Posts Tagged ‘Neil Selwyn’

The idea of ‘digital natives’ emerged at the turn of the century, was popularized by Marc Prensky (2001), and rapidly caught the public imagination, especially the imagination of technology marketers. Its popularity has dwindled a little since then, but is still widely used. Alternative terms include ‘Generation Y’, ‘Millennials’ and the ‘Net Generation’, definitions of which vary slightly from writer to writer. Two examples of the continued currency of the term ‘digital native’ are a 2019 article on the Pearson Global Scale of English website entitled ‘Teaching digital natives to become more human’ and an article in The Pie News (a trade magazine for ‘professionals in international education’), extolling the virtues of online learning for digital natives in times of Covid-19.

Key to understanding ‘digital natives’, according to users of the term, is their fundamental differences from previous generations. They have grown up immersed in technology, have shorter attention spans, and are adept at multitasking. They ‘are no longer the people our educational system was designed to teach’ (Prensky, 2001), so educational systems must change in order to accommodate their needs.

The problem is that ‘digital natives’ are a myth. Prensky’s ideas were not based on any meaningful research: his observations and conclusions, seductive though they might be, were no more than opinions. Kirschner and De Bruyckere (2017) state the research consensus:

There is no such thing as a digital native who is information-skilled simply because (s)he has never known a world that was not digital. […] One of the alleged abilities of students in this generation, the ability to multitask, does not exist and that designing education that assumes the presence of this ability hinders rather than helps learning.

This is neither new (see Bennett et al., 2008) nor contentious. Almost ten years ago, Thomas (2011:3) reported that ‘some researchers have been asked to remove all trace of the term from academic papers submitted to conferences in order to be seriously considered for inclusion’. There are reasons, he added, to consider some uses of the term nothing more than technoevangelism (Thomas, 2011:4). Perhaps someone should tell Pearson and the Pie News? Then, again, perhaps, they wouldn’t care.

The attribution of particular characteristics to ‘digital natives’ / ‘Generation Y’ / ‘Millennials’ is an application of Generation Theory. This can be traced back to a 1928 paper by Karl Mannheim, called ‘Das Problem der Generationen’ which grew in popularity after being translated into English in the 1950s. According to Jauregui et al (2019), the theory was extensively debated in the 1960s and 1970s, but then disappeared from academic study. The theory was not supported by empirical research, was considered to be overly schematised and too culturally-bound, and led inexorably to essentialised and reductive stereotypes.

But Generation Theory gained a new lease of life in the 1990s, following the publication of ‘Generations’ by William Strauss and Neil Howe. The book was so successful that it spawned a slew of other titles leading up to ‘Millennials Rising’ (Howe & Strauss, 2000). This popularity has continued to the present, with fans including Steve Bannon (Kaiser, 2016) who made an ‘apocalyptical and polemical’ documentary film about the 2007 – 2008 financial crisis entitled ‘Generation Zero’. The work of Strauss and Howe has been dismissed as ‘more popular culture than social science’ (Jauregui et al., 2019: 63) and in much harsher terms in two fascinating articles in Jacobin (Hart, 2018) and Aeon (Onion, 2015). The sub-heading of the latter is ‘generational labels are lazy, useless and just plain wrong’. Although dismissed by scholars as pseudo-science, the popularity of such Generation Theory helps explain why Prensky’s paper about ‘digital natives’ fell on such fertile ground. The saying, often falsely attributed to Mark Twain, that we should ‘never let the truth get in the way of a good story’ comes to mind.

But by the end of the first decade of this century, ‘digital natives’ had become problematic in two ways: not only did the term not stand up to close analysis, but it also no longer referred to the generational cohort that pundits and marketers wanted to talk about.

Around January 2018, use of the term ‘Generation Z’ began to soar, and is currently at its highest point ever in the Google Trends graph. As with ‘digital natives’, the precise birth dates of Generation Z vary from writer to writer. After 2001, according to the Cambridge dictionary; slightly earlier according to other sources. The cut-off point is somewhere between the mid and late 2010s. Other terms for this cohort have been proposed, but ‘Generation Z’ is the most popular.

