Posts Tagged ‘ed tech’

I’m a sucker for meta-analyses, those aggregates of multiple studies that generate an effect size, and I am even fonder of meta-meta analyses. I skip over the boring stuff about inclusion criteria and statistical procedures and zoom in on the results and discussion. I’ve pored over Hattie (2009) and, more recently, Dunlosky et al (2013), and quoted both more often than is probably healthy. Hardly surprising, then, that I was eager to read Luke Plonsky and Nicole Ziegler’s ‘The CALL–SLA interface: insights from a second-order synthesis’ (Plonsky & Ziegler, 2016), an analysis of nearly 30 meta-analyses (later whittled down to 14) looking at the impact of technology on L2 learning. The big question they were looking to find an answer to? How effective is computer-assisted language learning compared to face-to-face contexts?

Plonsky & Ziegler

Plonsky and Ziegler found that there are unequivocally ‘positive effects of technology on language learning’. In itself, this doesn’t really tell us anything, simply because there are too many variables. It’s a statistical soundbite, ripe for plucking by anyone with an edtech product to sell. Much more useful is to understand which technologies used in which ways are likely to have a positive effect on learning. It appears from Plonsky and Ziegler’s work that the use of CALL glosses (to develop reading comprehension and vocabulary development) provides the strongest evidence of technology’s positive impact on learning. The finding is reinforced by the fact that this particular technology was the most well-represented research area in the meta-analyses under review.

What we know about glosses

gloss_gloss_WordA gloss is ‘a brief definition or synonym, either in L1 or L2, which is provided with [a] text’ (Nation, 2013: 238). They can take many forms (e.g. annotations in the margin or at the foot a printed page), but electronic or CALL glossing is ‘an instant look-up capability – dictionary or linked’ (Taylor, 2006; 2009) which is becoming increasingly standard in on-screen reading. One of the most widely used is probably the translation function in Microsoft Word: here’s the French gloss for the word ‘gloss’.

Language learning tools and programs are making increasing use of glosses. Here are two examples. The first is Lingro , a dictionary tool that learners can have running alongside any webpage: clicking on a word brings up a dictionary entry, and the word can then be exported into a wordlist which can be practised with spaced repetition software. The example here is using the English-English dictionary, but a number of bilingual pairings are available. The second is from Bliu Bliu , a language learning app that I unkindly reviewed here .Lingro_example

Bliu_Bliu_example_2

So, what did Plonsky and Ziegler discover about glosses? There were two key takeways:

  • both L1 and L2 CALL glossing can be beneficial to learners’ vocabulary development (Taylor, 2006, 2009, 2013)
  • CALL / electronic glosses lead to more learning gains than paper-based glosses (p.22)

On the surface, this might seem uncontroversial, but if you took a good look at the three examples (above) of online glosses, you’ll be thinking that something is not quite right here. Lingro’s gloss is a fairly full dictionary entry: it contains too much information for the purpose of a gloss. Cognitive Load Theory suggests that ‘new information be provided concisely so as not to overwhelm the learner’ (Khezrlou et al, 2017: 106): working out which definition is relevant here (the appropriate definition is actually the sixth in this list) will overwhelm many learners and interfere with the process of reading … which the gloss is intended to facilitate. In addition, the language of the definitions is more difficult than the defined item. Cognitive load is, therefore, further increased. Lingro needs to use a decent learner’s dictionary (with a limited defining vocabulary), rather than relying on the free Wiktionary.

Nation (2013: 240) cites research which suggests that a gloss is most effective when it provides a ‘core meaning’ which users will have to adapt to what is in the text. This is relatively unproblematic, from a technological perspective, but few glossing tools actually do this. The alternative is to use NLP tools to identify the context-specific meaning: our ability to do this is improving all the time but remains some way short of total accuracy. At the very least, NLP tools are needed to identify part of speech (which will increase the probability of hitting the right meaning). Bliu Bliu gets things completely wrong, confusing the verb and the adjective ‘own’.

Both Lingro and Bliu Bliu fail to meet the first requirement of a gloss: ‘that it should be understood’ (Nation, 2013: 239). Neither is likely to contribute much to the vocabulary development of learners. We will need to modify Plonsky and Ziegler’s conclusions somewhat: they are contingent on the quality of the glosses. This is not, however, something that can be assumed …. as will be clear from even the most cursory look at the language learning tools that are available.

