Posts Tagged ‘Cambridge University Press’

The most widely-used and popular tool for language learners is the bilingual dictionary (Levy & Steel, 2015), and the first of its kind appeared about 4,000 years ago (2,000 years earlier than the first monolingual dictionaries), offering wordlists in Sumerian and Akkadian (Wheeler, 2013: 9 -11). Technology has come a long way since the clay tablets of the Bronze Age. Good online dictionaries now contain substantially more information (in particular audio recordings) than their print equivalents of a few decades ago. In addition, they are usually quicker and easier to use, more popular, and lead to retention rates that are comparable to, or better than, those achieved with print (Töpel, 2014). The future of dictionaries is likely to be digital, and paper dictionaries may well disappear before very long (Granger, 2012: 2).

English language learners are better served than learners of other languages, and the number of free, online bilingual dictionaries is now enormous. Speakers of less widely-spoken languages may still struggle to find a good quality service, but speakers of, for example, Polish (with approximately 40 million speakers, and a ranking of #33 in the list of the world’s most widely spoken languages) will find over twenty free, online dictionaries to choose from (Lew & Szarowska, 2017). Speakers of languages that are more widely spoken (Chinese, Spanish or Portuguese, for example) will usually find an even greater range. The choice can be bewildering and neither search engine results nor rankings from app stores can be relied on to suggest the product of the highest quality.

Language teachers are not always as enthusiastic about bilingual dictionaries as their learners. Folse (2004: 114 – 120) reports on an informal survey of English teachers which indicated that 11% did not allow any dictionaries in class at all, 37% allowed monolingual dictionaries and only 5% allowed bilingual dictionaries. Other researchers (e.g. Boonmoh & Nesi, 2008), have found a similar situation, with teachers overwhelmingly recommending the use of a monolingual learner’s dictionary: almost all of their students bought one, but the great majority hardly ever used it, preferring instead a digital bilingual version.

Teachers’ preferences for monolingual dictionaries are usually motivated in part by a fear that their students will become too reliant on translation. Whilst this concern remains widespread, much recent suggests that this fear is misguided (Nation, 2013: 424) and that monolingual dictionaries do not actually lead to greater learning gains than their bilingual counterparts. This is, in part, due to the fact that learners typically use these dictionaries in very limited ways – to see if a word exists, check spelling or look up meaning (Harvey & Yuill, 1997). If they made fuller use of the information (about frequency, collocations, syntactic patterns, etc.) on offer, it is likely that learning gains would be greater: ‘it is accessing multiplicity of information that is likely to enhance retention’ (Laufer & Hill, 2000: 77). Without training, however, this is rarely the case.  With lower-level learners, a monolingual learner’s dictionary (even one designed for Elementary level students) can be a frustrating experience, because until they have reached a vocabulary size of around 2,000 – 3,000 words, they will struggle to understand the definitions (Webb & Nation, 2017: 119).

The second reason for teachers’ preference for monolingual dictionaries is that the quality of many bilingual dictionaries is undoubtedly very poor, compared to monolingual learner’s dictionaries such as those produced by Oxford University Press, Cambridge University Press, Longman Pearson, Collins Cobuild, Merriam-Webster and Macmillan, among others. The situation has changed, however, with the rapid growth of bilingualized dictionaries. These contain all the features of a monolingual learner’s dictionary, but also include translations into the learner’s own language. Because of the wealth of information provided by a good bilingualized dictionary, researchers (e.g. Laufer & Hadar, 1997; Chen, 2011) generally consider them preferable to monolingual or normal bilingual dictionaries. They are also popular with learners. Good bilingualized online dictionaries (such as the Oxford Advanced Learner’s English-Chinese Dictionary) are not always free, but many are, and with some language pairings free software can be of a higher quality than services that incur a subscription charge.