William Strauss died in 2007 and Neil Howe was in his late 60s when ‘Generation Z’ became a thing, so there was space for others to take up the baton. The most successful have probably been Corey Seemiller and Meghan Grace, who, since 2016, have been churning out a book a year devoted to ‘Generation Z’. In the first of these (Seemiller & Grace, 2016), they were clearly keen to avoid some of the criticisms that had been levelled at Strauss and Howe, and they carried out research. This consisted of 1143 responses to a self-reporting questionnaire by students at US institutions of higher education. The survey also collected information about Kolb’s learning styles and multiple intelligences. With refreshing candour, they admit that the sample is not entirely representative of higher education in the US. And, since it only looked at students in higher education, it told us nothing at all about those who weren’t.

In August 2018, Pearson joined the party, bringing out a report entitled ‘Beyond Millennials: The Next Generation of Learners’. Conducted by the Harris Poll, the survey looked at 2,587 US respondents, aged between 14 and 40. The results were weighted for age, gender, race/ethnicity, marital status, household income, and education, so were rather more representative than the Seemiller & Grace research.

In ELT and educational references to ‘Generation Z’, research, of even the very limited kind mentioned above, is rarely cited. When it is, Seemiller and Grace feature prominently (e.g. Mohr & Mohr, 2017). Alternatively, even less reliable sources are used. In an ELT webinar entitled ‘Engaging Generation Z’, for example, information about the characteristics of ‘Generation Z’ learners is taken from an infographic produced by an American office furniture company.

But putting aside quibbles about the reliability of the information, and the fact that it most commonly[1] refers to Americans (who are not, perhaps, the most representative group in global terms), what do the polls tell us?

Despite claims that Generation Z are significantly different from their Millennial predecessors, the general picture that emerges suggests that differences are more a question of degree than substance. These include:

  • A preference for visual / video information over text
  • A variety of bite-sized, entertaining educational experiences
  • Short attention spans and zero tolerance for delay

All of these were identified in 2008 (Williams et al., 2008) as characteristics of the ‘Google Generation’ (a label which usually seems to span Millennials and Generation Z). There is nothing fundamentally different from Prensky’s description of ‘digital natives’. The Pearson report claimed that ‘Generation Z expects experiences both inside and outside the classroom that are more rewarding, more engaging and less time consuming. Technology is no longer a transformative phenomena for this generation, but rather a normal, integral part of life’. However, there is no clear disjuncture or discontinuity between Generation Z and Millennials, any more than there was between ‘digital natives’ and previous generations (Selwyn, 2009: 375). What has really changed is that the technology has moved on (e.g. YouTube was founded in 2005 and the first iPhone was released in 2007).

TESOL TurkeyThe discourse surrounding ‘Generation Z’ is now steadily finding its way into the world of English language teaching. The 2nd TESOL Turkey International ELT Conference took place last November with ‘Teaching Generation Z: Passing on the baton from K12 to University’ as its theme. A further gloss explained that the theme was ‘in reference to the new digital generation of learners with outstanding multitasking skills; learners who can process and absorb information within mere seconds and yet possess the shortest attention span ever’.


A few more examples … Cambridge University Press ran a webinar ELT webinar entitled ‘Engaging Generation Z’ and Macmillan Education has a coursebook series called ‘Exercising English for Generation Z’. EBC, a TEFL training provider, ran a blog post in November last year, ‘Teaching English to generation Z students’. And EFL Magazine had an article, ‘Critical Thinking – The Key Competence For The Z Generation’, in February of this year.

The pedagogical advice that results from this interest in Generation Z seems to boil down to: ‘Accept the digital desires of the learners, use lots of video (i.e. use more technology in the classroom) and encourage multi-tasking’.

No one, I suspect, would suggest that teachers should not make use of topics and technologies that appeal to their learners. But recommendations to change approaches to language teaching, ‘based solely on the supposed demands and needs of a new generation of digital natives must be treated with caution’ (Bennett et al., 2008: 782). It is far from clear that generational differences (even if they really exist) are important enough ‘to be considered during the design of instruction or the use of different educational technologies – at this time, the weight of the evidence is negative’ (Reeves, 2008: 21).

Perhaps, it would be more useful to turn away from surveys of attitudes and towards more fact-based research. Studies in both the US and the UK have found that myopia and other problems with the eyes is rising fast among the Generation Z cohort, and that there is a link with increased screen time, especially with handheld devices. At the same time, Generation Zers are much more likely than their predecessors to be diagnosed with anxiety disorder and depression. While the connection between technology use and mental health is far from clear, it is possible that  ‘the rise of the smartphone and social media have at least something to do with [the rise in mental health issues]’ (Twenge, 2017).

Should we be using more technology in class because learners say they want or need it? If we follow that logic, perhaps we should also be encouraging the consumption of fast food, energy drinks and Ritalin before and after lessons?

[1] Studies have been carried out in other geographical settings, including Europe (e.g. Triple-a-Team AG, 2016) and China (Tang, 2019).


Bennett S., Maton K., & Kervin, L. (2008). The ‘digital natives’ debate: a critical review of the evidence. British Jmournal of Educational Technology, 39 (5):pp. 775-786.

Hart, A. (2018). Against Generational Politics. Jacobin, 28 February 2018.

Howe, N. & Strauss, W. (2000). Millennials Rising: The Next Great Generation. New York, NY: Vintage Books.

Jauregui, J., Watsjold, B., Welsh, L., Ilgen, J. S. & Robins, L. (2019). Generational “othering”: The myth of the Millennial learner. Medical Education,54: pp.60–65.

Kaiser, D. (2016). Donald Trump, Stephen Bannon and the Coming Crisis in American National Life. Time, 18 November 2016.

Kirschner, P.A. & De Bruyckere P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67: pp. 135-142.

Mohr, K. A. J. & Mohr, E. S. (2017). Understanding Generation Z Students to Promote a Contemporary Learning Environment. Journal on Empowering Teacher Excellence, 1 (1), Article 9 DOI:

Onion, R. (2015). Against generations. Aeon, 19 May, 2015.

Pearson (2018). Beyond Millennials: The Next Generation of Learners.

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9: pp. 1- 6

Reeves, T.C. (2008). Do Generational Differences Matter in Instructional Design? Athens, GA: University of Georgia, Department of Educational Psychology and Instructional Technology

Seemiller, C. & and Grace, M. (2016). Generation Z Goes to College. San Francisco: Jossey-Bass

Selwyn, N. (2009). The digital native-myth and reality. Perspectives, 61: pp. 364-379

Strauss W. & Howe, N. (1991). Generations: The History of America’s Future, 1584 to 2069. New York, New York: HarperCollins.

Tang F. (2019). A critical review of research on the work-related attitudes of Generation Z in China. Social Psychology and Society, 10 (2): pp. 19—28. Available at:

Thomas, M. (2011). Technology, Education, and the Discourse of the Digital Native: Between Evangelists and Dissenters. In Thomas, M. (ed). (2011). Deconstructing Digital Natives: Young people, technology and the new literacies. London: Routledge. pp. 1- 13)

Triple-a-Team AG. (2016). Generation Z Metastudie über die kommende Generation. Biglen, Switzerland. Available at:

Twenge, J. M. (2017). iGen. New York: Atria Books

Williams, P., Rowlands, I. & Fieldhouse, M. (2008). The ‘Google Generation’ – myths and realities about young people’s digital information behaviour. In Nicholas, D. & Rowlands, I. (eds.) (2008). Digital Consumers. London: Facet Publishers.