Nation (2013: 447) also cites research that ‘learning is generally better if the meaning is written in the learner’s first language. This is probably because the meaning can be easily understood and the first language meaning already has many rich associations for the learner. Laufer and Shmueli (1997) found that L1 glosses are superior to L2 glosses in both short-term and long-term (five weeks) retention and irrespective of whether the words are learned in lists, sentences or texts’. Not everyone agrees, and a firm conclusion either way is probably not possible: learner variables (especially learner preferences) preclude anything conclusive, which is why I’ve highlighted Nation’s use of the word ‘generally’. If we have a look at Lingro’s bilingual gloss, I think you’ll agree that the monolingual and bilingual glosses are equally unhelpful, equally unlikely to lead to better learning, whether it’s vocabulary acquisition or reading comprehension.bilingual lingro

 

The issues I’ve just discussed illustrate the complexity of the ‘glossing’ question, but they only scratch the surface. I’ll dig a little deeper.

1 Glosses are only likely to be of value to learning if they are used selectively. Nation (2013: 242) suggests that ‘it is best to assume that the highest density of glossing should be no more than 5% and preferably around 3% of the running words’. Online glosses make the process of look-up extremely easy. This is an obvious advantage over look-ups in a paper dictionary, but there is a real risk, too, that the ease of online look-up encourages unnecessary look-ups. More clicks do not always lead to more learning. The value of glosses cannot therefore be considered independently of a consideration of the level (i.e. appropriacy) of the text that they are being used with.

2 A further advantage of online glosses is that they can offer a wide range of information, e.g. pronunciation, L1 translation, L2 definition, visuals, example sentences. The review of literature by Khezrlou et al (2017: 107) suggests that ‘multimedia glosses can promote vocabulary learning but uncertainty remains as to whether they also facilitate reading comprehension’. Barcroft (2015), however, warns that pictures may help learners with meaning, but at the cost of retention of word form, and the research of Boers et al did not find evidence to support the use of pictures. Even if we were to accept the proposition that pictures might be helpful, we would need to hold two caveats. First, the amount of multimodal support should not lead to cognitive overload. Second, pictures need to be clear and appropriate: a condition that is rarely met in online learning programs. The quality of multimodal glosses is more important than their inclusion / exclusion.

3 It’s a commonplace to state that learners will learn more if they are actively engaged or involved in the learning, rather than simply (receptively) looking up a gloss. So, it has been suggested that cognitive engagement can be stimulated by turning the glosses into a multiple-choice task, and a fair amount of research has investigated this possibility. Barcroft (2015: 143) reports research that suggests that ‘multiple-choice glosses [are] more effective than single glosses’, but Nation (2013: 246) argues that ‘multiple choice glosses are not strongly supported by research’. Basically, we don’t know and even if we have replication studies to re-assess the benefits of multimodal glosses (as advocated by Boers et al, 2017), it is again likely that learner variables will make it impossible to reach a firm conclusion.

Learning from meta-analyses

Discussion of glosses is not new. Back in the late 19th century, ‘most of the Reform Movement teachers, took the view that glossing was a sensible technique’ (Howatt, 2004: 191). Sensible, but probably not all that important in the broader scheme of language learning and teaching. Online glosses offer a number of potential advantages, but there is a huge number of variables that need to be considered if the potential is to be realised. In essence, I have been arguing that asking whether online glosses are more effective than print glosses is the wrong question. It’s not a question that can provide us with a useful answer. When you look at the details of the research that has been brought together in the meta-analysis, you simply cannot conclude that there are unequivocally positive effects of technology on language learning, if the most positive effects are to be found in the digital variation of an old sensible technique.

Interesting and useful as Plonsky and Ziegler’s study is, I think it needs to be treated with caution. More generally, we need to be cautious about using meta-analyses and effect sizes. Mura Nava has a useful summary of an article by Adrian Simpson (Simpson, 2017), that looks at inclusion criteria and statistical procedures and warns us that we cannot necessarily assume that the findings of meta-meta-analyses are educationally significant. More directly related to technology and language learning, Boulton’s paper (Boulton, 2016) makes a similar point: ‘Meta-analyses need interpreting with caution: in particular, it is tempting to seize on a single figure as the ultimate answer to the question: Does it work? […] More realistically, we need to look at variation in what works’.

For me, the greatest value in Plonsky and Ziegler’s paper was nothing to do with effect sizes and big answers to big questions. It was the bibliography … and the way it forced me to be rather more critical about meta-analyses.