If a good bilingualized dictionary is available, there is no longer any compelling reason to use a monolingual learner’s dictionary, unless it contains features which cannot be found elsewhere. In order to compete in a crowded marketplace, many of the established monolingual learner’s dictionaries do precisely that. Examples of good, free online dictionaries include:

Students need help in selecting a dictionary that is right for them. Without this, many end up using as a dictionary a tool such as Google Translate , which, for all its value, is of very limited use as a dictionary. They need to understand that the most appropriate dictionary will depend on what they want to use it for (receptive, reading purposes or productive, writing purposes). Teachers can help in this decision-making process by addressing the issue in class (see the activity below).

In addition to the problem of selecting an appropriate dictionary, it appears that many learners have inadequate dictionary skills (Niitemaa & Pietilä, 2018). In one experiment (Tono, 2011), only one third of the vocabulary searches in a dictionary that were carried out by learners resulted in success. The reasons for failure include focussing on only the first meaning (or translation) of a word that is provided, difficulty in finding the relevant information in long word entries, an inability to find the lemma that is needed, and spelling errors (when they had to type in the word) (Töpel, 2014). As with monolingual dictionaries, learners often only check the meaning of a word in a bilingual dictionary and fail to explore the wider range of information (e.g. collocation, grammatical patterns, example sentences, synonyms) that is available (Laufer & Kimmel, 1997; Laufer & Hill, 2000; Chen, 2010). This information is both useful and may lead to improved retention.

Most learners receive no training in dictionary skills, but would clearly benefit from it. Nation (2013: 333) suggests that at least four or five hours, spread out over a few weeks, would be appropriate. He suggests (ibid: 419 – 421) that training should encourage learners, first, to look closely at the context in which an unknown word is encountered (in order to identify the part of speech, the lemma that needs to be looked up, its possible meaning and to decide whether it is worth looking up at all), then to help learners in finding the relevant entry or sub-entry (by providing information about common dictionary abbreviations (e.g. for parts of speech, style and register)), and, finally, to check this information against the original context.

Two good resource books full of practical activities for dictionary training are available: ‘Dictionary Activities’ by Cindy Leaney (Cambridge: Cambridge University Press, 2007) and ‘Dictionaries’ by Jon Wright (Oxford: Oxford University Press, 1998). Many of the good monolingual dictionaries offer activity guides to promote effective dictionary use and I have suggested a few activities here.

Activity: Understanding a dictionary

Outline: Students explore the use of different symbols in good online dictionaries.

Level: All levels, but not appropriate for very young learners. The activity ‘Choosing a dictionary’ is a good follow-up to this activity.

1 Distribute the worksheet and ask students to follow the instructions.

act_1

2 Check the answers.

Act_1_key

Activity: Choosing a dictionary

Outline: Students explore and evaluate the features of different free, online bilingual dictionaries.

Level: All levels, but not appropriate for very young learners. The text in stage 3 is appropriate for use with levels A2 and B1. For some groups of learners, you may want to adapt (or even translate) the list of features. It may be useful to do the activity ‘Understanding a dictionary’ before this activity.

1 Ask the class which free, online bilingual dictionaries they like to use. Write some of their suggestions on the board.

2 Distribute the list of features. Ask students to work individually and tick the boxes that are important for them. Ask students to work with a partner to compare their answers.

Act_2

3 Give students a list of free, online bilingual (English and the students’ own language) dictionaries. You can use suggestions from the list below, add the suggestions that your students made in stage 1, or add your own ideas. (For many language pairings, better resources are available than those in the list below.) Give the students the following short text and ask the students to use two of these dictionaries to look up the underlined words. Ask the students to decide which dictionary they found most useful and / or easiest to use.

act_2_text

dict_list

4 Conduct feedback with the whole class.

Activity: Getting more out of a dictionary

Outline: Students use a dictionary to help them to correct a text

Level: Levels B1 and B2, but not appropriate for very young learners. For higher levels, a more complex text (with less obvious errors) would be appropriate.

1 Distribute the worksheet below and ask students to follow the instructions.

act_3

2 Check answers with the whole class. Ask how easy it was to find the information in the dictionary that they were using.

