Posts Tagged ‘Personalization’

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’.

 

 

 

 

 

At the start of the last decade, ELT publishers were worried, Macmillan among them. The financial crash of 2008 led to serious difficulties, not least in their key Spanish market. In 2011, Macmillan’s parent company was fined ₤11.3 million for corruption. Under new ownership, restructuring was a constant. At the same time, Macmillan ELT was getting ready to move from its Oxford headquarters to new premises in London, a move which would inevitably lead to the loss of a sizable proportion of its staff. On top of that, Macmillan, like the other ELT publishers, was aware that changes in the digital landscape (the first 3G iPhone had appeared in June 2008 and wifi access was spreading rapidly around the world) meant that they needed to shift away from the old print-based model. With her finger on the pulse, Caroline Moore, wrote an article in October 2010 entitled ‘No Future? The English Language Teaching Coursebook in the Digital Age’ . The publication (at the start of the decade) and runaway success of the online ‘Touchstone’ course, from arch-rivals, Cambridge University Press, meant that Macmillan needed to change fast if they were to avoid being left behind.

Macmillan already had a platform, Campus, but it was generally recognised as being clunky and outdated, and something new was needed. In the summer of 2012, Macmillan brought in two new executives – people who could talk the ‘creative-disruption’ talk and who believed in the power of big data to shake up English language teaching and publishing. At the time, the idea of big data was beginning to reach public consciousness and ‘Big Data: A Revolution that Will Transform how We Live, Work, and Think’ by Viktor Mayer-Schönberger and Kenneth Cukier, was a major bestseller in 2013 and 2014. ‘Big data’ was the ‘hottest trend’ in technology and peaked in Google Trends in October 2014. See the graph below.

Big_data_Google_Trend

Not long after taking up their positions, the two executives began negotiations with Knewton, an American adaptive learning company. Knewton’s technology promised to gather colossal amounts of data on students using Knewton-enabled platforms. Its founder, Jose Ferreira, bragged that Knewton had ‘more data about our students than any company has about anybody else about anything […] We literally know everything about what you know and how you learn best, everything’. This data would, it was claimed, enable publishers to multiply, by orders of magnitude, the efficacy of learning materials, allowing publishers, like Macmillan, to provide a truly personalized and optimal offering to learners using their platform.

The contract between Macmillan and Knewton was agreed in May 2013 ‘to build next-generation English Language Learning and Teaching materials’. Perhaps fearful of being left behind in what was seen to be a winner-takes-all market (Pearson already had a financial stake in Knewton), Cambridge University Press duly followed suit, signing a contract with Knewton in September of the same year, in order ‘to create personalized learning experiences in [their] industry-leading ELT digital products’. Things moved fast because, by the start of 2014 when Macmillan’s new catalogue appeared, customers were told to ‘watch out for the ‘Big Tree’’, Macmillans’ new platform, which would be powered by Knewton. ‘The power that will come from this world of adaptive learning takes my breath away’, wrote the international marketing director.

Not a lot happened next, at least outwardly. In the following year, 2015, the Macmillan catalogue again told customers to ‘look out for the Big Tree’ which would offer ‘flexible blended learning models’ which could ‘give teachers much more freedom to choose what they want to do in the class and what they want the students to do online outside of the classroom’.

Macmillan_catalogue_2015

But behind the scenes, everything was going wrong. It had become clear that a linear model of language learning, which was a necessary prerequisite of the Knewton system, simply did not lend itself to anything which would be vaguely marketable in established markets. Skills development, not least the development of so-called 21st century skills, which Macmillan was pushing at the time, would not be facilitated by collecting huge amounts of data and algorithms offering personalized pathways. Even if it could, teachers weren’t ready for it, and the projections for platform adoptions were beginning to seem very over-optimistic. Costs were spiralling. Pushed to meet unrealistic deadlines for a product that was totally ill-conceived in the first place, in-house staff were suffering, and this was made worse by what many staffers thought was a toxic work environment. By the end of 2014 (so, before the copy for the 2015 catalogue had been written), the two executives had gone.

For some time previously, skeptics had been joking that Macmillan had been barking up the wrong tree, and by the time that the 2016 catalogue came out, the ‘Big Tree’ had disappeared without trace. The problem was that so much time and money had been thrown at this particular tree that not enough had been left to develop new course materials (for adults). The whole thing had been a huge cock-up of an extraordinary kind.

Cambridge, too, lost interest in their Knewton connection, but were fortunate (or wise) not to have invested so much energy in it. Language learning was only ever a small part of Knewton’s portfolio, and the company had raised over $180 million in venture capital. Its founder, Jose Ferreira, had been a master of marketing hype, but the business model was not delivering any better than the educational side of things. Pearson pulled out. In December 2016, Ferreira stepped down and was replaced as CEO. The company shifted to ‘selling digital courseware directly to higher-ed institutions and students’ but this could not stop the decline. In September of 2019, Knewton was sold for something under $17 million dollars, with investors taking a hit of over $160 million. My heart bleeds.

It was clear, from very early on (see, for example, my posts from 2014 here and here) that Knewton’s product was little more than what Michael Feldstein called ‘snake oil’. Why and how could so many people fall for it for so long? Why and how will so many people fall for it again in the coming decade, although this time it won’t be ‘big data’ that does the seduction, but AI (which kind of boils down to the same thing)? The former Macmillan executives are still at the game, albeit in new companies and talking a slightly modified talk, and Jose Ferreira (whose new venture has already raised $3.7 million) is promising to revolutionize education with a new start-up which ‘will harness the power of technology to improve both access and quality of education’ (thanks to Audrey Watters for the tip). Investors may be desperate to find places to spread their portfolio, but why do the rest of us lap up the hype? It’s a question to which I will return.

 

 

 

 

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’.

The use of big data and analytics in education continues to grow.

A vast apparatus of measurement is being developed to underpin national education systems, institutions and the actions of the individuals who occupy them. […] The presence of digital data and software in education is being amplified through massive financial and political investment in educational technologies, as well as huge growth in data collection and analysis in policymaking practices, extension of performance measurement technologies in the management of educational institutions, and rapid expansion of digital methodologies in educational research. To a significant extent, many of the ways in which classrooms function, educational policy departments and leaders make decisions, and researchers make sense of data, simply would not happen as currently intended without the presence of software code and the digital data processing programs it enacts. (Williamson, 2017: 4)

The most common and successful use of this technology so far has been in the identification of students at risk of dropping out of their courses (Jørno & Gynther, 2018: 204). The kind of analytics used in this context may be called ‘academic analytics’ and focuses on educational processes at the institutional level or higher (Gelan et al, 2018: 3). However, ‘learning analytics’, the capture and analysis of learner and learning data in order to personalize learning ‘(1) through real-time feedback on online courses and e-textbooks that can ‘learn’ from how they are used and ‘talk back’ to the teacher, and (2) individualization and personalization of the educational experience through adaptive learning systems that enable materials to be tailored to each student’s individual needs through automated real-time analysis’ (Mayer-Schönberger & Cukier, 2014) has become ‘the main keyword of data-driven education’ (Williamson, 2017: 10). See my earlier posts on this topic here and here and here.

Learning with big dataNear the start of Mayer-Schönberger and Cukier’s enthusiastic sales pitch (Learning with Big Data: The Future of Education) for the use of big data in education, there is a discussion of Duolingo. They quote Luis von Ahn, the founder of Duolingo, as saying ‘there has been little empirical work on what is the best way to teach a foreign language’. This is so far from the truth as to be laughable. Von Ahn’s comment, along with the Duolingo product itself, is merely indicative of a lack of awareness of the enormous amount of research that has been carried out. But what could the data gleaned from the interactions of millions of users with Duolingo tell us of value? The example that is given is the following. Apparently, ‘in the case of Spanish speakers learning English, it’s common to teach pronouns early on: words like “he,” “she,” and “it”.’ But, Duolingo discovered, ‘the term “it” tends to confuse and create anxiety for Spanish speakers, since the word doesn’t easily translate into their language […] Delaying the introduction of “it” until a few weeks later dramatically improves the number of people who stick with learning English rather than drop out.’ Was von Ahn unaware of the decades of research into language transfer effects? Did von Ahn (who grew up speaking Spanish in Guatemala) need all this data to tell him that English personal pronouns can cause problems for Spanish learners of English? Was von Ahn unaware of the debates concerning the value of teaching isolated words (especially grammar words!)?

The area where little empirical research has been done is not in different ways of learning another language: it is in the use of big data and learning analytics to assist language learning. Claims about the value of these technologies in language learning are almost always speculative – they are based on comparison to other school subjects (especially, mathematics). Gelan et al (2018: 2), who note this lack of research, suggest that ‘understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways’ (my italics). Reinders (2018: 81) writes ‘that analysis of prior experiences with certain groups or certain courses may help to identify key moments at which students need to receive more or different support. Analysis of student engagement and performance throughout a course may help with early identification of learning problems and may prompt early intervention’ (italics added). But there is some research out there, and it’s worth having a look at. Most studies that have collected learner-tracking data concern glossary use for reading comprehension and vocabulary retention (Gelan et al, 2018: 5), but a few have attempted to go further in scope.

Volk et al (2015) looked at the behaviour of the 20,000 students per day using the platform which accompanies ‘More!’ (Gerngross et al. 2008) to do their English homework for Austrian lower secondary schools. They discovered that

  • the exercises used least frequently were those that are located further back in the course book
  • usage is highest from Monday to Wednesday, declining from Thursday, with a rise again on Sunday
  • most interaction took place between 3:00 and 5:00 pm.
  • repetition of exercises led to a strong improvement in success rate
  • students performed better on multiple choice and matching exercises than they did where they had to produce some language

The authors of this paper conclude by saying that ‘the results of this study suggest a number of new avenues for research. In general, the authors plan to extend their analysis of exercise results and applied exercises to the population of all schools using the online learning platform more-online.at. This step enables a deeper insight into student’s learning behaviour and allows making more generalizing statements.’ When I shared these research findings with the Austrian lower secondary teachers that I work with, their reaction was one of utter disbelief. People get paid to do this research? Why not just ask us?

More useful, more actionable insights may yet come from other sources. For example, Gu Yueguo, Pro-Vice-Chancellor of the Beijing Foreign Studies University has announced the intention to set up a national Big Data research center, specializing in big data-related research topics in foreign language education (Yu, 2015). Meanwhile, I’m aware of only one big research project that has published its results. The EC Erasmus+ VITAL project (Visualisation Tools and Analytics to monitor Online Language Learning & Teaching) was carried out between 2015 and 2017 and looked at the learning trails of students from universities in Belgium, Britain and the Netherlands. It was discovered (Gelan et al, 2015) that:

  • students who did online exercises when they were supposed to do them were slightly more successful than those who were late carrying out the tasks
  • successful students logged on more often, spent more time online, attempted and completed more tasks, revisited both exercises and theory pages more frequently, did the work in the order in which it was supposed to be done and did more work in the holidays
  • most students preferred to go straight into the assessed exercises and only used the theory pages when they felt they needed to; successful students referred back to the theory pages more often than unsuccessful students
  • students made little use of the voice recording functionality
  • most online activity took place the day before a class and the day of the class itself

EU funding for this VITAL project amounted to 274,840 Euros[1]. The technology for capturing the data has been around for a long time. In my opinion, nothing of value, or at least nothing new, has been learnt. Publishers like Pearson and Cambridge University Press who have large numbers of learners using their platforms have been capturing learning data for many years. They do not publish their findings and, intriguingly, do not even claim that they have learnt anything useful / actionable from the data they have collected. Sure, an exercise here or there may need to be amended. Both teachers and students may need more support in using the more open-ended functionalities of the platforms (e.g. discussion forums). But are they getting ‘unprecedented insights into what works and what doesn’t’ (Mayer-Schönberger & Cukier, 2014)? Are they any closer to building better pedagogies? On the basis of what we know so far, you wouldn’t want to bet on it.

It may be the case that all the learning / learner data that is captured could be used in some way that has nothing to do with language learning. Show me a language-learning app developer who does not dream of monetizing the ‘behavioural surplus’ (Zuboff, 2018) that they collect! But, for the data and analytics to be of any value in guiding language learning, it must lead to actionable insights. Unfortunately, as Jørno & Gynther (2018: 198) point out, there is very little clarity about what is meant by ‘actionable insights’. There is a danger that data and analytics ‘simply gravitates towards insights that confirm longstanding good practice and insights, such as “students tend to ignore optional learning activities … [and] focus on activities that are assessed” (Jørno & Gynther, 2018: 211). While this is happening, the focus on data inevitably shapes the way we look at the object of study (i.e. language learning), ‘thereby systematically excluding other perspectives’ (Mau, 2019: 15; see also Beer, 2019). The belief that tech is always the solution, that all we need is more data and better analytics, remains very powerful: it’s called techno-chauvinism (Broussard, 2018: 7-8).

References

Beer, D. 2019. The Data Gaze. London: Sage

Broussard, M. 2018. Artificial Unintelligence. Cambridge, Mass.: MIT Press

Gelan, A., Fastre, G., Verjans, M., Martin, N., Jansenswillen, G., Creemers, M., Lieben, J., Depaire, B. & Thomas, M. 2018. ‘Affordances and limitations of learning analytics for computer­assisted language learning: a case study of the VITAL project’. Computer Assisted Language Learning. pp. 1­26. http://clok.uclan.ac.uk/21289/

Gerngross, G., Puchta, H., Holzmann, C., Stranks, J., Lewis-Jones, P. & Finnie, R. 2008. More! 1 Cyber Homework. Innsbruck, Austria: Helbling

Jørno, R. L. & Gynther, K. 2018. ‘What Constitutes an “Actionable Insight” in Learning Analytics?’ Journal of Learning Analytics 5 (3): 198 – 221

Mau, S. 2019. The Metric Society. Cambridge: Polity Press

Mayer-Schönberger, V. & Cukier, K. 2014. Learning with Big Data: The Future of Education. New York: Houghton Mifflin Harcourt

Reinders, H. 2018. ‘Learning analytics for language learning and teaching’. JALT CALL Journal 14 / 1: 77 – 86 https://files.eric.ed.gov/fulltext/EJ1177327.pdf

Volk, H., Kellner, K. & Wohlhart, D. 2015. ‘Learning Analytics for English Language Teaching.’ Journal of Universal Computer Science, Vol. 21 / 1: 156-174 http://www.jucs.org/jucs_21_1/learning_analytics_for_english/jucs_21_01_0156_0174_volk.pdf

Williamson, B. 2017. Big Data in Education. London: Sage

Yu, Q. 2015. ‘Learning Analytics: The next frontier for computer assisted language learning in big data age’ SHS Web of Conferences, 17 https://www.shs-conferences.org/articles/shsconf/pdf/2015/04/shsconf_icmetm2015_02013.pdf

Zuboff, S. 2019. The Age of Surveillance Capitalism. London: Profile Books

 

[1] See https://ec.europa.eu/programmes/erasmus-plus/sites/erasmusplus2/files/ka2-2015-he_en.pdf

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.

 

On Sunday 17 June I’ll be giving a talk at a conference in London, organised by Regent’s University and Trinity College London. Further information about the conference can be found here.

The talk is entitled ‘Personalized learning: the past, present and future of ELT’ and draws heavily on earlier posts on this blog. For anyone attending the talk, here are links to the references I cite along with further reading.

  1. Personalized learning – attempts to define it and its links to technology: see Personalized learning: Hydra and the power of ambiguity and Evaluating personalization
  2. Goal-setting and standardization: see Personalization and goal-setting
  3. Self-pacing and programmed instruction: see Self-paced language learning
  4. The promotion of personalized learning in ELT: see Personalized learning at IATEFL

 

 

It’s international ELT conference season again, with TESOL Chicago having just come to a close and IATEFL Brighton soon to start. I decided to take a look at how the subject of personalized learning will be covered at the second of these. Taking the conference programme , I trawled through looking for references to my topic.

Jing_word_cloudMy first question was: how do conference presenters feel about personalised learning? One way of finding out is by looking at the adjectives that are found in close proximity. This is what you get.

The overall enthusiasm is even clearer when the contexts are looked at more closely. Here are a few examples:

  • inspiring assessment, personalising learning
  • personalised training can contribute to professionalism and […] spark ideas for teacher trainers
  • a personalised educational experience that ultimately improves learner outcomes
  • personalised teacher development: is it achievable?

Particularly striking is the complete absence of anything that suggests that personalized learning might not be a ‘good thing’. The assumption throughout is that personalized learning is desirable and the only question that is asked is how it can be achieved. Unfortunately (and however much we might like to believe that it is a ‘good thing’), there is a serious lack of research evidence which demonstrates that this is the case. I have written about this here and here and here . For a useful summary of the current situation, see Benjamin Riley’s article where he writes that ‘it seems wise to ask what evidence we presently have that personalized learning works. Answer: Virtually none. One remarkable aspect of the personalized-learning craze is how quickly the concept has spread despite the almost total absence of rigorous research in support of it, at least thus far.’

Given that personalized learning can mean so many things and given the fact that people do not have space to define their terms in their conference abstracts, it is interesting to see what other aspects of language learning / teaching it is associated with. The four main areas are as follows (in alphabetical order):

  • assessment (especially formative assessment) / learning outcomes
  • continuous professional development
  • learner autonomy
  • technology / blended learning

The IATEFL TD SIG would appear to be one of the main promoters of personalized learning (or personalized teacher development) with a one-day pre-conference event entitled ‘Personalised teacher development – is it achievable?’ and a ‘showcase’ forum entitled ‘Forum on Effective & personalised: the holy grail of CPD’. Amusingly (but coincidentally, I suppose), the forum takes place in the ‘Cambridge room’ (see below).

I can understand why the SIG organisers may have chosen this focus. It’s something of a hot topic, and getting hotter. For example:

  • Cambridge University Press has identified personalization as one of the ‘six key principles of effective teacher development programmes’ and is offering tailor-made teacher development programmes for institutions.
  • NILE and Macmillan recently launched a partnership whose brief is to ‘curate personalised professional development with an appropriate mix of ‘formal’ and ‘informal’ learning delivered online, blended and face to face’.
  • Pearson has developed the Pearson’s Teacher Development Interactive (TDI) – ‘an interactive online course to train and certify teachers to deliver effective instruction in English as a foreign language […] You can complete each module on your own time, at your own pace from anywhere you have access to the internet.’

These examples do not, of course, provide any explanation for why personalized learning is a hot topic, but the answer to that is simple. Money. Billions and billions, and if you want a breakdown, have a look at the appendix of Monica Bulger’s report, ‘Personalized Learning: The Conversations We’re Not Having’ . Starting with Microsoft and the Gates Foundation plus Facebook and the Chan / Zuckerberg Foundation, dozens of venture philanthropists have thrown unimaginable sums of money at the idea of personalized learning. They have backed up their cash with powerful lobbying and their message has got through. Consent has been successfully manufactured.

PearsonOne of the most significant players in this field is Pearson, who have long been one of the most visible promoters of personalized learning (see the screen capture). At IATEFL, two of the ten conference abstracts which include the word ‘personalized’ are directly sponsored by Pearson. Pearson actually have ten presentations they have directly sponsored or are very closely associated with. Many of these do not refer to personalized learning in the abstract, but would presumably do so in the presentations themselves. There is, for example, a report on a professional development programme in Brazil using TDI (see above). There are two talks about the GSE, described as a tool ‘used to provide a personalised view of students’ language’. The marketing intent is clear: Pearson is to be associated with personalized learning (which is, in turn, associated with a variety of tech tools) – they even have a VP of data analytics, data science and personalized learning.

But the direct funding of the message is probably less important these days than the reinforcement, by those with no vested interests, of the set of beliefs, the ideology, which underpin the selling of personalized learning products. According to this script, personalized learning can promote creativity, empowerment, inclusiveness and preparedness for the real world of work. It sets itself up in opposition to lockstep and factory models of education, and sets learners free as consumers in a world of educational choice. It is a message with which it is hard for many of us to disagree.

manufacturing consentIt is also a marvellous example of propaganda, of the way that consent is manufactured. (If you haven’t read it yet, it’s probably time to read Herman and Chomsky’s ‘Manufacturing Consent: The Political Economy of the Mass Media’.) An excellent account of the way that consent for personalized learning has been manufactured can be found at Benjamin Doxtdator’s blog .

So, a hot topic it is, and its multiple inclusion in the conference programme will no doubt be welcomed by those who are selling ‘personalized’ products. It must be very satisfying to see how normalised the term has become, how it’s no longer necessary to spend too much on promoting the idea, how it’s so associated with technology, (formative) assessment, autonomy and teacher development … since others are doing it for you.

A personalized language learning programme that is worth its name needs to offer a wide variety of paths to accommodate the varying interests, priorities, levels and preferred approaches to learning of the users of the programme. For this to be possible, a huge quantity of learning material is needed (Iwata et al., 2011: 1): the preparation and curation of this material is extremely time-consuming and expensive (despite the pittance that is paid to writers and editors). It’s not surprising, then, that a growing amount of research is being devoted to the exploration of ways of automatically generating language learning material. One area that has attracted a lot of attention is the learning of vocabulary.

Memrise screenshot 2Many simple vocabulary learning tasks are relatively simple to generate automatically. These include matching tasks of various kinds, such as the matching of words or phrases to meanings (either in English or the L1), pictures or collocations, as in many flashcard apps. Doing it well is rather harder: the definitions or translations have to be good and appropriate for learners of the level, the pictures need to be appropriate. If, as is often the case, the lexical items have come from a text or form part of a group of some kind, sense disambiguation software will be needed to ensure that the right meaning is being practised. Anyone who has used flashcard apps knows that the major problem is usually the quality of the content (whether it has been automatically generated or written by someone).

A further challenge is the generation of distractors. In the example here (from Memrise), the distractors have been so badly generated as to render the task more or less a complete waste of time. Distractors must, in some way, be viable alternatives (Smith et al., 2010) but still clearly wrong. That means they should normally be the same part of speech and true cognates should be avoided. Research into the automatic generation of distractors is well-advanced (see, for instance, Kumar at al., 2015) with Smith et al (2010), for example, using a very large corpus and various functions of Sketch Engine (the most well-known corpus query tool) to find collocates and other distractors. Their TEDDCLOG (Testing English with Data-Driven CLOze Generation) system produced distractors that were deemed acceptable 91% of the time. Whilst impressive, there is still a long way to go before human editing / rewriting is no longer needed.

One area that has attracted attention is, of course, tests, and some tasks, such as those in TOEFL (see image). Susanti et al (2015, 2017) were able, given a target word, to automatically generate a reading passage from web sources along with questions of the TOEFL kind. However, only about half of them were considered good enough to be used in actual tests. Again, that is some way off avoiding human intervention altogether, but the automatically generated texts and questions can greatly facilitate the work of human item writers.

toefl task

 

Other tools that might be useful include the University of Nottingham AWL (Academic Word List) Gapmaker . This allows users to type or paste in a text, from which items from the AWL are extracted and replaced as a gap. See the example below. It would, presumably, not be too difficult, to combine this approach with automatic distractor generation and to create multiple choice tasks.

Nottingham_AWL_Gapmaster

WordGapThere are a number of applications that offer the possibility of generating cloze tasks from texts selected by the user (learner or teacher). These have not always been designed with the language learner in mind but one that was is the Android app, WordGap (Knoop & Wilske, 2013). Described by its developers as a tool that ‘provides highly individualized exercises to support contextualized mobile vocabulary learning …. It matches the interests of the learner and increases the motivation to learn’. It may well do all that, but then again, perhaps not. As Knoop & Wilske acknowledge, it is only appropriate for adult, advanced learners and its value as a learning task is questionable. The target item that has been automatically selected is ‘novel’, a word that features in the list Oxford 2000 Keywords (as do all three distractors), and therefore ought to be well below the level of the users. Some people might find this fun, but, in terms of learning, they would probably be better off using an app that made instant look-up of words in the text possible.

More interesting, in my view, is TEDDCLOG (Smith et al., 2010), a system that, given a target learning item (here the focus is on collocations), trawls a large corpus to find the best sentence that illustrates it. ‘Good sentences’ were defined as those which were short (but not too short, or there is not enough useful context, begins with a capital letter and ends with a full stop, has a maximum of two commas; and otherwise contains only the 26 lowercase letters. It must be at a lexical and grammatical level that an intermediate level learner of English could be expected to understand. It must be well-formed and without too much superfluous material. All others were rejected. TEDDCLOG uses Sketch Engine’s GDEX function (Good Dictionary Example Extractor, Kilgarriff et al 2008) to do this.

My own interest in this area came about as a result of my work in the development of the Oxford Vocabulary Trainer . The app offers the possibility of studying both pre-determined lexical items (e.g. the vocabulary list of a coursebook that the learner is using) and free choice (any item could be activated and sent to a learning queue). In both cases, practice takes the form of sentences with the target item gapped. There are a range of hints and help options available to the learner, and feedback is both automatic and formative (i.e. if the supplied answer is not correct, hints are given to push the learner to do better on a second attempt). Leveraging some fairly heavy technology, we were able to achieve a fair amount of success in the automation of intelligent feedback, but what had, at first sight, seemed a lesser challenge – the generation of suitable ‘carrier sentences’, proved more difficult.

The sentences which ‘carry’ the gap should, ideally, be authentic: invented examples often ‘do not replicate the phraseology and collocational preferences of naturally-occurring text’ (Smith et al., 2010). The technology of corpus search tools should allow us to do a better job than human item writers. For that to be the case, we need not only good search tools but a good corpus … and some are better than others for the purposes of language learning. As Fenogenova & Kuzmenko (2016) discovered when using different corpora to automatically generate multiple choice vocabulary exercises, the British Academic Written English corpus (BAWE) was almost 50% more useful than the British National Corpus (BNC). In the development of the Oxford Vocabulary Trainer, we thought we had the best corpus we could get our hands on – the tagged corpus used for the production of the Oxford suite of dictionaries. We could, in addition and when necessary, turn to other corpora, including the BAWE and the BNC. Our requirements for acceptable carrier sentences were similar to those of Smith et al (2010), but were considerably more stringent.

To cut quite a long story short, we learnt fairly quickly that we simply couldn’t automate the generation of carrier sentences with sufficient consistency or reliability. As with some of the other examples discussed in this post, we were able to use the technology to help the writers in their work. We also learnt (rather belatedly, it has to be admitted) that we were trying to find technological solutions to problems that we hadn’t adequately analysed at the start. We hadn’t, for example, given sufficient thought to learner differences, especially the role of L1 (and other languages) in learning English. We hadn’t thought enough about the ‘messiness’ of either language or language learning. It’s possible, given enough resources, that we could have found ways of improving the algorithms, of leveraging other tools, or of deploying additional databases (especially learner corpora) in our quest for a personalised vocabulary learning system. But, in the end, it became clear to me that we were only nibbling at the problem of vocabulary learning. Deliberate learning of vocabulary may be an important part of acquiring a language, but it remains only a relatively small part. Technology may be able to help us in a variety of ways (and much more so in testing than learning), but the dreams of the data scientists (who wrote much of the research cited here) are likely to be short-lived. Experienced writers and editors of learning materials will be needed for the foreseeable future. And truly personalized vocabulary learning, fully supported by technology, will not be happening any time soon.

 

References

Fenogenova, A. & Kuzmenko, E. 2016. Automatic Generation of Lexical Exercises Available online at http://www.dialog-21.ru/media/3477/fenogenova.pdf

Iwata, T., Goto, T., Kojiri, T., Watanabe, T. & T. Yamada. 2011. ‘Automatic Generation of English Cloze Questions Based on Machine Learning’. NTT Technical Review Vol. 9 No. 10 Oct. 2011

Kilgarriff, A. et al. 2008. ‘GDEX: Automatically Finding Good Dictionary Examples in a Corpus.’ In E. Bernal and J. DeCesaris (eds.), Proceedings of the XIII EURALEX International Congress: Barcelona, 15-19 July 2008. Barcelona: l’Institut Universitari de Lingüística Aplicada (IULA) dela Universitat Pompeu Fabra, 425–432.

Knoop, S. & Wilske, S. 2013. ‘WordGap – Automatic generation of gap-filling vocabulary exercises for mobile learning’. Proceedings of the second workshop on NLP for computer-assisted language learning at NODALIDA 2013. NEALT Proceedings Series 17 / Linköping Electronic Conference Proceedings 86: 39–47. Available online at http://www.ep.liu.se/ecp/086/004/ecp13086004.pdf

Kumar, G., Banchs, R.E. & D’Haro, L.F. 2015. ‘RevUP: Automatic Gap-Fill Question Generation from Educational Texts’. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 154–161, Denver, Colorado, June 4, Association for Computational Linguistics

Smith, S., Avinesh, P.V.S. & Kilgariff, A. 2010. ‘Gap-fill tests for Language Learners: Corpus-Driven Item Generation’. Proceedings of ICON-2010: 8th International Conference on Natural Language Processing, Macmillan Publishers, India. Available online at https://curve.coventry.ac.uk/open/file/2b755b39-a0fa-4171-b5ae-5d39568874e5/1/smithcomb2.pdf

Susanti, Y., Iida, R. & Tokunaga, T. 2015. ‘Automatic Generation of English Vocabulary Tests’. Proceedings of 7th International Conference on Computer Supported Education. Available online https://pdfs.semanticscholar.org/aead/415c1e07803756902b859e8b6e47ce312d96.pdf

Susanti, Y., Tokunaga, T., Nishikawa, H. & H. Obari 2017. ‘Evaluation of automatically generated English vocabulary questions’ Research and Practice in Technology Enhanced Learning 12 / 11

 

In my last post, I looked at the way that, in the absence of a clear, shared understanding of what ‘personalization’ means, it has come to be used as a slogan for the promoters of edtech. In this post, I want to look a little more closely at the constellation of meanings that are associated with the term, suggest a way of evaluating just how ‘personalized’ an instructional method might be, and look at recent research into ‘personalized learning’.

In English language teaching, ‘personalization’ often carries a rather different meaning than it does in broader educational discourse. Jeremy Harmer (Harmer, 2012: 276) defines it as ‘when students use language to talk about themselves and things which interest them’. Most commonly, this is in the context of ‘freer’ language practice of grammar or vocabulary of the following kind: ‘Complete the sentences so that they are true for you’. It is this meaning that Scott Thornbury refers to first in his entry for ‘Personalization’ in his ‘An A-Z of ELT’ (Thornbury, 2006: 160). He goes on, however, to expand his definition of the term to include humanistic approaches such as Community Language Learning / Counseling learning (CLL), where learners decide the content of a lesson, where they have agency. I imagine that no one would disagree that an approach such as this is more ‘personalized’ than a ‘complete-the-sentences-so-they-are-true-for you’ exercise to practise the present perfect.

Outside of ELT, ‘personalization’ has been used to refer to everything from ‘from customized interfaces to adaptive tutors, from student-centered classrooms to learning management systems’ (Bulger, 2016: 3). The graphic below (from Bulger, 2016: 3) illustrates just how wide the definitional reach of ‘personalization’ is.

TheBulger_pie_chart

As with Thornbury’s entry in his ‘A – Z of ELT’, it seems uncontentious to say that some things are more ‘personalized’ than others.

Given the current and historical problems with defining the term, it’s not surprising that a number of people have attempted to develop frameworks that can help us to get to grips with the thorny question of ‘personalization’. In the context of language teaching / learning, Renée Disick (Disick, 1975: 58) offered the following categorisation:

Disick

In a similar vein, a few years later, Howard Altman (Altman, 1980) suggested that teaching activities can differ in four main ways: the time allocated for learning, the curricular goal, the mode of learning and instructional expectations (personalized goal setting). He then offered eight permutations of these variables (see below, Altman, 1980: 9), although many more are imaginable.

Altman 1980 chart

Altman and Disick were writing, of course, long before our current technology-oriented view of ‘personalization’ became commonplace. The recent classification of technologically-enabled personalized learning systems by Monica Bulger (see below, Bulger, 2016: 6) reflects how times have changed.

5_types_of_personalized_learning_system

Bulger’s classification focusses on the technology more than the learning, but her continuum is very much in keeping with the views of Disick and Altman. Some approaches are more personalized than others.

The extent to which choices are offered determines the degree of individualization in a particular program. (Disick, 1975: 5)

It is important to remember that learner-centered language teaching is not a point, but rather a continuum. (Altman, 1980: 6)

Larry Cuban has also recently begun to use a continuum as a way of understanding the practices of ‘personalization’ that he observes as part of his research. The overall goals of schooling at both ends of the curriculum are not dissimilar: helping ‘children grow into adults who are creative thinkers, help their communities, enter jobs and succeed in careers, and become thoughtful, mindful adults’.

Cubans curriculum

As Cuban and others before him (e.g. Januszewski, 2001: 57) make clear, the two perspectives are not completely independent of each other. Nevertheless, we can see that one end of this continuum is likely to be materials-centred with the other learner-centred (Dickinson, 1987: 57). At one end, teachers (or their LMS replacements) are more likely to be content-providers and enact traditional roles. At the other, teachers’ roles are ‘more like those of coaches or facilitators’ (Cavanagh, 2014). In short, one end of the continuum is personalization for the learner; the other end is personalization by the learner.

It makes little sense, therefore, to talk about personalized learning as being a ‘good’ or a ‘bad’ thing. We might perceive one form of personalized learning to be more personalized than another, but that does not mean it is any ‘better’ or more effective. The only possible approach is to consider and evaluate the different elements of personalization in an attempt to establish, first, from a theoretical point of view whether they are likely to lead to learning gains, and, second, from an evidence-based perspective whether any learning gains are measurable. In recent posts on this blog, I have been attempting to do that with elements such as learning styles , self-pacing and goal-setting.

Unfortunately, but perhaps not surprisingly, none of the elements that we associate with ‘personalization’ will lead to clear, demonstrable learning gains. A report commissioned by the Gates Foundation (Pane et al, 2015) to find evidence of the efficacy of personalized learning did not, despite its subtitle (‘Promising Evidence on Personalized Learning’), manage to come up with any firm and unequivocal evidence (see Riley, 2017). ‘No single element of personalized learning was able to discriminate between the schools with the largest achievement effects and the others in the sample; however, we did identify groups of elements that, when present together, distinguished the success cases from others’, wrote the authors (Pane et al., 2015: 28). Undeterred, another report (Pane et al., 2017) was commissioned: in this the authors were unable to do better than a very hedged conclusion: ‘There is suggestive evidence that greater implementation of PL practices may be related to more positive effects on achievement; however, this finding requires confirmation through further research’ (my emphases). Don’t hold your breath!

In commissioning the reports, the Gates Foundation were probably asking the wrong question. The conceptual elasticity of the term ‘personalization’ makes its operationalization in any empirical study highly problematic. Meaningful comparison of empirical findings would, as David Hartley notes, be hard because ‘it is unlikely that any conceptual consistency would emerge across studies’ (Hartley, 2008: 378). The question of what works is unlikely to provide a useful (in the sense of actionable) response.

In a new white paper out this week, “A blueprint for breakthroughs,” Michael Horn and I argue that simply asking what works stops short of the real question at the heart of a truly personalized system: what works, for which students, in what circumstances? Without this level of specificity and understanding of contextual factors, we’ll be stuck understanding only what works on average despite aspirations to reach each individual student (not to mention mounting evidence that “average” itself is a flawed construct). Moreover, we’ll fail to unearth theories of why certain interventions work in certain circumstances. And without that theoretical underpinning, scaling personalized learning approaches with predictable quality will remain challenging. Otherwise, as more schools embrace personalized learning, at best each school will have to go at it alone and figure out by trial and error what works for each student. Worse still, if we don’t support better research, “personalized” schools could end up looking radically different but yielding similar results to our traditional system. In other words, we risk rushing ahead with promising structural changes inherent to personalized learning—reorganizing space, integrating technology tools, freeing up seat-time—without arming educators with reliable and specific information about how to personalize to their particular students or what to do, for which students, in what circumstances. (Freeland Fisher, 2016)

References

Altman, H.B. 1980. ‘Foreign language teaching: focus on the learner’ in Altman, H.B. & James, C.V. (eds.) 1980. Foreign Language Teaching: Meeting Individual Needs. Oxford: Pergamon Press, pp.1 – 16

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. New York: Data and Society Research Institute. https://www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Cavanagh, S. 2014. ‘What Is ‘Personalized Learning’? Educators Seek Clarity’ Education Week http://www.edweek.org/ew/articles/2014/10/22/09pl-overview.h34.html

Dickinson, L. 1987. Self-instruction in Language Learning. Cambridge: Cambridge University Press

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

Freeland Fisher, J. 2016. ‘The inconvenient truth about personalized learning’ [Blog post] retrieved from http://www.christenseninstitute.org/blog/the-inconvenient-truth-about-personalized-learning/ (May 4, 2016)

Harmer, J. 2012. Essential Teacher Knowledge. Harlow: Pearson Education

Hartley, D. 2008. ‘Education, Markets and the Pedagogy of Personalisation’ British Journal of Educational Studies 56 / 4: 365 – 381

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

Pane, J. F., Steiner, E. D., Baird, M. D. & Hamilton, L. S. 2015. Continued Progress: Promising Evidence on Personalized Learning. Seattle: Rand Corporation retrieved from http://www.rand.org/pubs/research_reports/RR1365.html

Pane, J.F., Steiner, E. D., Baird, M. D., Hamilton, L. S. & Pane, J.D. 2017. Informing Progress: Insights on Personalized Learning Implementation and Effects. Seattle: Rand Corporation retrieved from https://www.rand.org/pubs/research_reports/RR2042.html

Riley, B. 2017. ‘Personalization vs. How People Learn’ Educational Leadership 74 / 6: 68-73

Thornbury, S. 2006. An A – Z of ELT. Oxford: Macmillan Education

 

 

 

440px-HydraOrganization_HeadLike the mythical monster, the ancient Hydra organisation of Marvel Comics grows two more heads if one is cut off, becoming more powerful in the process. With the most advanced technology on the planet and with a particular focus on data gathering, Hydra operates through international corporations and highly-placed individuals in national governments.
Personalized learning has also been around for centuries. Its present incarnation can be traced to the individualized instructional programmes of the late 19th century which ‘focused on delivering specific subject matter […] based on the principles of scientific management. The intent was to solve the practical problems of the classroom by reducing waste and increasing efficiency, effectiveness, and cost containment in education (Januszewski, 2001: 58). Since then, personalized learning has adopted many different names, including differentiated instruction, individualized instruction, individually guided education, programmed instruction, personalized learning, personalized instruction, and individually prescribed instruction.
Disambiguating the terms has never been easy. In the world of language learning / teaching, it was observed back in the early 1970s ‘that there is little agreement on the description and definition of individualized foreign language instruction’ (Garfinkel, 1971: 379). The point was echoed a few years later by Grittner (1975: 323): it ‘means so many things to so many different people’. A UNESCO document (Chaix & O’Neil, 1978: 6) complained that ‘the term ‘individualization’ and the many expressions using the same root, such as ‘individualized learning’, are much too ambiguous’. Zoom forward to the present day and nothing has changed. Critiquing the British government’s focus on personalized learning, the Institute for Public Policy Research (Johnson, 2004: 17) wrote that it ‘remains difficult to be certain what the Government means by personalised learning’. In the U.S. context, a piece by Sean Cavanagh (2014) in Education Week (which is financially supported by the Gates Foundation) noted that although ‘the term “personalized learning” seems to be everywhere, there is not yet a shared understanding of what it means’. In short, as Arthur Levine  has put it, the words personalized learning ‘generate more heat than light’.
Despite the lack of clarity about what precisely personalized learning actually is, it has been in the limelight of language teaching and learning since before the 1930s when Pendleton (1930: 195) described the idea as being more widespread than ever before. Zoom forward to the 1970s and we find it described as ‘one of the major movements in second-language education at the present time’ (Chastain, 1975: 334). In 1971, it was described as ‘a bandwagon onto which foreign language teachers at all levels are jumping’ (Altman & Politzer, 1971: 6). A little later, in the 1980s, ‘words or phrases such as ‘learner-centered’, ‘student-centered’, ‘personalized’, ‘individualized’, and ‘humanized’ appear as the most frequent modifiers of ‘instruction’ in journals and conferences of foreign language education (Altman & James, 1980). Continue to the present day, and we find that personalized learning is at the centre of the educational policies of governments across the world. Between 2012 and 2015, the U.S. Department of Education threw over half a billion dollars at personalized learning initiatives (Bulger, 2016: 22). At the same time, there is massive sponsorship of personalized learning from the biggest international corporations (the William and Flora Hewlett Foundation, Rogers Family Foundation, Susan and Michael Dell Foundation, and the Eli and Edythe Broad Foundation) (Bulger, 2016: 22). The Bill & Melinda Gates Foundation has invested nearly $175 million in personalized learning development and Facebook’s Mark Zuckerberg is ploughing billions of dollars into it.
There has, however, been one constant: the belief that technology can facilitate the process of personalization (whatever that might be). Technology appears to offer the potential to realise the goal of personalized learning. We have come a long way from Sydney Pressey’s attempts in the 1920s to use teaching machines to individualize instruction. At that time, the machines were just one part of the programme (and not the most important). But each new technology has offered a new range of possibilities to be exploited and each new technology, its advocates argue, ‘will solve the problems better than previous efforts’ (Ferster, 2014: xii). With the advent of data-capturing learning technologies, it has now become virtually impossible to separate advocacy of personalized instruction from advocacy of digitalization in education. As the British Department for Education has put it ‘central to personalised learning is schools’ use of data (DfES (2005) White Paper: Higher Standards, Better Schools for All. London, Department for Education and Skills, para 4.50). When the U.S. Department of Education threw half a billion dollars at personalized learning initiatives, the condition was that these projects ‘use collaborative, data-based strategies and 21st century tools to deliver instruction’ (Bulger, 2016: 22).
Is it just a coincidence that the primary advocates of personalized learning are either vendors of technology or are very close to them in the higher echelons of Hydra (World Economic Forum, World Bank, IMF, etc.)? ‘Personalized learning’ has ‘almost no descriptive value’: it is ‘a term that sounds good without the inconvenience of having any obviously specific pedagogical meaning’ (Feldstein & Hill, 2016: 30). It evokes positive responses, with its ‘nod towards more student-centered learning […], a move that honors the person learning not just the learning institution’ (Watters, 2014). As such, it is ‘a natural for marketing purposes’ since nobody in their right mind would want unpersonalized or depersonalized learning (Feldstein & Hill, 2016: 25). It’s ‘a slogan that nobody’s going to be against, and everybody’s going to be for. Nobody knows what it means, because it doesn’t mean anything. Its crucial value is that it diverts your attention from a question that does mean something: Do you support our policy?’ (Chomsky, 1997).
None of the above is intended to suggest that there might not be goals that come under the ‘personalized learning’ umbrella that are worth working towards. But that’s another story – one I will return to in another post. For the moment, it’s just worth remembering that, in one of the Marvel Comics stories, Captain America, who appeared to be fighting the depersonalized evils of the world, was actually a deep sleeper agent for Hydra.

References
Altman, H.B. & James, C.V. (eds.) 1980. Foreign Language Teaching: Meeting Individual Needs. Oxford: Pergamon Press
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
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. New York: Data and Society Research Institute.
Cavanagh, S. 2014. ‘What Is ‘Personalized Learning’? Educators Seek Clarity’ Education Week
Chaix, P., & O’Neil, C. 1978. A Critical Analysis of Forms of Autonomous Learning (Autodidaxy and Semi-autonomy in the Field of Foreign Language Learning. Final Report. UNESCO Doc Ed 78/WS/58
Chastain, K. 1975. ‘An Examination of the Basic Assumptions of “Individualized” Instruction’ The Modern Language Journal 59 / 7: 334 – 344
Chomsky, N. 1997. Media Control: The Spectacular Achievements of Propaganda. New York: Seven Stories Press
Feldstein, M. & Hill, P. 2016. ‘Personalized Learning: What it Really is and why it Really Matters’ EduCause Review March / April 2016: 25 – 35
Ferster, B. 2014. Teaching Machines. Baltimore: John Hopkins University Press
Garfinkel, A. 1971. ‘Stanford University Conference on Individualizing Foreign Language Instruction, May 6-8, 1971.’ The Modern Language Journal Vol. 55, No. 6 (Oct., 1971), pp. 378-381
Grittner, F. M. 1975. ‘Individualized Instruction: An Historical Perspective’ The Modern Language Journal 59 / 7: 323 – 333
Januszewski, A. 2001. Educational Technology: The Development of a Concept. Englewood, Colorado: Libraries Unlimited
Johnson, M. 2004. Personalised Learning – an Emperor’s Outfit? London: Institute for Public Policy Research
Pendleton, C. S. 1930. ‘Personalizing English Teaching’ Peabody Journal of Education 7 / 4: 195 – 200
Watters, A. 2014. The problem with ‘personalization’ Hack Education