References

Barcroft, J. 2015. Lexical Input Processing and Vocabulary Learning. Amsterdam: John Benjamins

Boers, F., Warren, P., He, L. & Deconinck, J. 2017. ‘Does adding pictures to glosses enhance vocabulary uptake from reading?’ System 66: 113 – 129

Boulton, A. 2016. ‘Quantifying CALL: significance, effect size and variation’ in S. Papadima-Sophocleus, L. Bradley & S. Thouësny (eds.) CALL Communities and Culture – short papers from Eurocall 2016 pp.55 – 60 http://files.eric.ed.gov/fulltext/ED572012.pdf

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J. & Willingham, D.T. 2013. ‘Improving Students’ Learning With Effective Learning Techniques’ Psychological Science in the Public Interest 14 / 1: 4 – 58

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

Howatt, A.P.R. 2004. A History of English Language Teaching 2nd edition. Oxford: Oxford University Press

Khezrlou, S., Ellis, R. & K. Sadeghi 2017. ‘Effects of computer-assisted glosses on EFL learners’ vocabulary acquisition and reading comprehension in three learning conditions’ System 65: 104 – 116

Laufer, B. & Shmueli, K. 1997. ‘Memorizing new words: Does teaching have anything to do with it?’ RELC Journal 28 / 1: 89 – 108

Nation, I.S.P. 2013. Learning Vocabulary in Another Language. Cambridge: Cambridge University Press

Plonsky, L. & Ziegler, N. 2016. ‘The CALL–SLA interface:  insights from a second-order synthesis’ Language Learning & Technology 20 / 2: 17 – 37

Simpson, A. 2017. ‘The misdirection of public policy: Comparing and combining standardised effect sizes’ Journal of Education Policy, 32 / 4: 450-466

Taylor, A. M. 2006. ‘The effects of CALL versus traditional L1 glosses on L2 reading comprehension’. CALICO Journal, 23, 309–318.

Taylor, A. M. 2009. ‘CALL-based versus paper-based glosses: Is there a difference in reading comprehension?’ CALICO Journal, 23, 147–160.

Taylor, A. M. 2013. CALL versus paper: In which context are L1 glosses more effective? CALICO Journal, 30, 63-8

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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

 

 

 

Introduction

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

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

Historical attempts to facilitate self-paced learning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Lessons to be learned

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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’.

Google_trends

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’.

future-ready-learning-reimagining-the-role-of-technology-in-education-1-638In 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 2nd 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

It’s a good time to be in Turkey if you have digital ELT products to sell. Not so good if you happen to be an English language learner. This post takes a look at both sides of the Turkish lira.

OUP, probably the most significant of the big ELT publishers in Turkey, recorded ‘an outstanding performance’ in the country in the last financial year, making it their 5th largest ELT market. OUP’s annual report for 2013 – 2014 describes the particularly strong demand for digital products and services, a demand which is now influencing OUP’s global strategy for digital resources. When asked about the future of ELT, Peter Marshall , Managing Director of OUP’s ELT Division, suggested that Turkey was a country that could point us in the direction of an answer to the question. Marshall and OUP will be hoping that OUP’s recently launched Digital Learning Platform (DLP) ‘for the global distribution of adult and secondary ELT materials’ will be an important part of that future, in Turkey and elsewhere. I can’t think of any good reason for doubting their belief.

tbl-ipad1OUP aren’t the only ones eagerly checking the pound-lira exchange rates. For the last year, CUP also reported ‘significant sales successes’ in Turkey in their annual report . For CUP, too, it was a year in which digital development has been ‘a top priority’. CUP’s Turkish success story has been primarily driven by a deal with Anadolu University (more about this below) to provide ‘a print and online solution to train 1.7 million students’ using their Touchstone course. This was the biggest single sale in CUP’s history and has inspired publishers, both within CUP and outside, to attempt to emulate the deal. The new blended products will, of course, be adaptive.

Just how big is the Turkish digital ELT pie? According to a 2014 report from Ambient Insight , revenues from digital ELT products reached $32.0 million in 2013. They are forecast to more than double to $72.6 million in 2018. This is a growth rate of 17.8%, a rate which is practically unbeatable in any large economy, and Turkey is the 17th largest economy in the world, according to World Bank statistics .

So, what makes Turkey special?