Key

When you are reading, you probably only need a dictionary when you don’t know the meaning of a word and you want to look it up. For this, a simple bilingual dictionary is good enough. But when you are writing or editing your writing, you will need something that gives you more information about a word: grammatical patterns, collocations (the words that usually go with other words), how formal the word is, and so on. For this, you will need a better dictionary. Many of the better dictionaries are monolingual (see the box), but there are also some good bilingual ones.

Use one (or more) of the online dictionaries in the box (or a good bilingual dictionary) and make corrections to this text. There are eleven mistakes (they have been underlined) in total.

References

Boonmoh, A. & Nesi, H. 2008. ‘A survey of dictionary use by Thai university staff and students with special reference to pocket electronic dictionaries’ Horizontes de Linguística Aplicada , 6(2), 79 – 90

Chen, Y. 2011. ‘Studies on Bilingualized Dictionaries: The User Perspective’. International Journal of Lexicography, 24 (2): 161–197

Folse, K. 2004. Vocabulary Myths. Ann Arbor: University of Michigan Press

Granger, S. 2012. Electronic Lexicography. Oxford: Oxford University Press

Harvey, K. & Yuill, D. 1997. ‘A study of the use of a monolingual pedagogical dictionary by learners of English engaged in writing’ Applied Linguistics, 51 (1): 253 – 78

Laufer, B. & Hadar, L. 1997. ‘Assessing the effectiveness of monolingual, bilingual and ‘bilingualized’ dictionaries in the comprehension and production of new words’. Modern Language Journal, 81 (2): 189 – 96

Laufer, B. & M. Hill 2000. ‘What lexical information do L2 learners select in a CALL dictionary and how does it affect word retention?’ Language Learning & Technology 3 (2): 58–76

Laufer, B. & Kimmel, M. 1997. ‘Bilingualised dictionaries: How learners really use them’, System, 25 (3): 361 -369

Leaney, C. 2007. Dictionary Activities. Cambridge: Cambridge University Press

Levy, M. and Steel, C. 2015. ‘Language learner perspectives on the functionality and use of electronic language dictionaries’. ReCALL, 27(2): 177–196

Lew, R. & Szarowska, A. 2017. ‘Evaluating online bilingual dictionaries: The case of popular free English-Polish dictionaries’ ReCALL 29(2): 138–159

Nation, I.S.P. 2013. Learning Vocabulary in Another Language 2nd edition. Cambridge: Cambridge University Press

Niitemaa, M.-L. & Pietilä, P. 2018. ‘Vocabulary Skills and Online Dictionaries: A Study on EFL Learners’ Receptive Vocabulary Knowledge and Success in Searching Electronic Sources for Information’, Journal of Language Teaching and Research, 9 (3): 453-462

Tono, Y. 2011. ‘Application of eye-tracking in EFL learners’ dictionary look-up process research’, International Journal of Lexicography 24 (1): 124–153

Töpel, A. 2014. ‘Review of research into the use of electronic dictionaries’ in Müller-Spitzer, C. (Ed.) 2014. Using Online Dictionaries. Berlin: De Gruyter, pp. 13 – 54

Webb, S. & Nation, P. 2017. How Vocabulary is Learned. Oxford: Oxford University Press

Wheeler, G. 2013. Language Teaching through the Ages. New York: Routledge

Wright, J. 1998. Dictionaries. Oxford: Oxford University Press

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

 

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

by Philip Kerr & Andrew Wickham

from IATEFL 2016 Birmingham Conference Selections (ed. Tania Pattison) Faversham, Kent: IATEFL pp. 75 – 78

ELT publishing, international language testing and private language schools are all industries: products are produced, bought and sold for profit. English language teaching (ELT) is not. It is an umbrella term that is used to describe a range of activities, some of which are industries, and some of which (such as English teaching in high schools around the world) might better be described as public services. ELT, like education more generally, is, nevertheless, often referred to as an ‘industry’.

