without some reductionist definition of both terms. Instead, my interest here is to explore some of the parallels between the two, and, in the spirit of the topic, I’d like to do this by comparing the behaviours of behaviourists and adaptive learning scientists.

Data and theory

Both behaviourism and adaptive learning (in its big data form) are centrally concerned with behaviour – capturing and measuring it in an objective manner. In both, experimental observation and the collection of ‘facts’ (physical, measurable, behavioural occurrences) precede any formulation of theory. John Mills’ description of behaviourists could apply equally well to adaptive learning scientists: theory construction was a seesaw process whereby one began with crude outgrowths from observations and slowly created one’s theory in such a way that one could make more and more precise observations, building those observations into the theory at each stage. No behaviourist ever considered the possibility of taking existing comprehensive theories of mind and testing or refining them.[3]

Positivism and the panopticon

Both behaviourism and adaptive learning are pragmatically positivist, believing that truth can be established by the study of facts. J. B. Watson, the founding father of behaviourism whose article ‘Psychology as the Behaviorist Views Itset the behaviourist ball rolling, believed that experimental observation could ‘reveal everything that can be known about human beings’[4]. Jose Ferreira of Knewton has made similar claims: We get five orders of magnitude more data per user than Google does. We get more data about people than any other data company gets about people, about anything — and it’s not even close. We’re looking at what you know, what you don’t know, how you learn best. […] We know everything about what you know and how you learn best because we get so much data. Digital data analytics offer something that Watson couldn’t have imagined in his wildest dreams, but he would have approved.

happiness industryThe revolutionary science

Big data (and the adaptive learning which is a part of it) is presented as a game-changer: The era of big data challenges the way we live and interact with the world. […] Society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality[5]. But the reverence for technology and the ability to reach understandings of human beings by capturing huge amounts of behavioural data was adumbrated by Watson a century before big data became a widely used term. Watson’s 1913 lecture at Columbia University was ‘a clear pitch’[6] for the supremacy of behaviourism, and its potential as a revolutionary science.

Prediction and controlnudge

The fundamental point of both behaviourism and adaptive learning is the same. The research practices and the theorizing of American behaviourists until the mid-1950s, writes Mills[7] were driven by the intellectual imperative to create theories that could be used to make socially useful predictions. Predictions are only useful to the extent that they can be used to manipulate behaviour. Watson states this very baldly: the theoretical goal of psychology is the prediction and control of behaviour[8]. Contemporary iterations of behaviourism, such as behavioural economics or nudge theory (see, for example, Thaler & Sunstein’s best-selling ‘Nudge’, Penguin Books, 2008), or the British government’s Behavioural Insights Unit, share the same desire to divert individual activity towards goals (selected by those with power), ‘without either naked coercion or democratic deliberation’[9]. Jose Ferreira of Knewton has an identical approach: We can predict failure in advance, which means we can pre-remediate it in advance. We can say, “Oh, she’ll struggle with this, let’s go find the concept from last year’s materials that will help her not struggle with it.” Like the behaviourists, Ferreira makes grand claims about the social usefulness of his predict-and-control technology: The end is a really simple mission. Only 22% of the world finishes high school, and only 55% finish sixth grade. Those are just appalling numbers. As a species, we’re wasting almost four-fifths of the talent we produce. […] I want to solve the access problem for the human race once and for all.

Ethics

Because they rely on capturing large amounts of personal data, both behaviourism and adaptive learning quickly run into ethical problems. Even where informed consent is used, the subjects must remain partly ignorant of exactly what is being tested, or else there is the fear that they might adjust their behaviour accordingly. The goal is to minimise conscious understanding of what is going on[10]. For adaptive learning, the ethical problem is much greater because of the impossibility of ensuring the security of this data. Everything is hackable.

Marketing

Behaviourism was seen as a god-send by the world of advertising. J. B. Watson, after a front-page scandal about his affair with a student, and losing his job at John Hopkins University, quickly found employment on Madison Avenue. ‘Scientific advertising’, as practised by the Mad Men from the 1920s onwards, was based on behaviourism. The use of data analytics by Google, Amazon, et al is a direct descendant of scientific advertising, so it is richly appropriate that adaptive learning is the child of data analytics.

[1] Hunter, D. and Smith, R. (2012) ‘Unpacking the past: “CLT” through ELTJ keywords’. ELT Journal, 66/4: 430-439.

[2] Kara, N. & Sevim, N. 2013. ‘Adaptive learning systems: beyond teaching machines’, Contemporary Educational Technology, 4(2), 108-120

[3] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.5

[4] Davies, W. (2015) The Happiness Industry. London: Verso. p.91

[5] Mayer-Schönberger, V. & Cukier, K. (2013) Big Data. London: John Murray, p.7

[6] Davies, W. (2015) The Happiness Industry. London: Verso. p.87

[7] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.2

[8] Watson, J. B. (1913) ‘Behaviorism as the Psychologist Views it’ Psychological Review 20: 158

[9] Davies, W. (2015) The Happiness Industry. London: Verso. p.88

[10] Davies, W. (2015) The Happiness Industry. London: Verso. p.92

Then and now in educationThe School of Tomorrow will pay far more attention to individuals than the schools of the past. Each child will be studied and measured repeatedly from many angles, both as a basis of prescriptions for treatment and as a means of controlling development. The new education will be scientific in that it will rest on a fact basis. All development of knowledge and skill will be individualized, and classroom practice and recitation as they exist today in conventional schools will largely disappear. […] Experiments in laboratories and in schools of education [will discover] what everyone should know and the best way to learn essential elements.

This is not, you may be forgiven for thinking, from a Knewton blog post. It was written in 1924 and comes from Otis W. Caldwell & Stuart A. Courtis Then and Now in Education, 1845: 1923 (New York: Appleton) and is cited in Petrina, S. 2002. ‘Getting a Purchase on “The School of Tomorrow” and its Constituent Commodities: Histories and Historiographies of Technologies’ History of Education Quarterly, Vol. 42, No. 1 (Spring, 2002), pp. 75-111.

presseyIn the same year that Caldwell and Courtis predicted the School of Tomorrow, Sidney Pressey, ‘contrived an intelligence testing machine, which he transformed during 1924-1934 into an ‘Automatic Teacher.’ His machine automated and individualized routine classroom processes such as testing and drilling. It could reduce the burden of testing and scoring for teachers and therapeutically treat students after examination and diagnosis’ (Petrina, p. 99). Six years later, the ‘Automatic Teacher’ was recognised as a commercial failure. For more on Pressey’s machine (including a video of Pressey demonstrating it), see Audrey Watter’s excellent piece.

Caldwell, Courtis and Pressey are worth bearing in mind when you read the predictions of people like Knewton’s Jose Ferreira. Here are a few of his ‘Then and Now’ predictions:

“Online learning” will soon be known simply as “learning.” All of the world’s education content is being digitized right now, and that process will be largely complete within five years. (01.09.2010)

There will soon be lots of wonderful adaptive learning apps: adaptive quizzing apps, flashcard apps, textbook apps, simulation apps — if you can imagine it, someone will make it. In a few years, every education app will be adaptive. Everyone will be an adaptive learning app maker. (23.04.13)

Right now about 22 percent of the people in the world graduate high school or the equivalent. That’s pathetic. In one generation we could get close to 100 percent, almost for free. (19.07.13)

95% of materials (textbooks, software, etc used for classes, tutoring, corp training…) will be purely online in 5-10 years. That’s a $200B global industry. And people predict that 50% of higher ed and 25% of K-12 will eventually be purely online classes. If so, that would create a new, $3 trillion or so industry. (25.11.2013)