  • Turkey has a large and young population that is growing by about 1.4% each year, which is equivalent to approximately 1 million people. According to the Turkish Ministry of Education, there are currently about 5.5 million students enrolled in upper-secondary schools. Significant growth in numbers is certain.
  • Turkey is currently in the middle of a government-sponsored $990 million project to increase the level of English proficiency in schools. The government’s target is to position the country as one of the top ten global economies by 2023, the centenary of the Turkish Republic, and it believes that this position will be more reachable if it has a population with the requisite foreign language (i.e. English) skills. As part of this project, the government has begun to introduce English in the 1st grade (previously it was in the 4th grade).
  • The level of English in Turkey is famously low and has been described as a ‘national weakness’. In October/November 2011, the Turkish research institute SETA and the Turkish Ministry for Youth and Sports conducted a large survey across Turkey of 10,174 young citizens, aged 15 to 29. The result was sobering: 59 per cent of the young people said they “did not know any foreign language.” A recent British Council report (2013) found the competence level in English of most (90+%) students across Turkey was evidenced as rudimentary – even after 1000+ hours (estimated at end of Grade 12) of English classes. This is, of course, good news for vendors of English language learning / teaching materials.
  • Turkey has launched one of the world’s largest educational technology projects: the FATIH Project (The Movement to Enhance Opportunities and Improve Technology). One of its objectives is to provide tablets for every student between grades 5 and 12. At the same time, according to the Ambient report , the intention is to ‘replace all print-based textbooks with digital content (both eTextbooks and online courses).’
  • Purchasing power in Turkey is concentrated in a relatively small number of hands, with the government as the most important player. Institutions are often very large. Anadolu University, for example, is the second largest university in the world, with over 2 million students, most of whom are studying in virtual classrooms. There are two important consequences of this. Firstly, it makes scalable, big-data-driven LMS-delivered courses with adaptive software a more attractive proposition to purchasers. Secondly, it facilitates the B2B sales model that is now preferred by vendors (including the big ELT publishers).
  • Turkey also has a ‘burgeoning private education sector’, according to Peter Marshall, and a thriving English language school industry. According to Ambient ‘commercial English language learning in Turkey is a $400 million industry with over 600 private schools across the country’. Many of these are grouped into large chains (see the bullet point above).
  • Turkey is also ‘in the vanguard of the adoption of educational technology in ELT’, according to Peter Marshall. With 36 million internet users, the 5th largest internet population in Europe, and the 3rd highest online engagement in Europe, measured by time spent online, (reported by Sina Afra ), the country’s enthusiasm for educational technology is not surprising. Ambient reports that ‘the growth rate for mobile English educational apps is 27.3%’. This enthusiasm is reflected in Turkey’s thriving ELT conference scene. The most popular conference themes and conference presentations are concerned with edtech. A keynote speech by Esat Uğurlu at the ISTEK schools 3rd international ELT conference at Yeditepe in April 2013 gives a flavour of the current interests. The talk was entitled ‘E-Learning: There is nothing to be afraid of and plenty to discover’.

All of the above makes Turkey a good place to be if you’re selling digital ELT products, even though the competition is pretty fierce. If your product isn’t adaptive, personalized and gamified, you may as well not bother.

What impact will all this have on Turkey’s English language learners? A report co-produced by TEPAV (the Economic Policy Research Foundation of Turkey) and the British Council in November 2013 suggests some of the answers, at least in the school population. The report  is entitled ‘Turkey National Needs Assessment of State School English Language Teaching’ and its Executive Summary is brutally frank in its analysis of the low achievements in English language learning in the country. It states:

The teaching of English as a subject and not a language of communication was observed in all schools visited. This grammar-based approach was identified as the first of five main factors that, in the opinion of this report, lead to the failure of Turkish students to speak/ understand English on graduation from High School, despite having received an estimated 1000+ hours of classroom instruction.

In all classes observed, students fail to learn how to communicate and function independently in English. Instead, the present teacher-centric, classroom practice focuses on students learning how to answer teachers’ questions (where there is only one, textbook-type ‘right’ answer), how to complete written exercises in a textbook, and how to pass a grammar-based test. Thus grammar-based exams/grammar tests (with right/wrong answers) drive the teaching and learning process from Grade 4 onwards. This type of classroom practice dominates all English lessons and is presented as the second causal factor with respect to the failure of Turkish students to speak/understand English.

The problem, in other words, is the curriculum and the teaching. In its recommendations, the report makes this crystal clear. Priority needs to be given to developing a revised curriculum and ‘a comprehensive and sustainable system of in-service teacher training for English teachers’. Curriculum renewal and programmes of teacher training / development are the necessary prerequisites for the successful implementation of a programme of educational digitalization. Unfortunately, research has shown again and again that these take a long time and outcomes are difficult to predict in advance.

By going for digitalization first, Turkey is taking a huge risk. What LMSs, adaptive software and most apps do best is the teaching of language knowledge (grammar and vocabulary), not the provision of opportunities for communicative practice (for which there is currently no shortage of opportunity … it is just that these opportunities are not being taken). There is a real danger, therefore, that the technology will push learning priorities in precisely the opposite direction to that which is needed. Without significant investments in curriculum reform and teacher training, how likely is it that the transmission-oriented culture of English language teaching and learning will change?

Even if the money for curriculum reform and teacher training were found, it is also highly unlikely that effective country-wide approaches to blended learning for English would develop before the current generation of tablets and their accompanying content become obsolete.

Sadly, the probability is, once more, that educational technology will be a problem-changer, even a problem-magnifier, rather than a problem-solver. I’d love to be wrong.

The cheer-leading for big data in education continues unabated. Almost everything you read online on the subject is an advertisement, usually disguised as a piece of news or a blog post, but which can invariably be traced back to an organisation with a vested interest in digital disruption.  A typical example is this advergraphic which comes under a banner that reads ‘Big Data Improves Education’. The site, Datafloq, is selling itself as ‘the one-stop-shop around Big Data.’ Their ‘vision’ is ‘Connecting Data and People and [they] aim to achieve that by spurring the understanding, acceptance and application of Big Data in order to drive innovation and economic growth.’

Critical voices are rare, but growing. There’s a very useful bibliography of recent critiques here. And in the world of English language teaching, I was pleased to see that there’s a version of Gavin Dudeney’s talk, ‘Of Big Data & Little Data’, now up on YouTube. The slides which accompany his talk can be accessed here.

His main interest is in reclaiming the discourse of edtech in ELT, in moving away from the current obsession with numbers, and in returning the focus to what he calls ‘old edtech’ – the everyday technological practices of the vast majority of ELT practitioners.2014-12-01_2233

It’s a stimulating and deadpan-entertaining talk and well worth 40 minutes of your time. Just fast-forward the bit when he talks about me.

If you’re interested in hearing more critical voices, you may also like to listen to a series of podcasts, put together by the IATEFL Learning Technologies and Global Issues Special Interest Groups. In the first of these, I interview Neil Selwyn and, in the second, Lindsay Clandfield interviews Audrey Watters of Hack Education.

 

(This post won’t make a lot of sense unless you read the previous two – Researching research: part 1 and part 2!)

The work of Jayaprakash et al was significantly informed and inspired by the work done at Purdue University. In the words of these authors, they even ‘relied on [the] work at Purdue with Course Signals’ for parts of the design of their research. They didn’t know when they were doing their research that the Purdue studies were fundamentally flawed. This was, however, common knowledge (since September 2013) before their article (‘Early Alert of Academically At-Risk Students’) was published. This raises the interesting question of why the authors (and the journal in which they published) didn’t pull the article when they could still have done so. I can’t answer that question, but I can suggest some possible reasons. First, though, a little background on the Purdue research.

The Purdue research is important, more than important, because it was the first significant piece of research to demonstrate the efficacy of academic analytics. Except that, in all probability, it doesn’t! Michael Caulfield, director of blended and networked learning at Washington State University at Vancouver, and Alfred Essa, McGraw-Hill Education’s vice-president of research and development and analytics, took a closer look at the data. What they found was that the results were probably the result of selection bias rather than a real finding. In other words, as summarized by Carl Straumsheim in Inside Higher Ed in November of last year, there was no causal connection between students who use [Course Signals] and their tendency to stick with their studies .The Times Higher Education and the e-Literate blog contacted Purdue, but, to date, there has been no serious response to the criticism. The research is still on Purdue’s website .

The Purdue research article, ‘Course Signals at Purdue: Using Learning Analytics to Increase Student Success’ by Kimberley Arnold and Matt Pistilli, was first published as part of the proceedings of the Learning Analytics and Knowledge (LAK) conference in May 2012. The LAK conference is organised by the Society for Learning Analytics Research (SoLAR), in partnership with Purdue. SoLAR, you may remember, is the organisation which published the new journal in which Jayaprakash et al’s article appeared. Pistilli happens to be an associate editor of the journal. Jayaprakash et al also presented at the LAK ’12 conference. Small world.

The Purdue research was further publicized by Pistilli and Arnold in the Educause review. Their research had been funded by the Gates Foundation (a grant of $1.2 million in November 2011). Educause, in its turn, is also funded by the Gates Foundation (a grant of $9 million in November 2011). The research of Jayaprakash et al was also funded by Educause, which stipulated that ‘effective techniques to improve student retention be investigated and demonstrated’ (my emphasis). Given the terms of their grant, we can perhaps understand why they felt the need to claim they had demonstrated something.