Education in a neoliberal world

The framing of ELT as an industry is both a reflection of how we understand the term and a force that shapes our understanding. Associated with the idea of ‘industry’ is a constellation of other ideas and words (such as efficacy, productivity, privatization, marketization, consumerization, digitalization and globalization) which become a part of ELT once it is framed as an industry. Repeated often enough, ‘ELT as an industry’ can become a metaphor that we think and live by. Those activities that fall under the ELT umbrella, but which are not industries, become associated with the desirability of industrial practices through such discourse.

The shift from education, seen as a public service, to educational managerialism (where education is seen in industrial terms with a focus on efficiency, free market competition, privatization and a view of students as customers) can be traced to the 1980s and 1990s (Gewirtz, 2001). In 1999, under pressure from developed economies, the General Agreement on Trade in Services (GATS) transformed education into a commodity that could be traded like any other in the marketplace (Robertson, 2006). The global industrialisation and privatization of education continues to be promoted by transnational organisations (such as the World Bank and the OECD), well-funded free-market think-tanks (such as the Cato Institute), philanthro-capitalist foundations (such as the Gates Foundation) and educational businesses (such as Pearson) (Ball, 2012).

Efficacy and learning outcomes

Managerialist approaches to education require educational products and services to be measured and compared. In ELT, the most visible manifestation of this requirement is the current ubiquity of learning outcomes. Contemporary coursebooks are full of ‘can-do’ statements, although these are not necessarily of any value to anyone. Examples from one unit of one best-selling course include ‘Now I can understand advice people give about hotels’ and ‘Now I can read an article about unique hotels’ (McCarthy et al. 2014: 74). However, in a world where accountability is paramount, they are deemed indispensable. The problem from a pedagogical perspective is that teaching input does not necessarily equate with learning uptake. Indeed, there is no reason why it should.

Drawing on the Common European Framework of Reference for Languages (CEFR) for inspiration, new performance scales have emerged in recent years. These include the Cambridge English Scale and the Pearson Global Scale of English. Moving away from the broad six categories of the CEFR, such scales permit finer-grained measurement and we now see individual vocabulary and grammar items tagged to levels. Whilst such initiatives undoubtedly support measurements of efficacy, the problem from a pedagogical perspective is that they assume that language learning is linear and incremental, as opposed to complex and jagged.

Given the importance accorded to the measurement of language learning (or what might pass for language learning), it is unsurprising that attention is shifting towards the measurement of what is probably the most important factor impacting on learning: the teaching. Teacher competency scales have been developed by Cambridge Assessment, the British Council and EAQUALS (Evaluation and Accreditation of Quality Language Services), among others.

The backwash effects of the deployment of such scales are yet to be fully experienced, but the likely increase in the perception of both language learning and teacher learning as the synthesis of granularised ‘bits of knowledge’ is cause for concern.

Digital technology

Digital technology may offer advantages to both English language teachers and learners, but its rapid growth in language learning is the result, primarily but not exclusively, of the way it has been promoted by those who stand to gain financially. In education, generally, and in English language teaching, more specifically, advocacy of the privatization of education is always accompanied by advocacy of digitalization. The global market for digital English language learning products was reported to be $2.8 billion in 2015 and is predicted to reach $3.8 billion by 2020 (Ambient Insight, 2016).

In tandem with the increased interest in measuring learning outcomes, there is fierce competition in the market for high-stakes examinations, and these are increasingly digitally delivered and marked. In the face of this competition and in a climate of digital disruption, companies like Pearson and Cambridge English are developing business models of vertical integration where they can provide and sell everything from placement testing, to courseware (either print or delivered through an LMS), teaching, assessment and teacher training. Huge investments are being made in pursuit of such models. Pearson, for example, recently bought GlobalEnglish, Wall Street English, and set up a partnership with Busuu, thus covering all aspects of language learning from resources provision and publishing to off- and online training delivery.

As regards assessment, the most recent adult coursebook from Cambridge University Press (in collaboration with Cambridge English Language Assessment), ‘Empower’ (Doff, et. Al, 2015) sells itself on a combination of course material with integrated, validated assessment.