The ‘Routledge Handbook of Language Learning and Technology’ (eds. Farr and Murray, 2016) claims to be ‘the essential reference’ on the topic and its first two sections are devoted to ‘Historical and conceptual concepts’ and ‘Core issues’. One chapter (‘Limitations and boundaries in language learning and technology’ by Kern and Malinowski) mentions that ‘a growing body of research in intercultural communication and online language learning recognises how all technologies are embedded in cultural and linguistic practices, meaning that a given technological artefact can be used in radically different ways, and for different purposes by different groups of people’ (p.205). However, in terms of critical analyses of technology and language learning, that’s about as far as this book goes. In over 500 pages, there is one passing reference to privacy and a couple of brief mentions of the digital divide. There is no meaningful consideration of the costs, ownership or externalities of EdTech, of the ways in which EdTech is sold and marketed, of the vested interests that profit from EdTech, of the connections between EdTech and the privatisation of education, of the non-educational uses to which data is put, or of the implications of attention tracking, facial analysis and dataveillance in educational settings.

The Routledge Handbook is not alone in this respect. Li Li’s ‘New Technologies and Language Learning’ (Palgrave, 2017) is breathlessly enthusiastic about the potential of EdTech. The opening chapter catalogues a series of huge investments in global EdTech, as if the scale of investment was an indication of its wisdom. No mention of the lack of evidence that huge investments into IWBs and PCs in classrooms led to any significant improvement in learning. No mention of how these investments were funded (or which other parts of budgets were cut). Instead, we are told that ‘computers can promote visual, verbal and kinaesthetic learning’ (p.5).

I have never come across a book-length critical analysis of technology and language learning. As the world of language teaching jumps on board Zoom, Google Meet, Microsoft Teams, Skype (aka Microsoft) and the like, the need for a better critical awareness of EdTech and language learning has never been more urgent. Fortunately, there is a growing body of critical literature on technology and general education. Here are my twelve favourites:

Big Data in Education1 Big Data in Education

Ben Williamson (Sage, 2017)

An investigation into the growing digitalization and datafication of education. Williamson looks at how education policy is enacted through digital tools, the use of learning analytics and educational data science. His interest is in the way that technology has reshaped the way we think about education and the book may be read as a critical response to the techno-enthusiasm of Mayer-Schönberger and Cukier’s ‘Learning with Big Data: The Future of Education’ (Houghton Mifflin Harcourt, 2014). Williamson’s blog, Code Acts in Education, is excellent.


Distrusting Educational Technology2 Distrusting Educational Technology

Neil Selwyn (Routledge, 2014)

Neil Selwyn is probably the most widely-quoted critical voice in this field, and this book is as good a place to start with his work as any. EdTech, for Selwyn, is a profoundly political affair, and this book explores the gulf between how it could be used, and how it is actually used. Unpacking the ideological agendas of what EdTech is and does, Selwyn covers the reduction of education along data-driven lines, the deskilling of educational labour, the commodification of learning, issues of inequality, and much more. An essential primer.



The Great American Education Industrial Complex3 The Great American Education-Industrial Complex

Anthony G. Picciano & Joel Spring (Routledge, 2013)

Covering similar ground to both ‘Education Networks’ and ‘’ (see below), this book’s subtitle, ‘Ideology, Technology, and Profit’, says it all. Chapter 4 (‘Technology in American Education’) is of particular interest, tracing the recent history of EdTech and the for-profit sector. Chapter 5 provides a wide range of examples of the growing privatization (through EdTech) of American schooling.



Disruptive Fixation4 Disruptive Fixation

Christo Sims (Princeton University Press, 2017)

The story of a New York school, funded by philanthropists and put together by games designers and educational reformers, that promised to ‘reinvent the classroom for the digital age’. And how it all went wrong … reverting to conventional rote learning with an emphasis on discipline, along with gender and racialized class divisions. A cautionary tale about techno-philanthropism.



Education Networks5 Education Networks

Joel Spring (Routledge, 2012)

Similar in many ways to ‘’ (see below), this is an analysis of the relationships between the interest groups (international agencies, private companies and philanthropic foundations) that are pushing for greater use of EdTech. Spring considers the psychological, social and political implications of the growth of EdTech and concludes with a discussion of the dangers of consumerist approaches to education and dataveillance.




Stephen J. Ball, Carolina Junemann & Diego Santori (Routledge, 2017)

An account of the ways in which international agencies, private companies (e.g. Bridge International Academies, Pearson) and philanthropic foundations shape global education policies, with a particular focus on India and Ghana. These policies include the standardisation of education, the focus on core subjects, the use of corporate management models and test-based accountability, and are key planks in what has been referred to as the Global Education Reform Movement (GERM). Chapter 4 (‘Following things’) focusses on the role of EdTech in realising GERM goals.