What exactly is Educause, which plays such an important role in all of this? According to their own website, it is a non-profit association whose mission is to advance higher education through the use of information technology. However, it is rather more than that. It is also a lobbying and marketing umbrella for edtech. The following screenshot from their website makes this abundantly clear.educause

If you’ll bear with me, I’d like to describe one more connection between the various players I’ve been talking about. Purdue’s Couse Signals is marketed by a company called Ellucian. Ellucian’s client list includes both Educause and the Gates Foundation. A former Senior Vice President of Ellucian, Anne K Keehn, is currently ‘Senior Fellow -Technology and Innovation, Education, Post-Secondary Success’ at the Gates Foundation – presumably the sort of person to whom you’d have to turn if you wanted funding from the Gates Foundation. Small world.

Personal, academic and commercial networks are intricately intertwined in the high-stakes world of edtech. In such a world (not so very different from the pharmaceutical industry), independent research is practically impossible. The pressure to publish positive research results must be extreme. The temptation to draw conclusions of the kind that your paymasters are looking for must be high. Th edtech juggernaut must keep rolling on.

While the big money will continue to go, for the time being, into further attempts to prove that big data is the future of education, there are still some people who are interested in alternatives. Coincidentally (?), a recent survey  has been carried out at Purdue which looks into what students think about their college experience, about what is meaningful to them. Guess what? It doesn’t have much to do with technology.

Back in the Neanderthal days before Web 2.0, iPhones, tablets, the cloud, learning analytics and so on, Chris Bigum and Jane Kenway wrote a paper called ‘New Information Technologies and the Ambiguous Future of Schooling’. Although published in 1998, it remains relevant and can be accessed here.

They analysed the spectrum of discourse that was concerned with new technologies in education. At one end of this spectrum was a discourse community which they termed ‘boosters’. Then, as now, the boosters were far and away the dominant voices. Bigum and Kenway characterized the boosters as having an ‘unswerving faith in the technology’s capacity to improve education and most other things in society’. I discussed the boosterist discourse in my post on this blog, ‘Saving the World (adaptive marketing)’, focussing on the language of Knewton, as a representative example.

At the other end of Bigum and Kenway’s spectrum was what they termed ‘doomsters’ – ‘unqualified opponents of new technologies’ who see inevitable damage to society and education if we uncritically accept these new technologies.

Since starting this blog, I have been particularly struck by two things. The first of these is that I have had to try to restrain my aversion to the excesses of boosterist discourse – not always, it must be said, with complete success. The second is that I have found myself characterized by some people (perhaps those who have only superficially read a post of two) as an anti-technology doomsterist. At the same time, I have noticed that the debate about adaptive learning and educational technology, in general, tends to become polarized into booster and doomster camps.

To some extent, such polarization is inevitable. When a discourse is especially dominant, anyone who questions it risks finding themselves labelled as the extreme opposite. In some parts of the world, for example, any critique of neoliberal doxa is likely to be critiqued, in its turn, as ‘socialist, or worse’: ‘if you’re not with us, you’re against us’.

GramsciWhen it comes to adaptive learning, one can scoff at the adspeak of Knewton or the gapfills of Voxy, without having a problem with the technology per se. But, given the dominance of the booster discourse, one can’t really be neutral. Neil Selwyn (yes, him again!) suggests that the best way of making full sense of educational technology is to adopt a pessimistic perspective. ‘If nothing else,’ he writes, ‘a pessimistic view remains true to the realities of what has actually taken place with regards to higher education and digital technology over the past thirty years (to be blunt, things have clearly not been transformed or improved by digital technology so far, so why should we expect anything different in the near future?)’. This is not an ‘uncompromising pessimism’, but ‘a position akin to Gramsci’s notion of being ‘a pessimist because of intelligence, but an optimist because of will’’.

Note: The quotes from Neil Selwyn here are taken from his new book Digital Technology and the Contemporary University (2014, Abingdon: Routledge). In the autumn of this year, there will be an online conference, jointly organised by the Learning Technologies and Global Issues Special Interest Groups of IATEFL, during which I will be interviewing Neil Selwyn. I’ll keep you posted.