Besides its potential for scalability (and therefore greater profit margins), the appeal (to some) of platform-delivered English language instruction is that it facilitates assessment that is much finer-grained and actionable in real time. Digitization and testing go hand in hand.

Few English language teachers have been unaffected by the move towards digital. In the state sectors, large-scale digitization initiatives (such as the distribution of laptops for educational purposes, the installation of interactive whiteboards, the move towards blended models of instruction or the move away from printed coursebooks) are becoming commonplace. In the private sectors, online (or partially online) language schools are taking market share from the traditional bricks-and-mortar institutions.

These changes have entailed modifications to the skill-sets that teachers need to have. Two announcements at this conference reflect this shift. First of all, Cambridge English launched their ‘Digital Framework for Teachers’, a matrix of six broad competency areas organised into four levels of proficiency. Secondly, Aqueduto, the Association for Quality Education and Training Online, was launched, setting itself up as an accreditation body for online or blended teacher training courses.

Teachers’ pay and conditions

In the United States, and likely soon in the UK, the move towards privatization is accompanied by an overt attack on teachers’ unions, rights, pay and conditions (Selwyn, 2014). As English language teaching in both public and private sectors is commodified and marketized it is no surprise to find that the drive to bring down costs has a negative impact on teachers worldwide. Gwynt (2015), for example, catalogues cuts in funding, large-scale redundancies, a narrowing of the curriculum, intensified workloads (including the need to comply with ‘quality control measures’), the deskilling of teachers, dilapidated buildings, minimal resources and low morale in an ESOL department in one British further education college. In France, a large-scale study by Wickham, Cagnol, Wright and Oldmeadow (Linguaid, 2015; Wright, 2016) found that EFL teachers in the very competitive private sector typically had multiple employers, limited or no job security, limited sick pay and holiday pay, very little training and low hourly rates that were deteriorating. One of the principle drivers of the pressure on salaries is the rise of online training delivery through Skype and other online platforms, using offshore teachers in low-cost countries such as the Philippines. This type of training represents 15% in value and up to 25% in volume of all language training in the French corporate sector and is developing fast in emerging countries. These examples are illustrative of a broad global trend.

Implications

Given the current climate, teachers will benefit from closer networking with fellow professionals in order, not least, to be aware of the rapidly changing landscape. It is likely that they will need to develop and extend their skill sets (especially their online skills and visibility and their specialised knowledge), to differentiate themselves from competitors and to be able to demonstrate that they are in tune with current demands. More generally, it is important to recognise that current trends have yet to run their full course. Conditions for teachers are likely to deteriorate further before they improve. More than ever before, teachers who want to have any kind of influence on the way that marketization and industrialization are shaping their working lives will need to do so collectively.

References

Ambient Insight. 2016. The 2015-2020 Worldwide Digital English Language Learning Market. http://www.ambientinsight.com/Resources/Documents/AmbientInsight_2015-2020_Worldwide_Digital_English_Market_Sample.pdf

Ball, S. J. 2012. Global Education Inc. Abingdon, Oxon.: Routledge

Doff, A., Thaine, C., Puchta, H., Stranks, J. and P. Lewis-Jones 2015. Empower. Cambridge: Cambridge University Press

Gewirtz, S. 2001. The Managerial School: Post-welfarism and Social Justice in Education. Abingdon, Oxon.: Routledge

Gwynt, W. 2015. ‘The effects of policy changes on ESOL’. Language Issues 26 / 2: 58 – 60

McCarthy, M., McCarten, J. and H. Sandiford 2014. Touchstone 2 Student’s Book Second Edition. Cambridge: Cambridge University Press

Linguaid, 2015. Le Marché de la Formation Langues à l’Heure de la Mondialisation. Guildford: Linguaid

Robertson, S. L. 2006. ‘Globalisation, GATS and trading in education services.’ published by the Centre for Globalisation, Education and Societies, University of Bristol, Bristol BS8 1JA, UK at http://www.bris.ac.uk/education/people/academicStaff/edslr/publications/04slr

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

Wright, R. 2016. ‘My teacher is rich … or not!’ English Teaching Professional 103: 54 – 56

 

 

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 drive towards adaptive learning is being fuelled less by individual learners or teachers than it is by commercial interests, large educational institutions and even larger agencies, including national governments. How one feels about adaptive learning is likely to be shaped by one’s beliefs about how education should be managed.

Huge amounts of money are at stake. Education is ‘a global marketplace that is estimated conservatively to be worth in excess of $5 trillion per annum’ (Selwyn, Distrusting Educational Technology 2013, p.2). With an eye on this pot, in one year, 2012, ‘venture capital funds, private equity investors and transnational corporations like Pearson poured over $1.1 billion into education technology companies’[1] Knewton, just one of a number of adaptive learning companies, managed to raise $54 million before it signed multi-million dollar contracts with ELT publishers like Macmillan and Cambridge University Press. In ELT, some publishing companies are preferring to sit back and wait to see what happens. Most, however, have their sights firmly set on the earnings potential and are fully aware that late-starters may never be able to catch up with the pace-setters.

The nexus of vested interests that is driving the move towards adaptive learning is both tight and complicated. Fuller accounts of this can be found in Stephen Ball’s ‘Education Inc.’ (2012) and Joel Spring’s ‘Education Networks’ (2012) but for this post I hope that a few examples will suffice.

Leading the way is the Bill and Melinda Gates Foundation, the world’s largest private foundation with endowments of almost $40 billion. One of its activities is the ‘Adaptive Learning Market Acceleration Program’ which seeks to promote adaptive learning and claims that the adaptive learning loop can defeat the iron triangle of costs, quality and access (referred to in The Selling Points of Adaptive Learning, above). It is worth noting that this foundation has also funded Teach Plus, an organisation that has been lobbying US ‘state legislatures to eliminate protection of senior teachers during layoffs’ (Spring, 2012, p.51). It also supports the Foundation for Excellence in Education, ‘a major advocacy group for expanding online instruction by changing state laws’ (ibid., p.51). The chairman of this foundation is Jeb Bush, brother of ex-president Bush, who took the message of his foundation’s ‘Digital Learning Now!’ program on the road in 2011. The message, reports Spring (ibid. p.63) was simple: ‘the economic crises provided an opportunity to reduce school budgets by replacing teachers with online courses.’ The Foundation for Excellence in Education is also supported by the Walton Foundation (the Walmart family) and iQity, a company whose website makes clear its reasons for supporting Jeb Bush’s lobbying. ‘The iQity e-Learning Platform is the most complete solution available for the electronic search and delivery of curriculum, courses, and other learning objects. Delivering over one million courses each year, the iQity Platform is a proven success for students, teachers, school administrators, and district offices; as well as state, regional, and national education officials across the country.[2]

Another supporter of the Foundation for Excellence in Education is the Pearson Foundation, the philanthropic arm of Pearson. The Pearson Foundation, in its turn, is supported by the Gates Foundation. In 2011, the Pearson Foundation received funding from the Gates Foundation to create 24 online courses, four of which would be distributed free and the others sold by Pearson the publishers (Spring, 2012, p.66).

The campaign to promote online adaptive learning is massively funded and extremely well-articulated. It receives support from transnational agencies such as the World Bank, WTO and OECD, and its arguments are firmly rooted in the discourse ‘of international management consultancies and education businesses’ (Ball, 2012, p.11-12). It is in this context that observers like Neil Selwyn connect the growing use of digital technologies in education to the corporatisation and globalisation of education and neo-liberal ideology.

Adaptive learning also holds rich promise for those who can profit from the huge amount of data it will generate. Jose Fereira, CEO of Knewton, acknowledges that adaptive learning has ‘the capacity to produce a tremendous amount of data, more than maybe any other industry’[3]. He continues ‘Big data is going to impact education in a big way. It is inevitable. It has already begun. If you’re part of an education organization, you need to have a vision for how you will take advantage of big data. Wait too long and you’ll wake up to find that your competitors (and the instructors that use them) have left you behind with new capabilities and insights that seem almost magical.’ Rather paradoxically, he then concludes that ‘we must all commit to the principle that the data ultimately belong to the students and the schools’. It is not easy to understand how such data can be both the property of individuals and, at the same time, be used by educational organizations to gain competitive advantage.

The existence and exploitation of this data may also raise concerns about privacy. In the same way that many people do not fully understand the extent or purpose of ‘dataveillance’ by cookies when they are browsing the internet, students cannot be expected to fully grasp the extent or potential commercial use of the data that they generate when engaged in adaptive learning programs.

Selwyn (Distrusting Educational Technology 2013, p.59-60) highlights a further problem connected with the arrival of big data. ‘Dataveillance’, he writes, also ‘functions to decrease the influence of ‘human’ experience and judgement, with it no longer seeming to matter what a teacher may personally know about a student in the face of his or her ‘dashboard’ profile and aggregated tally of positive and negative ‘events’. As such, there would seem to be little room for ‘professional’ expertise or interpersonal emotion when faced with such data. In these terms, institutional technologies could be said to be both dehumanizing and deprofessionalizing the relationships between people in an education context – be they students, teachers, administrators or managers.’

Adaptive learning in online and blended programs may well offer a number of advantages, but these will need to be weighed against the replacement or deskilling of teachers, and the growing control of big business over educational processes and content. Does adaptive learning increase the risk of transforming language teaching into a digital diploma mill (Noble, Digital Diploma Mills: The automation of higher education 2002)?

Solutionism

Evgeney Morozov’s 2013 best-seller, ‘To Save Everything, Click Here’, takes issue with our current preoccupation with finding technological solutions to complex and contentious problems. If adaptive learning is being presented as a solution, what is the problem that it is the solution of? In Morosov’s analysis, it is not an educational problem. ‘Digital technologies might be a perfect solution to some problems,’ he writes, ‘but those problems don’t include education – not if by education we mean the development of the skills to think critically about any given issue’ (Morosov, 2013, p.8). Only if we conceive of education as the transmission of bits of information (and in the case of language education as the transmission of bits of linguistic information), could adaptive learning be seen as some sort of solution to an educational problem. The push towards adaptive learning in ELT can be seen, in Morosov’s terms, as reaching ‘for the answer before the questions have been fully asked’ (ibid., p.6).

The world of education has been particularly susceptible to the dreams of a ‘technical fix’. Its history, writes Neil Selwyn, ‘has been characterised by attempts to use the ‘power’ of technology in order to solve problems that are non-technological in nature. […] This faith in the technical fix is pervasive and relentless – especially in the minds of the key interests and opinion formers of this digital age. As the co-founder of the influential Wired magazine reasoned more recently, ‘tools and technology drive us. Even if a problem has been caused by technology, the answer will always be more technology’ (Selwyn, Education in a Digital World 2013, p.36).

Morosov cautions against solutionism in all fields of human activity, pointing out that, by the time a problem is ‘solved’, it becomes something else entirely. Anyone involved in language teaching would be well-advised to identify and prioritise the problems that matter to them before jumping to the conclusion that adaptive learning is the ‘solution’. Like other technologies, it might, just possibly, ‘reproduce, perpetuate, strengthen and deepen existing patterns of social relations and structures – albeit in different forms and guises. In this respect, then, it is perhaps best to approach educational technology as a ‘problem changer’ rather than a ‘problem solver’ (Selwyn, Education in a Digital World 2013, p.21).


[1] Philip McRae Rebirth of the Teaching Machine through the Seduction of Data Analytics: This time it’s personal April 14, 2013 http://philmcrae.com/2/post/2013/04/rebirth-of-the-teaching-maching-through-the-seduction-of-data-analytics-this-time-its-personal1.html (last accessed 13 January 2014)

[2] http://www.iq-ity.com/ (last accessed 13 January, 2014)