Education and Technology7 Education and Technology

Neil Selwyn (Continuum, 2011)

Although covering some similar ground to his ‘Distrusting Educational Technology’, this handy volume summarises key issues, including ‘does technology inevitably change education?’, ‘what can history tell us about education and technology?’, ‘does technology improve learning?’, ‘does technology make education fairer?’, ‘will technology displace the teacher?’ and ‘will technology displace the school?’.



The Evolution of American Educational Technology8 The Evolution of American Educational Technology

Paul Saettler (Information Age, 2004)

A goldmine of historical information, this is the first of three history books on my list. Early educational films from the start of the 20th century, educational radio, teaching machines and programmed instruction, early computer-assisted instruction like the PLATO project, educational broadcasting and television … moving on to interactive video, teleconferencing, and artificial intelligence. A fascinatingly detailed study of educational dreams and obsolescence.


Oversold and Underused9 Oversold and Underused

Larry Cuban (Harvard University Press, 2003)

Larry Cuban’s ground-breaking ‘Teachers and Machines: The Classroom Use of Technology since 1920’ (published in 1986, four years before Saettler’s history) was arguably the first critical evaluation of EdTech. In this title, Cuban pursues his interest in the troubled relationship between teachers and technology, arguing that more attention needs to be paid to the civic and social goals of schooling, goals that make the question of how many computers are in classrooms trivial. Larry Cuban’s blog is well worth following.


The Flickering Mind10 The Flickering Mind

Todd Oppenheimer (Random House, 2003)

A journalistic account of how approximately $70 billion was thrown at EdTech in American schools at the end of the 20th century in an attempt to improve them. It’s a tale of getting the wrong priorities, technological obsolescence and, ultimately, a colossal waste of money. Technology has changed since the writing of this book, but as the epigram of Alphonse Karr (cited by Oppenheimer in his afterword) puts it – ‘plus ça change, plus c’est la même chose’.



Teaching Machines11 Teaching Machines

Bill Ferster (John Hopkins University Press, 2014)

This is the third history of EdTech on my list. A critical look at past attempts to automate instruction, and learning from successes and failures as a way of trying to avoid EdTech insanity (‘doing the same thing over and over again and expecting different results’). Not explicitly political, but the final chapter offers a useful framework for ‘making sense of teaching machines’.



The Technical Fix12 The Technical Fix

Kevin Robbins & Frank Webster (Macmillan, 1989)

Over thirty years old now, this remarkably prescient book situates the push for more EdTech in Britain in the 1980s as a part of broader social and political forces demanding a more market-oriented and entrepreneurial approach to education. The argument that EdTech cannot be extracted from relations of power and the social values that these entail is presented forcefully. Technology, write the authors, ‘is always shaped by, even constitutive of, prevailing values and power distribution’.



And here’s hoping that Audrey Watters’ new book sees the light of day soon, so it can be added to the list of history books!








At the beginning of March, I’ll be going to Cambridge to take part in a Digital Learning Colloquium (for more information about the event, see here ). One of the questions that will be explored is how research might contribute to the development of digital language learning. In this, the first of two posts on the subject, I’ll be taking a broad overview of the current state of play in edtech research.

I try my best to keep up to date with research. Of the main journals, there are Language Learning and Technology, which is open access; CALICO, which offers quite a lot of open access material; and reCALL, which is the most restricted in terms of access of the three. But there is something deeply frustrating about most of this research, and this is what I want to explore in these posts. More often than not, research articles end with a call for more research. And more often than not, I find myself saying ‘Please, no, not more research like this!’

First, though, I would like to turn to a more reader-friendly source of research findings. Systematic reviews are, basically literature reviews which can save people like me from having to plough through endless papers on similar subjects, all of which contain the same (or similar) literature review in the opening sections. If only there were more of them. Others agree with me: the conclusion of one systematic review of learning and teaching with technology in higher education (Lillejord et al., 2018) was that more systematic reviews were needed.

Last year saw the publication of a systematic review of research on artificial intelligence applications in higher education (Zawacki-Richter, et al., 2019) which caught my eye. The first thing that struck me about this review was that ‘out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis’. In other words, only just over 5% of the research was considered worthy of inclusion.

The review did not paint a very pretty picture of the current state of AIEd research. As the second part of the title of this review (‘Where are the educators?’) makes clear, the research, taken as a whole, showed a ‘weak connection to theoretical pedagogical perspectives’. This is not entirely surprising. As Bates (2019) has noted: ‘since AI tends to be developed by computer scientists, they tend to use models of learning based on how computers or computer networks work (since of course it will be a computer that has to operate the AI). As a result, such AI applications tend to adopt a very behaviourist model of learning: present / test / feedback.’ More generally, it is clear that technology adoption (and research) is being driven by technology enthusiasts, with insufficient expertise in education. The danger is that edtech developers ‘will simply ‘discover’ new ways to teach poorly and perpetuate erroneous ideas about teaching and learning’ (Lynch, 2017).

This, then, is the first of my checklist of things that, collectively, researchers need to do to improve the value of their work. The rest of this list is drawn from observations mostly, but not exclusively, from the authors of systematic reviews, and mostly come from reviews of general edtech research. In the next blog post, I’ll look more closely at a recent collection of ELT edtech research (Mavridi & Saumell, 2020) to see how it measures up.

1 Make sure your research is adequately informed by educational research outside the field of edtech

Unproblematised behaviourist assumptions about the nature of learning are all too frequent. References to learning styles are still fairly common. The most frequently investigated skill that is considered in the context of edtech is critical thinking (Sosa Neira, et al., 2017), but this is rarely defined and almost never problematized, despite a broad literature that questions the construct.

2 Adopt a sceptical attitude from the outset

Know your history. Decades of technological innovation in education have shown precious little in the way of educational gains and, more than anything else, have taught us that we need to be sceptical from the outset. ‘Enthusiasm and praise that are directed towards ‘virtual education, ‘school 2.0’, ‘e-learning and the like’ (Selwyn, 2014: vii) are indications that the lessons of the past have not been sufficiently absorbed (Levy, 2016: 102). The phrase ‘exciting potential’, for example, should be banned from all edtech research. See, for example, a ‘state-of-the-art analysis of chatbots in education’ (Winkler & Söllner, 2018), which has nothing to conclude but ‘exciting potential’. Potential is fine (indeed, it is perhaps the only thing that research can unambiguously demonstrate – see section 3 below), but can we try to be a little more grown-up about things?

3 Know what you are measuring

Measuring learning outcomes is tricky, to say the least, but it’s understandable that researchers should try to focus on them. Unfortunately, ‘the vast array of literature involving learning technology evaluation makes it challenging to acquire an accurate sense of the different aspects of learning that are evaluated, and the possible approaches that can be used to evaluate them’ (Lai & Bower, 2019). Metrics such as student grades are hard to interpret, not least because of the large number of variables and the danger of many things being conflated in one score. Equally, or possibly even more, problematic, are self-reporting measures which are rarely robust. It seems that surveys are the most widely used instrument in qualitative research (Sosa Neira, et al., 2017), but these will tell us little or nothing when used for short-term interventions (see point 5 below).

4 Ensure that the sample size is big enough to mean something

In most of the research into digital technology in education that was analysed in a literature review carried out for the Scottish government (ICF Consulting Services Ltd, 2015), there were only ‘small numbers of learners or teachers or schools’.

5 Privilege longitudinal studies over short-term projects

The Scottish government literature review (ICF Consulting Services Ltd, 2015), also noted that ‘most studies that attempt to measure any outcomes focus on short and medium term outcomes’. The fact that the use of a particular technology has some sort of impact over the short or medium term tells us very little of value. Unless there is very good reason to suspect the contrary, we should assume that it is a novelty effect that has been captured (Levy, 2016: 102).

