Posts Tagged ‘social networking’

At a recent ELT conference, a plenary presentation entitled ‘Getting it right with edtech’ (sponsored by a vendor of – increasingly digital – ELT products) began with the speaker suggesting that technology was basically neutral, that what you do with educational technology matters far more than the nature of the technology itself. The idea that technology is a ‘neutral tool’ has a long pedigree and often accompanies exhortations to embrace edtech in one form or another (see for example Fox, 2001). It is an idea that is supported by no less a luminary than Chomsky, who, in a 2012 video entitled ‘The Purpose of Education’ (Chomsky, 2012), said that:

As far as […] technology […] and education is concerned, technology is basically neutral. It’s kind of like a hammer. I mean, […] the hammer doesn’t care whether you use it to build a house or whether a torturer uses it to crush somebody’s skull; a hammer can do either. The same with the modern technology; say, the Internet, and so on.

Womans hammerAlthough hammers are not usually classic examples of educational technology, they are worthy of a short discussion. Hammers come in all shapes and sizes and when you choose one, you need to consider its head weight (usually between 16 and 20 ounces), the length of the handle, the shape of the grip, etc. Appropriate specifications for particular hammering tasks have been calculated in great detail. The data on which these specifications is based on an analysis of the hand size and upper body strength of the typical user. The typical user is a man, and the typical hammer has been designed for a man. The average male hand length is 177.9 mm, that of the average woman is 10 mm shorter (Wang & Cai, 2017). Women typically have about half the upper body strength of men (Miller et al., 1993). It’s possible, but not easy to find hammers designed for women (they are referred to as ‘Ladies hammers’ on Amazon). They have a much lighter head weight, a shorter handle length, and many come in pink or floral designs. Hammers, in other words, are far from neutral: they are highly gendered.

Moving closer to educational purposes and ways in which we might ‘get it right with edtech’, it is useful to look at the smart phone. The average size of these devices has risen in recent years, and is now 5.5 inches, with the market for 6 inch screens growing fast. Why is this an issue? Well, as Caroline Criado Perez (2019: 159) notes, ‘while we’re all admittedly impressed by the size of your screen, it’s a slightly different matter when it comes to fitting into half the population’s hands. The average man can fairly comfortably use his device one-handed – but the average woman’s hand is not much bigger than the handset itself’. This is despite the fact the fact that women are more likely to own an iPhone than men  .

It is not, of course, just technological artefacts that are gendered. Voice-recognition software is also very biased. One researcher (Tatman, 2017) has found that Google’s speech recognition tool is 13% more accurate for men than it is for women. There are also significant biases for race and social class. The reason lies in the dataset that the tool is trained on: the algorithms may be gender- and socio-culturally-neutral, but the dataset is not. It would not be difficult to redress this bias by training the tool on a different dataset.

The same bias can be found in automatic translation software. Because corpora such as the BNC or COCA have twice as many male pronouns as female ones (as a result of the kinds of text that are selected for the corpora), translation software reflects the bias. With Google Translate, a sentence in a language with a gender-neutral pronoun, such as ‘S/he is a doctor’ is rendered into English as ‘He is a doctor’. Meanwhile, ‘S/he is a nurse’ is translated as ‘She is a nurse’ (Criado Perez, 2019: 166).

Datasets, then, are often very far from neutral. Algorithms are not necessarily any more neutral than the datasets, and Cathy O’Neil’s best-seller ‘Weapons of Math Destruction’ catalogues the many, many ways in which algorithms, posing as neutral mathematical tools, can increase racial, social and gender inequalities.

It would not be hard to provide many more examples, but the selection above is probably enough. Technology, as Langdon Winner (Winner, 1980) observed almost forty years ago, is ‘deeply interwoven in the conditions of modern politics’. Technology cannot be neutral: it has politics.

So far, I have focused primarily on the non-neutrality of technology in terms of gender (and, in passing, race and class). Before returning to broader societal issues, I would like to make a relatively brief mention of another kind of non-neutrality: the pedagogic. Language learning materials necessarily contain content of some kind: texts, topics, the choice of values or role models, language examples, and so on. These cannot be value-free. In the early days of educational computer software, one researcher (Biraimah, 1993) found that it was ‘at least, if not more, biased than the printed page it may one day replace’. My own impression is that this remains true today.

