Life skills, technology, teacher evaluation and the World Bank

If you cast your eye over the English language teaching landscape, you can’t help noticing a number of prominent features that weren’t there, or at least were much less visible, twenty years ago. I’d like to highlight three. First, there is the interest in life skills (aka 21^st century skills). Second, there is the use of digital technology to deliver content. And third, there is a concern with measuring educational outputs through frameworks such as the Pearson GSE. In this post, I will focus primarily on the last of these, with a closer look at measuring teacher performance.

Recent years have seen the development of a number of frameworks for evaluating teacher competence in ELT. These include

• The British Council CPD Framework
• The Cambridge English Teaching Framework
• The EAQUALS Framework for Language Teacher Training and Development
• The TESOL Technology Standards Framework

TESOL has also produced a set of guidelines for developing professional teaching standards for EFL.

Frameworks such as these were not always intended as tools to evaluate teachers. The British Council’s framework, for example, was apparently designed for teachers to understand and plan their own professional development. Similarly, the Cambridge framework says that it is for teachers to see where they are in their development – and think about where they want to go next. But much like the CEFR for language competence, frameworks can be used for purposes rather different from their designers’ intentions. I think it is likely that frameworks such as these are more often used to evaluate teachers than for teachers to evaluate themselves.

But where did the idea for such frameworks come from? Was there a suddenly perceived need for things like this to aid in self-directed professional development? Were teachers’ associations calling out for frameworks to help their members? Even if that were the case, it would still be useful to know why, and why now.

One possibility is that the interest in life skills, digital technology and the measurement of educational outputs have all come about as a result of what has been called the Global Educational Reform Movement, or GERM (Sahlberg, 2016). GERM dates back to the 1980s and the shifts (especially in the United States under Reagan and the United Kingdom under Thatcher) in education policy towards more market-led approaches which emphasize (1) greater competition between educational providers, (2) greater autonomy from the state for educational providers (and therefore a greater role for private suppliers), (3) greater choice of educational provider for students and their parents, and (4) standardized tests and measurements which allow consumers of education to make more informed choices. One of the most significant GERM vectors is the World Bank.

The interest in incorporating the so-called 21^st century skills as part of the curriculum can be traced back to the early 1980s when the US National Commission on Excellence in Education recommended the inclusion of a range of skills, which eventually crystallized into the four Cs of communication, collaboration, critical thinking and creativity. The labelling of this skill set as ‘life skills’ or ‘21^st century skills’ was always something of a misnomer: the reality was that these were the soft skills required by the world of work. The key argument for their inclusion in the curriculum was that they were necessary for the ‘competitiveness and wealth of corporations and countries’ (Trilling & Fadel, 2009: 7). Unsurprisingly, the World Bank, whose interest in education extends only so far as its economic value, embraced the notion of ‘life skills’ with enthusiasm. Its document ‘Life skills : what are they, why do they matter, and how are they taught?’ (World Bank, 2013), makes the case very clearly. It took a while for the world of English language teaching to get on board, but by 2012, Pearson was already sponsoring a ‘signature event’ at IATEFL Glasgow entitled ‘21st Century Skills for ELT’. Since then, the currency of ‘life skills’ as an ELT buzz phrase has not abated.

Just as the World Bank’s interest in ‘life skills’ is motivated by the perceived need to prepare students for the world of work (for participation in the ‘knowledge economy’), the Bank emphasizes the classroom use of computers and resources from the internet: Information and communication technology (ICT) allows the adaptation of globally available information to local learning situations. […] A large percentage of the World Bank’s education funds are used for the purchase of educational technology. […] According to the Bank’s figures, 40 per cent of their education budget in 2000 and 27 per cent in 2001 was used to purchase technology. (Spring, 2015: 50).

Digital technology is also central to capturing data, which will allow for the measurement of educational outputs. As befits an organisation of economists that is interested in the cost-effectiveness of investments into education, it accords enormous importance to what are thought to be empirical measures or accountability. So intrinsic to the Bank’s approach is this concern with measurement that ‘the Bank’s implicit message to national governments seems to be: ‘improve your data collection capacity so that we can run more reliable cross-country analysis and regressions’. (Verger & Bonal, 2012: 131).

Measuring the performance of teachers is, of course, a part of assessing educational outputs. The World Bank, which sees global education as fundamentally ‘broken’, has, quite recently, turned more of its attention to the role of teachers. A World Bank blog from 2019 explains the reasons:

A growing body of evidence suggests the learning crisis is, at its core, a teaching crisis. For students to learn, they need good teachers—but many education systems pay little attention to what teachers know, what they do in the classroom, and in some cases whether they even show up. Rapid technological change is raising the stakes. Technology is already playing a crucial role in providing support to teachers, students, and the learning process more broadly. It can help teachers better manage the classroom and offer different challenges to different students. And technology can allow principals, parents, and students to interact seamlessly.

A key plank in the World Banks’s attempts to implement its educational vision is its System Assessment and Benchmarking for Education Results (SABER), which I will return to in due course. As part of its SABER efforts, last year the World Bank launched its ‘Teach’ tool . This tool is basically an evaluation framework. Videos of lessons are recorded and coded for indicators of teacher efficiency by coders who can be ‘90% reliable’ after only four days of training. The coding system focuses on the time that students spend on-task, but also ‘life skills’ like collaboration and critical thinking (see below).

Like the ELT frameworks, it can be used as a professional development tool, but, like them, it may also be used for summative evaluation.

The connections between those landmarks on the ELT landscape and the concerns of the World Bank are not, I would suggest, coincidental. The World Bank is, of course, not the only player in GERM, but it is a very special case. It is the largest single source of external financing in ‘developing countries’ (Beech, 2009: 345), managing a portfolio of $8.9 billion, with operations in 70 countries as of August 2013 (Spring, 2015: 32). Its loans come attached with conditions which tie the borrowing countries to GERM objectives. Arguably of even greater importance than its influence through funding, is the Bank’s direct entry into the world of ideas:

The Bank yearns for a deeper and more comprehensive impact through avenues of influence transcending both project and program loans. Not least in education, the World Bank is investing much in its quest to shape global opinion about economic, developmental, and social policy. Rather than imposing views through specific loan negotiations, Bank style is broadening in attempts to lead borrower country officials to its preferred way of thinking. (Jones, 2007: 259).

The World Bank sees itself as a Knowledge Bank and acts accordingly. Rizvi and Lingard (2010: 48) observe that ‘in many nations of the Global South, the only extant education policy analysis is research commissioned by donor agencies such as the World Bank […] with all the implications that result in relation to problem setting, theoretical frameworks and methodologies’. Hundreds of academics are engaged to do research related to the Bank’s areas of educational interest, and ‘the close links with the academic world give a strong credibility to the ideas disseminated by the Bank […] In fact, many ideas that acquired currency and legitimacy were originally proposed by them. This is the case of testing students and using the results to evaluate progress in education’ (Castro, 2009: 472).

Through a combination of substantial financial clout and relentless marketing (Selwyn, 2013: 50), the Bank has succeeded in shaping global academic discourse. In partnership with similar institutions, it has introduced a way of classifying and thinking about education (Beech, 2009: 352). It has become, in short, a major site ‘for the organization of knowledge about education’ (Rizvi & Lingard, 2010: 79), wielding ‘a degree of power that has arguably enabled it to shape the educational agendas of nations throughout the Global South’ and beyond (Menashy, 2012).

So, is there any problem in the world of ELT taking up the inclusion of ‘life skills’? I think there is. The first is one of definition. Creativity and critical thinking are very poorly defined, meaning very different things to different people, so it is not always clear what is being taught. Following on from this, there is substantial debate about whether such skills can actually be taught at all, and, if they can, how they should be taught. It seems highly unlikely that the tokenistic way in which they are ‘taught’ in most published ELT courses can be of any positive impact. But this is not my main reservation, which is that, by and large, we have come to uncritically accept the idea that English language learning is mostly concerned with preparation for the workplace (see my earlier post ‘The EdTech Imaginary in ELT’).

Is there any problem with the promotion of digital technologies in ELT? Again, I think there is, and a good proportion of the posts on this blog have argued for the need for circumspection in rolling out more technology in language learning and teaching. My main reason is that while it is clear that this trend is beneficial to technology vendors, it is much less clear that advantages will necessarily accrue to learners. Beyond this, there must be serious concerns about data ownership, privacy, and the way in which the datafication of education, led by businesses and governments in the Global North, is changing what counts as good education, a good student or an effective teacher, especially in the Global South. ‘Data and metrics,’ observe Williamson et al. (2020: 353), ‘do not just reflect what they are designed to measure, but actively loop back into action that can change the very thing that was measured in the first place’.

And what about tools for evaluating teacher competences? Here I would like to provide a little more background. There is, first of all, a huge question mark about how accurately such tools measure what they are supposed to measure. This may not matter too much if the tool is only used for self-evaluation or self-development, but ‘once smart systems of data collection and social control are available, they are likely to be widely applied for other purposes’ (Sadowski, 2020: 138). Jaime Saavedra, head of education at the World Bank, insists that the World Bank’s ‘Teach’ tool is not for evaluation and is not useful for firing teachers who perform badly.

Saavedra needs teachers to buy into the tool, so he obviously doesn’t want to scare them off. However, ‘Teach’ clearly is an evaluation tool (if not, what is it?) and, as with other tools (I’m thinking of CEFR and teacher competency frameworks in ELT), its purposes will evolve. Eric Hanushek, an education economist at Stanford University, has commented that ‘this is a clear evaluation tool at the probationary stage … It provides a basis for counseling new teachers on how they should behave … but then again if they don’t change over the first few years you also have information you should use’.

At this point, it is useful to take a look at the World Bank’s attitudes towards teachers. Teachers are seen to be at the heart of the ‘learning crisis’. However, the greatest focus in World Bank documents is on (1) teacher absenteeism in some countries, (2) unskilled and demotivated teachers, and (3) the reluctance of teachers and their unions to back World Bank-sponsored reforms. As real as these problems are, it is important to understand that the Bank has been complicit in them:

For decades, the Bank has criticised pre-service and in-service teacher training as not cost-effective For decades, the Bank has been pushing the hiring of untrained contract teachers as a cheap fix and a way to get around teacher unions – and contract teachers are again praised in the World Bank Development Report (WDR). This contradicts the occasional places in the WDR in which the Bank argues that developing countries need to follow the lead of the few countries that attract the best students to teaching, improve training, and improve working conditions. There is no explicit evidence offered at all for the repeated claim that teachers are unmotivated and need to be controlled and monitored to do their job. The Bank has a long history of blaming teachers and teacher unions for educational failures. The Bank implicitly argues that the problem of teacher absenteeism, referred to throughout the report, means teachers are unmotivated, but that simply is not true. Teacher absenteeism is not a sign of low motivation. Teacher salaries are abysmally low, as is the status of teaching. Because of this, teaching in many countries has become an occupation of last resort, yet it still attracts dedicated teachers. Once again, the Bank has been very complicit in this state of affairs as it, and the IMF, for decades have enforced neoliberal, Washington Consensus policies which resulted in government cutbacks and declining real salaries for teachers around the world. It is incredible that economists at the Bank do not recognise that the deterioration of salaries is the major cause of teacher absenteeism and that all the Bank is willing to peddle are ineffective and insulting pay-for-performance schemes. (Klees, 2017).

The SABER framework (referred to above) focuses very clearly on policies for hiring, rewarding and firing teachers.

[The World Bank] places the private sector’s methods of dealing with teachers as better than those of the public sector, because it is more ‘flexible’. In other words, it is possible to say that teachers can be hired and fired more easily; that is, hired without the need of organizing a public competition and fired if they do not achieve the expected outcomes as, for example, students’ improvements in international test scores. Further, the SABER document states that ‘Flexibility in teacher contracting is one of the primary motivations for engaging the private sector’ (World Bank, 2011: 4). This affirmation seeks to reduce expenditures on teachers while fostering other expenses such as the creation of testing schemes and spending more on ICTs, as well as making room to expand the hiring of private sector providers to design curriculum, evaluate students, train teachers, produce education software, and books. (De Siqueira, 2012).

The World Bank has argued consistently for a reduction of education costs by driving down teachers’ salaries. One of the authors of the World Bank Development Report 2018 notes that ‘in most countries, teacher salaries consume the lion’s share of the education budget, so there are already fewer resources to implement other education programs’. Another World Bank report (2007) makes the importance of ‘flexible’ hiring and lower salaries very clear:

In particular, recent progress in primary education in Francophone countries resulted from reduced teacher costs, especially through the recruitment of contractual teachers, generally at about 50% the salary of civil service teachers. (cited in Compton & Weiner, 2008: 7).

Merit pay (or ‘pay for performance’) is another of the Bank’s preferred wheezes. Despite enormous problems in reaching fair evaluations of teachers’ work and a distinct lack of convincing evidence that merit pay leads to anything positive (and may actually be counter-productive) (De Bruyckere et al., 2018: 143 – 147), the Bank is fully committed to the idea. Perhaps this is connected to the usefulness of merit pay in keeping teachers on their toes, compliant and fearful of losing their jobs, rather than any desire to improve teacher effectiveness?

There is evidence that this may be the case. Yet another World Bank report (Bau & Das, 2017) argues, on the basis of research, that improved TVA (teacher value added) does not correlate with wages in the public sector (where it is hard to fire teachers), but it does in the private sector. The study found that ‘a policy change that shifted public hiring from permanent to temporary contracts, reducing wages by 35 percent, had no adverse impact on TVA’. All of which would seem to suggest that improving the quality of teaching is of less importance to the Bank than flexible hiring and firing. This is very much in line with a more general advocacy of making education fit for the world of work. Lois Weiner of New Jersey City University puts it like this:

The architects of [GERM] policies—imposed first in developing countries—openly state that the changes will make education better fit the new global economy by producing workers who are (minimally) educated for jobs that require no more than a 7th or 8th grade education; while a small fraction of the population receive a high quality education to become the elite who oversee finance, industry, and technology. Since most workers do not need to be highly educated, it follows that teachers with considerable formal education and experience are neither needed nor desired because they demand higher wages, which is considered a waste of government money. Most teachers need only be “good enough”—as one U.S. government official phrased it—to follow scripted materials that prepare students for standardized tests. (Weiner, 2012).

It seems impossible to separate the World Bank’s ‘Teach’ tool from the broader goals of GERM. Teacher evaluation tools, like the teaching of 21^st century skills and the datafication of education, need to be understood properly, I think, as means to an end. It’s time to spell out what that end is.

The World Bank’s mission is ‘to end extreme poverty (by reducing the share of the global population that lives in extreme poverty to 3 percent by 2030)’ and ‘to promote shared prosperity (by increasing the incomes of the poorest 40 percent of people in every country)’. Its education activities are part of this broad aim and are driven by subscription to human capital theory (a view of the skills, knowledge and experience of individuals in terms of their ability to produce economic value). This may be described as the ‘economization of education’: a shift in educational concerns away from ‘such things as civic participation, protecting human rights, and environmentalism to economic growth and employment’ (Spring, 2015: xiii). Both students and teachers are seen as human capital. For students, human capital education places an emphasis on the cognitive skills needed to succeed in the workplace and the ‘soft skills’, needed to function in the corporate world (Spring, 2015: 2). Accordingly, World Bank investments require ‘justifications on the basis of manpower demands’ (Heyneman, 2003: 317). One of the Bank’s current strategic priorities is the education of girls: although human rights and equity may also play a part, the Bank’s primary concern is that ‘Not Educating Girls Costs Countries Trillions of Dollars’ .

According to the Bank’s logic, its educational aims can best be achieved through a combination of support for the following:

• cost accounting and quantification (since returns on investment must be carefully measured)
• competition and market incentives (since it is believed that the ‘invisible hand’ of the market leads to the greatest benefits)
• the private sector in education and a rolling back of the role of the state (since it is believed that private ownership improves efficiency)

The package of measures is a straightforward reflection of ‘what Western mainstream economists believe’ (Castro, 2009: 474).

Mainstream Western economics is, however, going through something of a rocky patch right now. Human capital theory is ‘useful when prevailing conditions are right’ (Jones, 2007: 248), but prevailing conditions are not right in much of the world (even in the United States), and the theory ‘for the most part ignores the intersections of poverty, equity and education’ (Menashy, 2012). In poorer countries evidence for the positive effects of markets in education is in very short supply, and even in richer countries it is still not conclusive (Verger & Bonal, 2012: 135). An OECD Education Paper (Waslander et al., 2010: 64) found that the effects of choice and competition between schools were at best small, if indeed any effects were found at all. Similarly, the claim that privatization improves efficiency is not sufficiently supported by evidence. Analyses of PISA data would seem to indicate that, ‘all else being equal (especially when controlling for the socio-economic status of the students), the type of ownership of the school, whether it is a private or a state school, has only modest effects on student achievement or none at all’ (Verger & Bonal, 2012: 133). Educational privatization as a one-size-fits-all panacea to educational problems has little to recommend it.

There are, then, serious limitations in the Bank’s theoretical approach. Its practical track record is also less than illustrious, even by the Bank’s own reckoning. Many of the Bank’s interventions have proved very ‘costly to developing countries. At the Bank’s insistence countries over-invested in vocational and technical education. Because of the narrow definition of recurrent costs, countries ignored investments in reading materials and in maintaining teacher salaries. Later at the Bank’s insistence, countries invested in thousands of workshops and laboratories that, for the most part, became useless ‘white elephants’ (Heyneman, 2003: 333).

As a bank, the World Bank is naturally interested in the rate of return of investment in that capital, and is therefore concerned with efficiency and efficacy. This raises the question of ‘Effective for what?’ and given that what may be effective for one individual or group may not necessarily be effective for another individual or group, one may wish to add a second question: ