Vocab Victor: a review

Vocab Victor is a very curious vocab app. It’s not a flashcard system, designed to extend vocabulary breadth. Rather it tests the depth of a user’s vocabulary knowledge.

The app’s website refers to the work of Paul Meara (see, for example, Meara, P. 2009. Connected Words. Amsterdam: John Benjamins). Meara explored the ways in which an analysis of the words that we associate with other words can shed light on the organisation of our mental lexicon. Described as ‘gigantic multidimensional cobwebs’ (Aitchison, J. 1987. Words in the Mind. Oxford: Blackwell, p.86), our mental lexicons do not appear to store lexical items in individual slots, but rather they are distributed across networks of associations.

The size of the web (i.e. the number of words, or the level of vocabulary breadth) is important, but equally important is the strength of the connections within the web (or vocabulary depth), as this determines the robustness of vocabulary knowledge. These connections or associations are between different words and concepts and experiences, and they are developed by repeated, meaningful, contextualised exposure to a word. In other words, the connections are firmed up through extensive opportunities to use language.

In word association research, a person is given a prompt word and asked to say the first other word that comes to their mind. For an entertaining example of this process at work, you might enjoy this clip from the comedy show ‘Help’. The research has implications for a wide range of questions, not least second language acquisition. For example, given a particular prompt, native speakers produce a relatively small number of associative responses, and these are reasonably predictable. Learners, on the other hand, typically produce a much greater variety of responses (which might seem surprising, given that they have a smaller vocabulary store to select from).

One way of classifying the different kinds of response is to divide them into two categories: syntagmatic (words that are discoursally connected to the prompt, such as collocations) and paradigmatic (words that are semantically close to the prompt and are the same part of speech). Linguists have noted that learners (both L1 children and L2 learners) show a shift from predominantly syntagmatic responses to more paradigmatic responses as their mental lexicon develops.

The developers of Vocab Victor have set out to build ‘more and stronger associations for the words your students already know, and teaches new words by associating them with existing, known words, helping students acquire native-like word networks. Furthermore, Victor teaches different types of knowledge, including synonyms, “type-of” relationships, collocations, derivations, multiple meanings and form-focused knowledge’. Since we know how important vocabulary depth is, this seems like a pretty sensible learning target.

The app attempts to develop this breadth in two main ways (see below). The ‘core game’ is called ‘Word Strike’ where learners have to pick the word on the arrow which most closely matches the word on the target. The second is called ‘Word Drop’ where a bird holds a word card and the user has to decide if it relates more to one of two other words below. Significantly, they carry out these tasks before any kind of association between form and meaning has been established. The meaning of unknown items can be checked in a monolingual dictionary later. There are a couple of other, less important games that I won’t describe now. The graphics are attractive, if a little juvenile. The whole thing is gamified with levels, leaderboards and so on. It’s free and, presumably, still under development.

The app claims to be for ‘English language learners of all ages [to] develop a more native-like vocabulary’. It also says that it is appropriate for ‘native speaking primary students [to] build and strengthen vocabulary for better test performance and stronger reading skills’, as well as ‘secondary students [to] prepare for the PSAT and SAT’. It was the scope of these claims that first set my alarm bells ringing. How could one app be appropriate for such diverse users? (Spoiler: it can’t, and attempts to make an edtech product suitable for everyone inevitably end up with a product that is suitable for no one.)

Rich, associative lexical networks are the result of successful vocabulary acquisition, but neither Paul Meara nor anyone else in the word association field has, to the best of my knowledge, ever suggested that deliberate study is the way to develop the networks. It is uncontentious to say that vocabulary depth (as shown by associative networks) is best developed through extensive exposure to input – reading and listening.

It is also reasonably uncontentious to say that deliberate study of vocabulary pays greatest dividends in developing vocabulary breadth (not depth), especially at lower levels, with a focus on the top three to eight thousand words in terms of frequency. It may also be useful at higher levels when a learner needs to acquire a limited number of new words for a particular purpose. An example of this would be someone who is going to study in an EMI context and would benefit from rapid learning of the words of the Academic Word List.

The Vocab Victor website says that the app ‘is uniquely focused on intermediate-level vocabulary. The app helps get students beyond this plateau by selecting intermediate-level vocabulary words for your students’. At B1 and B2 levels, learners typically know words that fall between #2500 and #3750 in the frequency tables. At level C2, they know most of the most frequent 5000 items. The less frequent a word is, the less point there is in studying it deliberately.

For deliberate study of vocabulary to serve any useful function, the target language needs to be carefully selected, with a focus on high-frequency items. It makes little sense to study words that will already be very familiar. And it makes no sense to deliberately study apparently random words that are so infrequent (i.e. outside the top 10,000) that it is unlikely they will be encountered again before the deliberate study has been forgotten. Take a look at the examples below and judge for yourself how well chosen the items are.

Vocab Victor appears to focus primarily on semantic fields, as in the example above with ‘smashed’ as a key word. ‘Smashed’, ‘fractured’, ‘shattered’ and ‘cracked’ are all very close in meaning. In order to disambiguate them, it would help learners to see which nouns typically collocate with these words. But they don’t get this with the app – all they get are English-language definitions from Merriam-Webster. What this means is that learners are (1) unlikely to develop a sufficient understanding of target items to allow them to incorporate them into their productive lexicon, and (2) likely to get completely confused with a huge number of similar, low-frequency words (that weren’t really appropriate for deliberate study in the first place). What’s more, lexical sets of this kind may not be a terribly good idea, anyway (see my blog post on the topic).

Vocab Victor takes words, as opposed to lexical items, as the target learning objects. Users may be tested on the associations of any of the meanings of polysemantic items. In the example below (not perhaps the most appropriate choice for primary students!), there are two main meanings, but with other items, things get decidedly more complex (see the example with ‘toss’). Learners are also asked to do the associative tasks ‘Word Strike’ and ‘Word Drop’ before they have had a chance to check the possible meanings of either the prompt item or the associative options.

How anyone could learn from any of this is quite beyond me. I often struggled to choose the correct answer myself; there were also a small number of items whose meaning I wasn’t sure of. I could see no clear way in which items were being recycled (there’s no spaced repetition here). The website claims that ‘adaptating [sic] to your student’s level happens automatically from the very first game’, but I could not see this happening. In fact, it’s very hard to adapt target item selection to an individual learner, since right / wrong or multiple choice answers tell us so little. Does a correct answer tell us that someone knows an item or just that they made a lucky guess? Does an incorrect answer tell us that an item is unknown or just that, under game pressure, someone tapped the wrong button? And how do you evaluate a learner’s lexical level (as a starting point),  even with very rough approximation,  without testing knowledge of at least thirty items first? All in all, then, a very curious app.

One of the most powerful associative responses to a word (especially with younger learners) is what is called a ‘klang’ response: another word which rhymes with or sounds like the prompt word. So, if someone says the word ‘app’ to you, what’s the first klang response that comes to mind?

The fun they had

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Jenny Holzer, Protect me from what I want

Evaluating personalization

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.

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:

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 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.

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’.

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

 

 

 

Personalized learning: Hydra and the power of ambiguity

Like 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

Vocabulary apps: a wish list (revisited)

In December last year, I posted a wish list for vocabulary (flashcard) apps. At the time, I hadn’t read a couple of key research texts on the subject. It’s time for an update.

First off, there’s an article called ‘Intentional Vocabulary Learning Using Digital Flashcards’ by Hsiu-Ting Hung. It’s available online here. Given the lack of empirical research into the use of digital flashcards, it’s an important article and well worth a read. Its basic conclusion is that digital flashcards are more effective as a learning tool than printed word lists. No great surprises there, but of more interest, perhaps, are the recommendations that (1) ‘students should be educated about the effective use of flashcards (e.g. the amount and timing of practice), and this can be implemented through explicit strategy instruction in regular language courses or additional study skills workshops ‘ (Hung, 2015: 111), and (2) that digital flashcards can be usefully ‘repurposed for collaborative learning tasks’ (Hung, ibid.).

However, what really grabbed my attention was an article by Tatsuya Nakata. Nakata’s research is of particular interest to anyone interested in vocabulary learning, but especially so to those with an interest in digital possibilities. A number of his research articles can be freely accessed via his page at ResearchGate, but the one I am interested in is called ‘Computer-assisted second language vocabulary learning in a paired-associate paradigm: a critical investigation of flashcard software’. Don’t let the title put you off. It’s a review of a pile of web-based flashcard programs: since the article is already five years old, many of the programs have either changed or disappeared, but the critical approach he takes is more or less as valid now as it was then (whether we’re talking about web-based stuff or apps).

Nakata divides his evaluation for criteria into two broad groups.

Flashcard creation and editing

(1) Flashcard creation: Can learners create their own flashcards?

(2) Multilingual support: Can the target words and their translations be created in any language?

(3) Multi-word units: Can flashcards be created for multi-word units as well as single words?

(4) Types of information: Can various kinds of information be added to flashcards besides the word meanings (e.g. parts of speech, contexts, or audios)?

(5) Support for data entry: Does the software support data entry by automatically supplying information about lexical items such as meaning, parts of speech, contexts, or frequency information from an internal database or external resources?

(6) Flashcard set: Does the software allow learners to create their own sets of flashcards?

Learning

(1) Presentation mode: Does the software have a presentation mode, where new items are introduced and learners familiarise themselves with them?

(2) Retrieval mode: Does the software have a retrieval mode, which asks learners to recall or choose the L2 word form or its meaning?

(3) Receptive recall: Does the software ask learners to produce the meanings of target words?

(4) Receptive recognition: Does the software ask learners to choose the meanings of target words?

(5) Productive recall: Does the software ask learners to produce the target word forms corresponding to the meanings provided?

(6) Productive recognition: Does the software ask learners to choose the target word forms corresponding to the meanings provided?

(7) Increasing retrieval effort: For a given item, does the software arrange exercises in the order of increasing difficulty?

(8) Generative use: Does the software encourage generative use of words, where learners encounter or use previously met words in novel contexts?

(9) Block size: Can the number of words studied in one learning session be controlled and altered?

(10) Adaptive sequencing: Does the software change the sequencing of items based on learners’ previous performance on individual items?

(11) Expanded rehearsal: Does the software help implement expanded rehearsal, where the intervals between study trials are gradually increased as learning proceeds? (Nakata, T. (2011): ‘Computer-assisted second language vocabulary learning in a paired-associate paradigm: a critical investigation of flashcard software’ Computer Assisted Language Learning, 24:1, 17-38)

It’s a rather different list from my own (there’s nothing I would disagree with here), because mine is more general and his is exclusively oriented towards learning principles. Nakata makes the point towards the end of the article that it would ‘be useful to investigate learners’ reactions to computer-based flashcards to examine whether they accept flashcard programs developed according to learning principles’ (p. 34). It’s far from clear, he points out, that conformity to learning principles are at the top of learners’ agendas. More than just users’ feelings about computer-based flashcards in general, a key concern will be the fact that there are ‘large individual differences in learners’ perceptions of [any flashcard] program’ (Nakata, N. 2008. ‘English vocabulary learning with word lists, word cards and computers: implications from cognitive psychology research for optimal spaced learning’ ReCALL 20(1), p. 18).

I was trying to make a similar point in another post about motivation and vocabulary apps. In the end, as with any language learning material, research-driven language learning principles can only take us so far. User experience is a far more difficult creature to pin down or to make generalisations about. A user’s reaction to graphics, gamification, uploading time and so on are so powerful and so subjective that learning principles will inevitably play second fiddle. That’s not to say, of course, that Nakata’s questions are not important: it’s merely to wonder whether the bigger question is truly answerable.

Nakata’s research identifies plenty of room for improvement in digital flashcards, and although the article is now quite old, not a lot had changed. Key areas to work on are (1) the provision of generative use of target words, (2) the need to increase retrieval effort, (3) the automatic provision of information about meaning, parts of speech, or contexts (in order to facilitate flashcard creation), and (4) the automatic generation of multiple-choice distractors.

In the conclusion of his study, he identifies one flashcard program which is better than all the others. Unsurprisingly, five years down the line, the software he identifies is no longer free, others have changed more rapidly in the intervening period, and who knows will be out in front next week?

 

Adaptive learning in ELT: the current state of play

About two and a half years ago when I started writing this blog, there was a lot of hype around adaptive learning and the big data which might drive it. Two and a half years are a long time in technology. A look at Google Trends suggests that interest in adaptive learning has been pretty static for the last couple of years. It’s interesting to note that 3 of the 7 lettered points on this graph are Knewton-related media events (including the most recent, A, which is Knewton’s latest deal with Hachette) and 2 of them concern McGraw-Hill. It would be interesting to know whether these companies follow both parts of Simon Cowell’s dictum of ‘Create the hype, but don’t ever believe it’.

A look at the Hype Cycle (see here for Wikipedia’s entry on the topic and for criticism of the hype of Hype Cycles) of the IT research and advisory firm, Gartner, indicates that both big data and adaptive learning have now slid into the ‘trough of disillusionment’, which means that the market has started to mature, becoming more realistic about how useful the technologies can be for organizations.

A few years ago, the Gates Foundation, one of the leading cheerleaders and financial promoters of adaptive learning, launched its Adaptive Learning Market Acceleration Program (ALMAP) to ‘advance evidence-based understanding of how adaptive learning technologies could improve opportunities for low-income adults to learn and to complete postsecondary credentials’. It’s striking that the program’s aims referred to how such technologies could lead to learning gains, not whether they would. Now, though, with the publication of a report commissioned by the Gates Foundation to analyze the data coming out of the ALMAP Program, things are looking less rosy. The report is inconclusive. There is no firm evidence that adaptive learning systems are leading to better course grades or course completion. ‘The ultimate goal – better student outcomes at lower cost – remains elusive’, the report concludes. Rahim Rajan, a senior program office for Gates, is clear: ‘There is no magical silver bullet here.’

The same conclusion is being reached elsewhere. A report for the National Education Policy Center (in Boulder, Colorado) concludes: Personalized Instruction, in all its many forms, does not seem to be the transformational technology that is needed, however. After more than 30 years, Personalized Instruction is still producing incremental change. The outcomes of large-scale studies and meta-analyses, to the extent they tell us anything useful at all, show mixed results ranging from modest impacts to no impact. Additionally, one must remember that the modest impacts we see in these meta-analyses are coming from blended instruction, which raises the cost of education rather than reducing it (Enyedy, 2014: 15 -see reference at the foot of this post). In the same vein, a recent academic study by Meg Coffin Murray and Jorge Pérez (2015, ‘Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning’) found that ‘adaptive learning systems have negligible impact on learning outcomes’.

In the latest educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Reimagining the Role of Technology in Education’, 2016) the only mentions of the word ‘adaptive’ are in the context of testing. And the latest OECD report on ‘Students, Computers and Learning: Making the Connection’ (2015), finds, more generally, that information and communication technologies, when they are used in the classroom, have, at best, a mixed impact on student performance.

There is, however, too much money at stake for the earlier hype to disappear completely. Sponsored cheerleading for adaptive systems continues to find its way into blogs and national magazines and newspapers. EdSurge, for example, recently published a report called ‘Decoding Adaptive’ (2016), sponsored by Pearson, that continues to wave the flag. Enthusiastic anecdotes take the place of evidence, but, for all that, it’s a useful read.

In the world of ELT, there are plenty of sales people who want new products which they can call ‘adaptive’ (and gamified, too, please). But it’s striking that three years after I started following the hype, such products are rather thin on the ground. Pearson was the first of the big names in ELT to do a deal with Knewton, and invested heavily in the company. Their relationship remains close. But, to the best of my knowledge, the only truly adaptive ELT product that Pearson offers is the PTE test.

Macmillan signed a contract with Knewton in May 2013 ‘to provide personalized grammar and vocabulary lessons, exam reviews, and supplementary materials for each student’. In December of that year, they talked up their new ‘big tree online learning platform’: ‘Look out for the Big Tree logo over the coming year for more information as to how we are using our partnership with Knewton to move forward in the Language Learning division and create content that is tailored to students’ needs and reactive to their progress.’ I’ve been looking out, but it’s all gone rather quiet on the adaptive / platform front.

In September 2013, it was the turn of Cambridge to sign a deal with Knewton ‘to create personalized learning experiences in its industry-leading ELT digital products for students worldwide’. This year saw the launch of a major new CUP series, ‘Empower’. It has an online workbook with personalized extra practice, but there’s nothing (yet) that anyone would call adaptive. More recently, Cambridge has launched the online version of the 2^nd edition of Touchstone. Nothing adaptive there, either.

Earlier this year, Cambridge published The Cambridge Guide to Blended Learning for Language Teaching, edited by Mike McCarthy. It contains a chapter by M.O.Z. San Pedro and R. Baker on ‘Adaptive Learning’. It’s an enthusiastic account of the potential of adaptive learning, but it doesn’t contain a single reference to language learning or ELT!

So, what’s going on? Skepticism is becoming the order of the day. The early hype of people like Knewton’s Jose Ferreira is now understood for what it was. Companies like Macmillan got their fingers badly burnt when they barked up the wrong tree with their ‘Big Tree’ platform.

Noel Enyedy captures a more contemporary understanding when he writes: Personalized Instruction is based on the metaphor of personal desktop computers—the technology of the 80s and 90s. Today’s technology is not just personal but mobile, social, and networked. The flexibility and social nature of how technology infuses other aspects of our lives is not captured by the model of Personalized Instruction, which focuses on the isolated individual’s personal path to a fixed end-point. To truly harness the power of modern technology, we need a new vision for educational technology (Enyedy, 2014: 16).

Adaptive solutions aren’t going away, but there is now a much better understanding of what sorts of problems might have adaptive solutions. Testing is certainly one. As the educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Re-imagining the Role of Technology in Education’, 2016) puts it: Computer adaptive testing, which uses algorithms to adjust the difficulty of questions throughout an assessment on the basis of a student’s responses, has facilitated the ability of assessments to estimate accurately what students know and can do across the curriculum in a shorter testing session than would otherwise be necessary. In ELT, Pearson and EF have adaptive tests that have been well researched and designed.

Vocabulary apps which deploy adaptive technology continue to become more sophisticated, although empirical research is lacking. Automated writing tutors with adaptive corrective feedback are also developing fast, and I’ll be writing a post about these soon. Similarly, as speech recognition software improves, we can expect to see better and better automated adaptive pronunciation tutors. But going beyond such applications, there are bigger questions to ask, and answers to these will impact on whatever direction adaptive technologies take. Large platforms (LMSs), with or without adaptive software, are already beginning to look rather dated. Will they be replaced by integrated apps, or are apps themselves going to be replaced by bots (currently riding high in the Hype Cycle)? In language learning and teaching, the future of bots is likely to be shaped by developments in natural language processing (another topic about which I’ll be blogging soon). Nobody really has a clue where the next two and a half years will take us (if anywhere), but it’s becoming increasingly likely that adaptive learning will be only one very small part of it.

 

Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning. Boulder, CO: National Education Policy Center. Retrieved 17.07.16 from http://nepc.colorado.edu/publication/personalized-instruction

Adaptive teacher development?

Ok, let’s be honest here. This post is about teacher training, but ‘development’ sounds more respectful, more humane, more modern. Teacher development (self-initiated, self-evaluated, collaborative and holistic) could be adaptive, but it’s unlikely that anyone will want to spend the money on developing an adaptive teacher development platform any time soon. Teacher training (top-down, pre-determined syllabus and externally evaluated) is another matter. If you’re not too clear about this distinction, see Penny Ur’s article in The Language Teacher.

The main point of adaptive learning tools is to facilitate differentiated instruction. They are, as Pearson’s latest infomercial booklet describes them, ‘educational technologies that can respond to a student’s interactions in real-time by automatically providing the student with individual support’. Differentiation or personalization (or whatever you call it) is, as I’ve written before  , the declared goal of almost everyone in educational power these days. What exactly it is may be open to question (see Michael Feldstein’s excellent article), as may be the question of whether or not it is actually such a desideratum (see, for example, this article ). But, for the sake of argument, let’s agree that it’s mostly better than one-size-fits-all.

Teachers around the world are being encouraged to adopt a differentiated approach with their students, and they are being encouraged to use technology to do so. It is technology that can help create ‘robust personalized learning environments’ (says the White House)  . Differentiation for language learners could be facilitated by ‘social networking systems, podcasts, wikis, blogs, encyclopedias, online dictionaries, webinars, online English courses,’ etc. (see Alexandra Chistyakova’s post on eltdiary ).

But here’s the crux. If we want teachers to adopt a differentiated approach, they really need to have experienced it themselves in their training. An interesting post on edweek  sums this up: If professional development is supposed to lead to better pedagogy that will improve student learning AND we are all in agreement that modeling behaviors is the best way to show people how to do something, THEN why not ensure all professional learning opportunities exhibit the qualities we want classroom teachers to have?

Differentiated teacher development / training is rare. According to the Center for Public Education’s Teaching the Teachers report , almost all teachers participate in ‘professional development’ (PD) throughout the year. However, a majority of those teachers find the PD in which they participate ineffective. Typically, the development is characterised by ‘drive-by’ workshops, one-size-fits-all presentations, ‘been there, done that’ topics, little or no modelling of what is being taught, a focus on rotating fads and a lack of follow-up. This report is not specifically about English language teachers, but it will resonate with many who are working in English language teaching around the world.

The promotion of differentiated teacher development is gaining traction: see here or here , for example, or read Cindy A. Strickland’s ‘Professional Development for Differentiating Instruction’.

Remember, though, that it’s really training, rather than development, that we’re talking about. After all, if one of the objectives is to equip teachers with a skills set that will enable them to become more effective instructors of differentiated learning, this is most definitely ‘training’ (notice the transitivity of the verbs ‘enable’ and ‘equip’!). In this context, a necessary starting point will be some sort of ‘knowledge graph’ (which I’ve written about here ). For language teachers, these already exist, including the European Profiling Grid , the Eaquals Framework for Language Teacher Training and Development, the Cambridge English Teaching Framework and the British Council’s Continuing Professional Development Framework (CPD) for Teachers  . We can expect these to become more refined and more granularised, and a partial move in this direction is the Cambridge English Digital Framework for Teachers  . Once a knowledge graph is in place, the next step will be to tag particular pieces of teacher training content (e.g. webinars, tasks, readings, etc.) to locations in the framework that is being used. It would not be too complicated to engineer dynamic frameworks which could be adapted to individual or institutional needs.

This process will be facilitated by the fact that teacher training content is already being increasingly granularised. Whether it’s an MA in TESOL or a shorter, more practically oriented course, things are getting more and more bite-sized, with credits being awarded to these short bites, as course providers face stiffer competition and respond to market demands.

Classroom practice could also form part of such an adaptive system. One tool that could be deployed would be Visible Classroom , an automated system for providing real-time evaluative feedback for teachers. There is an ‘online dashboard providing teachers with visual information about their teaching for each lesson in real-time. This includes proportion of teacher talk to student talk, number and type of questions, and their talking speed.’ John Hattie, who is behind this project, says that teachers ‘account for about 30% of the variance in student achievement and [are] the largest influence outside of individual student effort.’ Teacher development with a tool like Visible Classroom is ultimately all about measuring teacher performance (against a set of best-practice benchmarks identified by Hattie’s research) in order to improve the learning outcomes of the students.

You may have noticed the direction in which this part of this blog post is going. I began by talking about social networking systems, podcasts, wikis, blogs and so on, and just now I’ve mentioned the summative, credit-bearing possibilities of an adaptive teacher development training programme. It’s a tension that is difficult to resolve. There’s always a paradox in telling anyone that they are going to embark on a self-directed course of professional development. Whoever pays the piper calls the tune and, if an institution decides that it is worth investing significant amounts of money in teacher development, they will want a return for their money. The need for truly personalised teacher development is likely to be overridden by the more pressing need for accountability, which, in turn, typically presupposes pre-determined course outcomes, which can be measured in some way … so that quality (and cost-effectiveness and so on) can be evaluated.

Finally, it’s worth asking if language teaching (any more than language learning) can be broken down into small parts that can be synthesized later into a meaningful and valuable whole. Certainly, there are some aspects of language teaching (such as the ability to use a dashboard on an LMS) which lend themselves to granularisation. But there’s a real danger of losing sight of the forest of teaching if we focus on the individual trees that can be studied and measured.

Vocabulary apps: a wish list

Having spent a lot of time recently looking at vocabulary apps, I decided to put together a Christmas wish list of the features of my ideal vocabulary app. The list is not exhaustive and I’ve given more attention to some features than others. What (apart from testing) have I missed out?

1             Spaced repetition

Since the point of a vocabulary app is to help learners memorise vocabulary items, it is hard to imagine a decent system that does not incorporate spaced repetition. Spaced repetition algorithms offer one well-researched way of improving the brain’s ‘forgetting curve’. These algorithms come in different shapes and sizes, and I am not technically competent to judge which is the most efficient. However, as Peter Ellis Jones, the developer of a flashcard system called CardFlash, points out, efficiency is only one half of the rote memorisation problem. If you are not motivated to learn, the cleverness of the algorithm is moot. Fundamentally, learning software needs to be fun, rewarding, and give a solid sense of progression.

2             Quantity, balance and timing of new and ‘old’ items

A spaced repetition algorithm determines the optimum interval between repetitions, but further algorithms will be needed to determine when and with what frequency new items will be added to the deck. Once a system knows how many items a learner needs to learn and the time in which they have to do it, it is possible to determine the timing and frequency of the presentation of new items. But the system cannot know in advance how well an individual learner will learn the items (for any individual, some items will be more readily learnable than others) nor the extent to which learners will live up to their own positive expectations of time spent on-app. As most users of flashcard systems know, it is easy to fall behind, feel swamped and, ultimately, give up. An intelligent system needs to be able to respond to individual variables in order to ensure that the learning load is realistic.

3             Task variety

A standard flashcard system which simply asks learners to indicate whether they ‘know’ a target item before they flip over the card rapidly becomes extremely boring. A system which tests this knowledge soon becomes equally dull. There needs to be a variety of ways in which learners interact with an app, both for reasons of motivation and learning efficiency. It may be the case that, for an individual user, certain task types lead to more rapid gains in learning. An intelligent, adaptive system should be able to capture this information and modify the selection of task types.

Most younger learners and some adult learners will respond well to the inclusion of games within the range of task types. Examples of such games include the puzzles developed by Oliver Rose in his Phrase Maze app to accompany Quizlet practice.

4             Generative use

Memory researchers have long known about the ‘Generation Effect’ (see for example this piece of research from the Journal of Verbal Learning and Learning Behavior, 1978). Items are better learnt when the learner has to generate, in some (even small) way, the target item, rather than simply reading it. In vocabulary learning, this could be, for example, typing in the target word or, more simply, inserting some missing letters. Systems which incorporate task types that require generative use are likely to result in greater learning gains than simple, static flashcards with target items on one side and definitions or translations on the other.

5             Receptive and productive practice

The most basic digital flashcard systems require learners to understand a target item, or to generate it from a definition or translation prompt. Valuable as this may be, it won’t help learners much to use these items productively, since these systems focus exclusively on meaning. In order to do this, information must be provided about collocation, colligation, register, etc and these aspects of word knowledge will need to be focused on within the range of task types. At the same time, most vocabulary apps that I have seen focus primarily on the written word. Although any good system will offer an audio recording of the target item, and many will offer the learner the option of recording themselves, learners are invariably asked to type in their answers, rather than say them. For the latter, speech recognition technology will be needed. Ideally, too, an intelligent system will compare learner recordings with the audio models and provide feedback in such a way that the learner is guided towards a closer reproduction of the model.

6             Scaffolding and feedback

Most flashcard systems are basically low-stakes, practice self-testing. Research (see, for example, Dunlosky et al’s metastudy ‘Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology’) suggests that, as a learning strategy, practice testing has high utility – indeed, of higher utility than other strategies like keyword mnemonics or highlighting. However, an element of tutoring is likely to enhance practice testing, and, for this, scaffolding and feedback will be needed. If, for example, a learner is unable to produce a correct answer, they will probably benefit from being guided towards it through hints, in the same way as a teacher would elicit in a classroom. Likewise, feedback on why an answer is wrong (as opposed to simply being told that you are wrong), followed by encouragement to try again, is likely to enhance learning. Such feedback might, for example, point out that there is perhaps a spelling problem in the learner’s attempted answer, that the attempted answer is in the wrong part of speech, or that it is semantically close to the correct answer but does not collocate with other words in the text. The incorporation of intelligent feedback of this kind will require a number of NLP tools, since it will never be possible for a human item-writer to anticipate all the possible incorrect answers. A current example of intelligent feedback of this kind can be found in the Oxford English Vocabulary Trainer app.

7             Content

At the very least, a decent vocabulary app will need good definitions and translations (how many different languages?), and these will need to be tagged to the senses of the target items. These will need to be supplemented with all the other information that you find in a good learner’s dictionary: syntactic patterns, collocations, cognates, an indication of frequency, etc. The only way of getting this kind of high-quality content is by paying to license it from a company with expertise in lexicography. It doesn’t come cheap.

There will also need to be example sentences, both to illustrate meaning / use and for deployment in tasks. Dictionary databases can provide some of these, but they cannot be relied on as a source. This is because the example sentences in dictionaries have been selected and edited to accompany the other information provided in the dictionary, and not as items in practice exercises, which have rather different requirements. Once more, the solution doesn’t come cheap: experienced item writers will be needed.

Dictionaries describe and illustrate how words are typically used. But examples of typical usage tend to be as dull as they are forgettable. Learning is likely to be enhanced if examples are cognitively salient: weird examples with odd collocations, for example. Another thing for the item writers to think about.

A further challenge for an app which is not level-specific is that both the definitions and example sentences need to be level-specific. An A1 / A2 learner will need the kind of content that is found in, say, the Oxford Essential dictionary; B2 learners and above will need content from, say, the OALD.

8             Artwork and design

It’s easy enough to find artwork or photos of concrete nouns, but try to find or commission a pair of pictures that differentiate, for example, the adjectives ‘wild’ and ‘dangerous’ … What kind of pictures might illustrate simple verbs like ‘learn’ or ‘remember’? Will such illustrations be clear enough when squeezed into a part of a phone screen? Animations or very short video clips might provide a solution in some cases, but these are more expensive to produce and video files are much heavier.

With a few notable exceptions, such as the British Councils’s MyWordBook 2, design in vocabulary apps has been largely forgotten.

9             Importable and personalisable lists

Many learners will want to use a vocabulary app in association with other course material (e.g. coursebooks). Teachers, however, will inevitably want to edit these lists, deleting some items, adding others. Learners will want to do the same. This is a huge headache for app designers. If new items are going to be added to word lists, how will the definitions, example sentences and illustrations be generated? Will the database contain audio recordings of these words? How will these items be added to the practice tasks (if these include task types that go beyond simple double-sided flashcards)? NLP tools are not yet good enough to trawl a large corpus in order to select (and possibly edit) sentences that illustrate the right meaning and which are appropriate for interactive practice exercises. We can personalise the speed of learning and even the types of learning tasks, so long as the target language is predetermined. But as soon as we allow for personalisation of content, we run into difficulties.

10          Gamification

Maintaining motivation to use a vocabulary app is not easy. Gamification may help. Measuring progress against objectives will be a start. Stars and badges and leaderboards may help some users. Rewards may help others. But gamification features need to be built into the heart of the system, into the design and selection of tasks, rather than simply tacked on as an afterthought. They need to be trialled and tweaked, so analytics will be needed.

11          Teacher support

Although the use of vocabulary flashcards is beginning to catch on with English language teachers, teachers need help with ways to incorporate them in the work they do with their students. What can teachers do in class to encourage use of the app? In what ways does app use require teachers to change their approach to vocabulary work in the classroom? Reporting functions can help teachers know about the progress their students are making and provide very detailed information about words that are causing problems. But, as anyone involved in platform-based course materials knows, teachers need a lot of help.

12          And, of course, …

Apps need to be usable with different operating systems. Ideally, they should be (partially) usable offline. Loading times need to be short. They need to be easy and intuitive to use.

It’s unlikely that I’ll be seeing a vocabulary app with all of these features any time soon. Or, possibly, ever. The cost of developing something that could do all this would be extremely high, and there is no indication that there is a market that would be ready to pay the sort of prices that would be needed to cover the costs of development and turn a profit. We need to bear in mind, too, the fact that vocabulary apps can only ever assist in the initial acquisition of vocabulary: apps alone can’t solve the vocabulary learning problem (despite the silly claims of some app developers). The need for meaningful communicative use, extensive reading and listening, will not go away because a learner has been using an app. So, how far can we go in developing better and better vocabulary apps before users decide that a cheap / free app, with all its shortcomings, is actually good enough?

I posted a follow up to this post in October 2016.

Research update

Decent research into adaptive learning remains very thin on the ground. Disappointingly, the Journal of Learning Analytics has only managed one issue so far in 2015, compared to three in 2014. But I recently came across an article in Vol. 18 (pp. 111 – 125) of  Informing Science: the International Journal of an Emerging Transdiscipline entitled Informing and performing: A study comparing adaptive learning to traditional learning by Murray, M. C., & Pérez, J. of Kennesaw State University.

The article is worth reading, not least because of the authors’ digestible review of  adaptive learning theory and their discussion of levels of adaptation, including a handy diagram (see below) which they have reproduced from a white paper by Tyton Partners ‘Learning to Adapt: Understanding the Adaptive Learning Supplier Landscape’. Murray and Pérez make clear that adaptive learning theory is closely connected to the belief that learning is improved when instruction is personalized — adapted to individual learning styles, but their approach is surprisingly uncritical. They write, for example, that the general acceptance of learning styles is evidenced in recommended teaching strategies in nearly every discipline, and learning styles continue to inform the evolution of adaptive learning systems, and quote from the much-quoted Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008) Learning styles: concepts and evidence, Psychological Science in the Public Interest, 9, 105–119. But Pashler et al concluded that the current evidence supporting the use of learning style-matched approaches is virtually non-existent (see here for a review of Pashler et al). And, in the world of ELT, an article in the latest edition of ELTJ by Carol Lethaby and Patricia Harries disses learning styles and other neuromyths. Given the close connection between adaptive learning theory and learning styles, one might reasonably predict that a comparative study of adaptive learning and traditional learning would not come out with much evidence in support of the former.

Murray and Pérez set out, anyway, to explore the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Their study compared adaptive and traditional methods in a university-level digital literacy course. Their conclusion? This study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes.

I was, however, more interested in the comments which followed this general conclusion. They point out that learning outcomes are only one measure of quality. Others, such as student persistence and engagement, they claim, can be positively affected by the employment of adaptive systems. I am not convinced. I think it’s simply far too soon to be able to judge this, and we need to wait quite some time for novelty effects to wear off. Murray and Pérez provide two references in support of their claim. One is an article by Josh Jarrett, Bigfoot, Goldilocks, and Moonshots: A Report from the Frontiers of Personalized Learning in Educause. Jarrett is Deputy Director for Postsecondary Success at the Bill & Melinda Gates Foundation and Educause is significantly funded by the Gates Foundation. Not, therefore, an entirely unbiased and trustworthy source. The other is a journalistic piece in Forbes. It’s by Tim Zimmer, entitled Rethinking higher ed: A case for adaptive learning and it reads like an advert. Zimmer is a ‘CCAP contributor’. CCAP is the Centre for College Affordability and Productivity, a libertarian, conservative foundation with a strong privatization agenda. Not, therefore, a particularly reliable source, either.

Despite their own findings, Murray and Pérez follow up their claim about student persistence and engagement with what they describe as a more compelling still argument for adaptive learning. This, they say, is the intuitively appealing case for adaptive learning systems as engines with which institutions can increase access and reduce costs. Ah, now we’re getting to the point!

 

 

 

 

 

 

 

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Behaviourism and adaptive learning

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 2^nd 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 It’ set 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.

The 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 control

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