Equally interesting to my mind is the fact that all educational technologies, ranging from the writing slate to the blackboard (see Buzbee, 2014), from the overhead projector to the interactive whiteboard, always privilege a particular kind of teaching (and learning). ‘Technologies are inherently biased because they are built to accomplish certain very specific goals which means that some technologies are good for some tasks while not so good for other tasks’ (Zhao et al., 2004: 25). Digital flashcards, for example, inevitably encourage a focus on rote learning. Contemporary LMSs have impressive multi-functionality (i.e. they often could be used in a very wide variety of ways), but, in practice, most teachers use them in very conservative ways (Laanpere et al., 2004). This may be a result of teacher and institutional preferences, but it is almost certainly due, at least in part, to the way that LMSs are designed. They are usually ‘based on traditional approaches to instruction dating from the nineteenth century: presentation and assessment [and] this can be seen in the selection of features which are most accessible in the interface, and easiest to use’ (Lane, 2009).

The argument that educational technology is neutral because it could be put to many different uses, good or bad, is problematic because the likelihood of one particular use is usually much greater than another. There is, however, another way of looking at technological neutrality, and that is to look at its origins. Elsewhere on this blog, in post after post, I have given examples of the ways in which educational technology has been developed, marketed and sold primarily for commercial purposes. Educational values, if indeed there are any, are often an afterthought. The research literature in this area is rich and growing: Stephen Ball, Larry Cuban, Neil Selwyn, Joel Spring, Audrey Watters, etc.

Rather than revisit old ground here, this is an opportunity to look at a slightly different origin of educational technology: the US military. The close connection of the early history of the internet and the Advanced Research Projects Agency (now DARPA) of the United States Department of Defense is fairly well-known. Much less well-known are the very close connections between the US military and educational technologies, which are catalogued in the recently reissued ‘The Classroom Arsenal’ by Douglas D. Noble.

Following the twin shocks of the Soviet Sputnik 1 (in 1957) and Yuri Gagarin (in 1961), the United States launched a massive programme of investment in the development of high-tech weaponry. This included ‘computer systems design, time-sharing, graphics displays, conversational programming languages, heuristic problem-solving, artificial intelligence, and cognitive science’ (Noble, 1991: 55), all of which are now crucial components in educational technology. But it also quickly became clear that more sophisticated weapons required much better trained operators, hence the US military’s huge (and continuing) interest in training. Early interest focused on teaching machines and programmed instruction (branches of the US military were by far the biggest purchasers of programmed instruction products). It was essential that training was effective and efficient, and this led to a wide interest in the mathematical modelling of learning and instruction.

What was then called computer-based education (CBE) was developed as a response to military needs. The first experiments in computer-based training took place at the Systems Research Laboratory of the Air Force’s RAND Corporation think tank (Noble, 1991: 73). Research and development in this area accelerated in the 1960s and 1970s and CBE (which has morphed into the platforms of today) ‘assumed particular forms because of the historical, contingent, military contexts for which and within which it was developed’ (Noble, 1991: 83). It is possible to imagine computer-based education having developed in very different directions. Between the 1960s and 1980s, for example, the PLATO (Programmed Logic for Automatic Teaching Operations) project at the University of Illinois focused heavily on computer-mediated social interaction (forums, message boards, email, chat rooms and multi-player games). PLATO was also significantly funded by a variety of US military agencies, but proved to be of much less interest to the generals than the work taking place in other laboratories. As Noble observes, ‘some technologies get developed while others do not, and those that do are shaped by particular interests and by the historical and political circumstances surrounding their development (Noble, 1991: 4).

According to Noble, however, the influence of the military reached far beyond the development of particular technologies. Alongside the investment in technologies, the military were the prime movers in a campaign to promote computer literacy in schools.

Computer literacy was an ideological campaign rather than an educational initiative – a campaign designed, at bottom, to render people ‘comfortable’ with the ‘inevitable’ new technologies. Its basic intent was to win the reluctant acquiescence of an entire population in a brave new world sculpted in silicon.

The computer campaign also succeeded in getting people in front of that screen and used to having computers around; it made people ‘computer-friendly’, just as computers were being rendered ‘used-friendly’. It also managed to distract the population, suddenly propelled by the urgency of learning about computers, from learning about other things, such as how computers were being used to erode the quality of their working lives, or why they, supposedly the citizens of a democracy, had no say in technological decisions that were determining the shape of their own futures.

Third, it made possible the successful introduction of millions of computers into schools, factories and offices, even homes, with minimal resistance. The nation’s public schools have by now spent over two billion dollars on over a million and a half computers, and this trend still shows no signs of abating. At this time, schools continue to spend one-fifth as much on computers, software, training and staffing as they do on all books and other instructional materials combined. Yet the impact of this enormous expenditure is a stockpile of often idle machines, typically used for quite unimaginative educational applications. Furthermore, the accumulated results of three decades of research on the effectiveness of computer-based instruction remain ‘inconclusive and often contradictory’. (Noble, 1991: x – xi)

Rather than being neutral in any way, it seems more reasonable to argue, along with (I think) most contemporary researchers, that edtech is profoundly value-laden because it has the potential to (i) influence certain values in students; (ii) change educational values in [various] ways; and (iii) change national values (Omotoyinbo & Omotoyinbo, 2016: 173). Most importantly, the growth in the use of educational technology has been accompanied by a change in the way that education itself is viewed: ‘as a tool, a sophisticated supply system of human cognitive resources, in the service of a computerized, technology-driven economy’ (Noble, 1991: 1). These two trends are inextricably linked.

References

Biraimah, K. 1993. The non-neutrality of educational computer software. Computers and Education 20 / 4: 283 – 290

Buzbee, L. 2014. Blackboard: A Personal History of the Classroom. Minneapolis: Graywolf Press

Chomsky, N. 2012. The Purpose of Education (video). Learning Without Frontiers Conference. https://www.youtube.com/watch?v=DdNAUJWJN08

Criado Perez, C. 2019. Invisible Women. London: Chatto & Windus

Fox, R. 2001. Technological neutrality and practice in higher education. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://clt.curtin.edu.au/events/conferences/tlf/tlf2001/fox.html

Laanpere, M., Poldoja, H. & Kikkas, K. 2004. The second thoughts about pedagogical neutrality of LMS. Proceedings of IEEE International Conference on Advanced Learning Technologies, 2004. https://ieeexplore.ieee.org/abstract/document/1357664

Lane, L. 2009. Insidious pedagogy: How course management systems impact teaching. First Monday, 14(10). https://firstmonday.org/ojs/index.php/fm/article/view/2530/2303Lane

Miller, A.E., MacDougall, J.D., Tarnopolsky, M. A. & Sale, D.G. 1993. ‘Gender differences in strength and muscle fiber characteristics’ European Journal of Applied Physiology and Occupational Physiology. 66(3): 254-62 https://www.ncbi.nlm.nih.gov/pubmed/8477683

Noble, D. D. 1991. The Classroom Arsenal. Abingdon, Oxon.: Routledge

Omotoyinbo, D. W. & Omotoyinbo, F. R. 2016. Educational Technology and Value Neutrality. Societal Studies, 8 / 2: 163 – 179 https://www3.mruni.eu/ojs/societal-studies/article/view/4652/4276

O’Neil, C. 2016. Weapons of Math Destruction. London: Penguin

Sundström, P. Interpreting the Notion that Technology is Value Neutral. Medicine, Health Care and Philosophy 1, 1998: 42-44

Tatman, R. 2017. ‘Gender and Dialect Bias in YouTube’s Automatic Captions’ Proceedings of the First Workshop on Ethics in Natural Language Processing, pp. 53–59 http://www.ethicsinnlp.org/workshop/pdf/EthNLP06.pdf

Wang, C. & Cai, D. 2017. ‘Hand tool handle design based on hand measurements’ MATEC Web of Conferences 119, 01044 (2017) https://www.matec-conferences.org/articles/matecconf/pdf/2017/33/matecconf_imeti2017_01044.pdf

Winner, L. 1980. Do Artifacts have Politics? Daedalus 109 / 1: 121 – 136

Zhao, Y, Alvarez-Torres, M. J., Smith, B. & Tan, H. S. 2004. The Non-neutrality of Technology: a Theoretical Analysis and Empirical Study of Computer Mediated Communication Technologies. Journal of Educational Computing Research 30 (1 &2): 23 – 55

Chatbots

Posted: December 1, 2016 in ed tech
Tags: , , , , , , , ,

 

Here’s a conversation I had recently with Cleverbot.

cleverbot

User (me): Hi Cleverbot. What are your views on the future of chatbots in language learning?

Cleverbot: Are you talking to yourself?

User: No, I’m asking you a question.

Cleverbot: Then what’s the question again? I forgot.

User: What are your views on the future of chatbots in language learning?

Cleverbot: Don’t you want me to finish my story?

User: No, I want you to answer my question.

Cleverbot: What is your question?

User: Do you think there is a future for chatbots in language education?

Cleverbot: Sure, if you are talking about the moon.

aliceI also tried to have a conversation with Alice, the avatar from EFL Classroom Bot, listed by Larry Ferlazzo as one of ‘the best online chatbots for practicing English’. I didn’t get any more sense out of her than out of Cleverbot.

Chatbots, apparently, are the next big thing. Again. David Mattin, head of trends and insights at trendwatching.com, writes (in the September 2016 issue of ‘Business Life’) that ‘the chatbot revolution is coming’ and that chatbots are a step towards the dream of an interface between user and technology that is so intuitive that the interface ‘simply fades away’. Chatbots have been around for some time. Remember Clippy – the Microsoft Office bot in the late 1990s – which you had to disable in order to stop yourself punching your computer screen? Since then, bots have become ubiquitous. There have been problems, such as Microsoft’s Tay bot that had to be taken down after sixteen hours earlier this year, when, after interacting with other Twitter users, it developed into an abusive Nazi. But chatbots aren’t going away and you’ve probably interacted with one to book a taxi, order food or attempt to talk to your bank. In September this year, the Guardian described them as ‘the talk of the town’ and ‘hot property in Silicon Valley’.

The real interest in chatbots is not, however, in the ‘exciting interface’ possibilities (both user interface and user experience remain pretty crude), but in the way that they are leaner, sit comfortably with the things we actually do on a phone and the fact that they offer a way of cutting out the high fees that developers have to pay to app stores . After so many start-up failures, chatbots offer a glimmer of financial hope to developers.

It’s no surprise, of course, to find the world of English language teaching beginning to sit up and take notice of this technology. A 2012 article by Ben Lehtinen in PeerSpectives enthuses about the possibilities in English language learning and reports the positive feedback of the author’s own students. ELTJam, so often so quick off the mark, developed an ELT Bot over the course of a hackathon weekend in March this year. Disappointingly, it wasn’t really a bot – more a case of humans pretending to be a bot pretending to be humans – but it probably served its exploratory purpose. duolingoAnd a few months ago Duolingo began incorporating bots. These are currently only available for French, Spanish and German learners in the iPhone app, so I haven’t been able to try it out and evaluate it. According to an infomercial in TechCrunch, ‘to make talking to the bots a bit more compelling, the company tried to give its different bots a bit of personality. There’s Chef Robert, Renee the Driver and Officer Ada, for example. They will react differently to your answers (and correct you as necessary), but for the most part, the idea here is to mimic a real conversation. These bots also allow for a degree of flexibility in your answers that most language-learning software simply isn’t designed for. There are plenty of ways to greet somebody, for example, but most services will often only accept a single answer. When you’re totally stumped for words, though, Duolingo offers a ‘help my reply’ button with a few suggested answers.’ In the last twelve months or so, Duolingo has considerably improved its ability to recognize multiple correct ways of expressing a particular idea, and its ability to recognise alternative answers to its translation tasks. However, I’m highly sceptical about its ability to mimic a real conversation any better than Cleverbot or Alice the EFL Bot, or its ability to provide systematically useful corrections.

My reasons lie in the current limitations of AI and NLP (Natural Language Processing). In a nutshell, we simply don’t know how to build a machine that can truly understand human language. Limited exchanges in restricted domains can be done pretty well (such as the early chatbot that did a good job of simulating an encounter with an evasive therapist, or, more recently ordering a taco and having a meaningless, but flirty conversation with a bot), but despite recent advances in semantic computing, we’re a long way from anything that can mimic a real conversation. As Audrey Watters puts it, we’re not even close.

When it comes to identifying language errors made by language learners, we’re not really much better off. Apps like Grammarly are not bad at identifying grammatical errors (but not good enough to be reliable), but pretty hopeless at dealing with lexical appropriacy. Much more reliable feedback to learners can be offered when the software is trained on particular topics and text types. Write & Improve does this with a relatively small selection of Cambridge English examination tasks, but a free conversation ….? Forget it.

So, how might chatbots be incorporated into language teaching / learning? A blog post from December 2015 entitled AI-powered chatbots and the future of language learning suggests one plausible possibility. Using an existing messenger service, such as WhatsApp or Telegram, an adaptive chatbot would send tasks (such as participation in a conversation thread with a predetermined topic, register, etc., or pronunciation practice or translation exercises) to a learner, provide feedback and record the work for later recycling. At the same time, the bot could send out reminders of work that needs to be done or administrative tasks that must be completed.

Kat Robb has written a very practical article about using instant messaging in English language classrooms. Her ideas are interesting (although I find the idea of students in a F2F classroom messaging each other slightly bizarre) and it’s easy to imagine ways in which her activities might be augmented with chatbot interventions. The Write & Improve app, mentioned above, could deploy a chatbot interface to give feedback instead of the flat (and, in my opinion, perfectly adequate) pop-up boxes currently in use. Come to think of it, more or less any digital language learning tool could be pimped up with a bot. Countless revisions can be envisioned.

But the overwhelming question is: would it be worth it? Bots are not likely, any time soon, to revolutionise language learning. What they might just do, however, is help to further reduce language teaching to a series of ‘mechanical and scripted gestures’. More certain is that a lot of money will be thrown down the post-truth edtech drain. Then, in the not too distant future, this latest piece of edtech will fall into the trough of disillusionment, to be replaced by the latest latest thing.

 

 

busuu is an online language learning service. I did not refer to it in the ‘guide’ because it does not seem to use any adaptive learning software yet, but this is set to change. According to founder Bernhard Niesner, the company is already working on incorporation of adaptive software.

A few statistics will show the significance of busuu. The site currently has over 40 million users (El Pais, 8 February 2014) and is growing by 40,000 a day. The basic service is free, but the premium service costs Euro 69.99 a year. The company will not give detailed user statistics, but say that ‘hundreds of thousands’ are paying for the premium service, that turnover was a 7-figure number last year and will rise to 8 figures this year.

It is easy to understand why traditional publishers might be worried about competition like busuu and why they are turning away from print-based courses.

Busuu offers 12 languages, but, as a translation-based service, any one of these languages can only be studied if you speak one of the other languages on offer. The levels of the different courses are tagged to the CEFR.

busuuframe

In some ways, busuu is not so different from competitors like duolingo. Students are presented with bilingual vocabulary sets, accompanied by pictures, which are tested in a variety of ways. As with duolingo, some of this is a little strange. For German at level A1, I did a vocabulary set on ‘pets’ which presented the German words for a ferret, a tortoise and a guinea-pig, among others. There are dialogues, which are both written and recorded, that are sometimes surreal.

Child: Mum, look over there, there’s a dog without a collar, can we take it?

Mother: No, darling, our house is too small to have a dog.

Child: Mum your bedroom is very big, it can sleep with dad and you.

Mother: Come on, I’ll buy you a toy dog.

The dialogues are followed up by multiple choice questions which test your memory of the dialogue. There are also writing exercises where you are given a picture from National Geographic and asked to write about it. It’s not always clear what one is supposed to write. What would you say about a photo that showed a large number of parachutes in the sky, beyond ‘I can see a lot of parachutes’?

There are also many gamification elements. There is a learning carrot where you can set your own learning targets and users can earn ‘busuuberries’ which can then be traded in for animations in a ‘language garden’.

2014-02-25_0911

But in one significant respect, busuu differs from its competitors. It combines the usual vocabulary, grammar and dialogue work with social networking. Users can interact with text or video, and feedback on written work comes from other users. My own experience with this was mixed, but the potential is clear. Feedback on other learners’ work is encouraged by the awarding of ‘busuuberries’.

We will have to wait and see what busuu does with adaptive software and what it will do with the big data it is generating. For the moment, its interest lies in illustrating what could be done with a learning platform and adaptive software. The big ELT publishers know they have a new kind of competition and, with a lot more money to invest than busuu, we have to assume that what they will launch a few years from now will do everything that busuu does, and more. Meanwhile, busuu are working on site redesign and adaptivity. They would do well, too, to sort out their syllabus!