6 Don’t forget the content

The starting point of much edtech research is the technology, but most edtech, whether it’s a flashcard app or a full-blown Moodle course, has content. Research reports rarely give details of this content, assuming perhaps that it’s just fine, and all that’s needed is a little tech to ‘present learners with the ‘right’ content at the ‘right’ time’ (Lynch, 2017). It’s a foolish assumption. Take a random educational app from the Play Store, a random MOOC or whatever, and the chances are you’ll find it’s crap.

7 Avoid anecdotal accounts of technology use in quasi-experiments as the basis of a ‘research article’

Control (i.e technology-free) groups may not always be possible but without them, we’re unlikely to learn much from a single study. What would, however, be extremely useful would be a large, collated collection of such action-research projects, using the same or similar technology, in a variety of settings. There is a marked absence of this kind of work.

8 Enough already of higher education contexts

Researchers typically work in universities where they have captive students who they can carry out research on. But we have a problem here. The systematic review of Lundin et al (2018), for example, found that ‘studies on flipped classrooms are dominated by studies in the higher education sector’ (besides lacking anchors in learning theory or instructional design). With some urgency, primary and secondary contexts need to be investigated in more detail, not just regarding flipped learning.

9 Be critical

Very little edtech research considers the downsides of edtech adoption. Online safety, privacy and data security are hardly peripheral issues, especially with younger learners. Ignoring them won’t make them go away.

More research?

So do we need more research? For me, two things stand out. We might benefit more from, firstly, a different kind of research, and, secondly, more syntheses of the work that has already been done. Although I will probably continue to dip into the pot-pourri of articles published in the main CALL journals, I’m looking forward to a change at the CALICO journal. From September of this year, one issue a year will be thematic, with a lead article written by established researchers which will ‘first discuss in broad terms what has been accomplished in the relevant subfield of CALL. It should then outline which questions have been answered to our satisfaction and what evidence there is to support these conclusions. Finally, this article should pose a “soft” research agenda that can guide researchers interested in pursuing empirical work in this area’. This will be followed by two or three empirical pieces that ‘specifically reflect the research agenda, methodologies, and other suggestions laid out in the lead article’.

But I think I’ll still have a soft spot for some of the other journals that are coyer about their impact factor and that can be freely accessed. How else would I discover (it would be too mean to give the references here) that ‘the effective use of new technologies improves learners’ language learning skills’? Presumably, the ineffective use of new technologies has the opposite effect? Or that ‘the application of modern technology represents a significant advance in contemporary English language teaching methods’?


Bates, A. W. (2019). Teaching in a Digital Age Second Edition. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from

ICF Consulting Services Ltd (2015). Literature Review on the Impact of Digital Technology on Learning and Teaching. Edinburgh: The Scottish Government.

Lai, J.W.M. & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133(1), 27-42. Elsevier Ltd. Retrieved January 14, 2020 from

Levy, M. 2016. Researching in language learning and technology. In Farr, F. & Murray, L. (Eds.) The Routledge Handbook of Language Learning and Technology. Abingdon, Oxon.: Routledge. pp.101 – 114

Lillejord S., Børte K., Nesje K. & Ruud E. (2018). Learning and teaching with technology in higher education – a systematic review. Oslo: Knowledge Centre for Education

Lundin, M., Bergviken Rensfeldt, A., Hillman, T. et al. (2018). Higher education dominance and siloed knowledge: a systematic review of flipped classroom research. International Journal of Educational Technology in Higher Education 15, 20 (2018) doi:10.1186/s41239-018-0101-6

Lynch, J. (2017). How AI Will Destroy Education. Medium, November 13, 2017.

Mavridi, S. & Saumell, V. (Eds.) (2020). Digital Innovations and Research in Language Learning. Faversham, Kent: IATEFL

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

Sosa Neira, E. A., Salinas, J. and de Benito Crosetti, B. (2017). Emerging Technologies (ETs) in Education: A Systematic Review of the Literature Published between 2006 and 2016. International Journal of Emerging Technologies in Education, 12 (5).

Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA.

Zawacki-Richter, O., Bond, M., Marin, V. I. And Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education 2019

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.