In Part 9 of the ‘guide’ on this blog (neo-liberalism and solutionism), I suggested that the major advocates of adaptive learning form a complex network of vested neo-liberal interests. Along with adaptive learning and the digital delivery of educational content, they promote a free-market, for-profit, ‘choice’-oriented (charter schools in the US and academies in the UK) ideology. The discourses of these advocates are explored in a fascinating article by Neil Selwyn, ‘Discourses of digital ‘disruption’ in education: a critical analysis’ which can be accessed here.

Stephen Ball includes a detailed chart of this kind of network in his ‘Global Education Inc.’ (Routledge 2012). I thought it would be interesting to attempt a similar, but less ambitious, chart of my own. Sugata Mitra’s plenary talk at the IATEFL conference yesterday has generated a lot of discussion, so I thought it would be interesting to focus on him. What such charts demonstrate very clearly is that there is a very close interlinking between EdTech advocacy and a wider raft of issues on the neo-liberal wish list. Adaptive learning developments (or, for example, schools in the cloud) need to be understood in a broader context … in the same way that Mitra, Tooley, Gates et al understand these technologies.

In order to understand the chart, you will need to look at the notes below. Many more nodes could be introduced, but I have tried my best to keep things simple. All of the information here is publicly available, but I found Stephen Ball’s work especially helpful.

mitra chart

People

Bill Gates is the former chief executive and chairman of Microsoft, co-chair of the Bill and Melinda Gates Foundation.

James Tooley is the Director of the E.G. West Centre. He is a founder of the Educare Trust, founder and chairman of Omega Schools, president of Orient Global, chairman of Rumi School of Excellence, and a former consultant to the International Finance Corporation. He is also a member of the advisory council of the Institute of Economic Affairs and was responsible for creating the Education and Training Unit at the Institute.

Michael Barber is Pearson’s Chief Education Advisor and Chairman of Pearson’s $15 million Affordable Learning Fund. He is also an advisor on ‘deliverology’ to the International Finance Corporation.

Sugata Mitra is Professor of Educational Technology at the E.G. West Centre and he is Chief Scientist, Emeritus, at NIIT. He is best known for his “Hole in the Wall” experiment. In 2013, he won the $1 million TED Prize to develop his idea of a ‘school-in-the-cloud’.

Institutions

Hiwel (Hole-in-the-Wall Education Limited) is the company behind Mitra’s “Hole in the Wall” experiment. It is a subsidiary of NIIT.

NIIT Limited is an Indian company based in Gurgaon, India that operates several for-profit higher education institutions.

Omega Schools is a privately held chain of affordable, for-profit schools based in Ghana.There are currently 38 schools educating over 20,000 students.

Orient Global is a Singapore-based investment group, which bought a $48 million stake in NIIT.

Pearson is … Pearson. Pearson’s Affordable Learning Fund was set up to invest in private companies committed to innovative approaches. Its first investment was a stake in Omega Schools.

Rumi Schools of Excellence is Orient Global’s chain of low-cost private schools in India, which aims to extend access and improve educational quality through affordable private schooling.

School-in-the-cloud is described by Mitra as’ a learning lab in India, where children can embark on intellectual adventures by engaging and connecting with information and mentoring online’. Microsoft are the key sponsors.

The E.G. West Centre of the University of Newcastle is dedicated to generating knowledge and understanding about how markets and self organising systems work in education.

The Educare Trustis a non-profit agency, formed in 2002 by Professor James Tooley of the University of Newcastle Upon Tyne, England, and other members associated with private unaided schools in India.It is advised by an international team from the University of Newcastle. It services include the running of a loan scheme for schools to improve their infrastructure and facilities.

The Institute of Economic Affairs is a right-wing free market think tank in London whose stated mission is to improve understanding of the fundamental institutions of a free society by analysing and expounding the role of markets in solving economic and social problems.

The International Finance Corporation is an international financial institution which offers investment, advisory, and asset management services to encourage private sector development in developing countries. The IFC is a member of the World Bank Group.

The Templeton Foundation is a philanthropic organization that funds inter-disciplinary research about human purpose and ultimate reality. Described by Barbara Ehrenreich as a ‘right wing venture’, it has a history of supporting the Cato Institute (publishers of Tooley’s most well-known book) , a libertarian think-tank, as well as projects at major research centers and universities that explore themes related to free market economics.

Additional connections

Barber is an old friend of Tooley’s from when both men were working in Zimbabwe in the 1990s.

Omega Schools are taking part in Sugata Mitra’s TED Prize Schools in the Cloud project.

Omega Schools use textbooks developed by Pearson.

Orient Global sponsored an Education Development fund at Newcastle University. The project leaders were Tooley and Mitra. They also sponsored the Hole-in-the-Wall experiment.

Pearson, the Pearson Foundation, Microsoft and the Gates Foundation work closely together on a wide variety of projects.

Some of Tooley’s work for the Educare Trust was funded by the Templeton Trust. Tooley was also winner of the 2006 Templeton Freedom Prize for Excellence.

The International Finance Corporation and the Gates Foundation are joint sponsors of a $60 million project to improve health in Nigeria.

The International Finance Corporation was another sponsor of the Hole-in-the-Wall experiment.

I mentioned the issue of privacy very briefly in Part 9 of the ‘Guide’, and it seems appropriate to take a more detailed look.

Adaptive learning needs big data. Without the big data, there is nothing for the algorithms to work on, and the bigger the data set, the better the software can work. Adaptive language learning will be delivered via a platform, and the data that is generated by the language learner’s interaction with the English language program on the platform is likely to be only one, very small, part of the data that the system will store and analyse. Full adaptivity requires a psychometric profile for each student.

It would make sense, then, to aggregate as much data as possible in one place. Besides the practical value in massively combining different data sources (in order to enhance the usefulness of the personalized learning pathways), such a move would possibly save educational authorities substantial amounts of money and allow educational technology companies to mine the rich goldmine of student data, along with the standardised platform specifications, to design their products.

And so it has come to pass. The Gates Foundation (yes, them again) provided most of the $100 million funding. A division of Murdoch’s News Corp built the infrastructure. Once everything was ready, a non-profit organization called inBloom was set up to run the thing. The inBloom platform is open source and the database was initially free, although this will change. Preliminary agreements were made with 7 US districts and involved millions of children. The data includes ‘students’ names, birthdates, addresses, social security numbers, grades, test scores, disability status, attendance, and other confidential information’ (Ravitch, D. ‘Reign of Error’ NY: Knopf, 2013, p. 235-236). Under federal law, this information can be ‘shared’ with private companies selling educational technology and services.

The edtech world rejoiced. ‘This is going to be a huge win for us’, said one educational software provider; ‘it’s a godsend for us,’ said another. Others are not so happy. If the technology actually works, if it can radically transform education and ‘produce game-changing outcomes’ (as its proponents claim so often), the price to be paid might just conceivably be worth paying. But the price is high and the research is not there yet. The price is privacy.

The problem is simple. InBloom itself acknowledges that it ‘cannot guarantee the security of the information stored… or that the information will not be intercepted when it is being transmitted.’ Experience has already shown us that organisations as diverse as the CIA or the British health service cannot protect their data. Hackers like a good challenge. So do businesses.

The anti-privatization (and, by extension, the anti-adaptivity) lobby in the US has found an issue which is resonating with electors (and parents). These dissenting voices are led by Class Size Matters, and their voice is being heard. Of the original partners of inBloom, only one is now left. The others have all pulled out, mostly because of concerns about privacy, although the remaining partner, New York, involves personal data on 2.7 million students, which can be shared without any parental notification or consent.

inbloom-student-data-bill-gates

This might seem like a victory for the anti-privatization / anti-adaptivity lobby, but it is likely to be only temporary. There are plenty of other companies that have their eyes on the data-mining opportunities that will be coming their way, and Obama’s ‘Race to the Top’ program means that the inBloom controversy will be only a temporary setback. ‘The reality is that it’s going to be done. It’s not going to be a little part. It’s going to be a big part. And it’s going to be put in place partly because it’s going to be less expensive than doing professional development,’ says Eva Baker of the Center for the Study of Evaluation at UCLA.

It is in this light that the debate about adaptive learning becomes hugely significant. Class Size Matters, the odd academic like Neil Selwyn or the occasional blogger like myself will not be able to reverse a trend with seemingly unstoppable momentum. But we are, collectively, in a position to influence the way these changes will take place.

If you want to find out more, check out the inBloom and Class Size Matters links. And you might like to read more from the news reports which I have used for information in this post. Of these, the second was originally published by Scientific American (owned by Macmillan, one of the leading players in ELT adaptive learning). The third and fourth are from Education Week, which is funded in part by the Gates Foundation.

http://www.reuters.com/article/2013/03/03/us-education-database-idUSBRE92204W20130303

http://www.salon.com/2013/08/01/big_data_puts_teachers_out_of_work_partner/

http://www.edweek.org/ew/articles/2014/01/08/15inbloom_ep.h33.html

http://blogs.edweek.org/edweek/marketplacek12/2013/12/new_york_battle_over_inBloom_data_privacy_heading_to_court.html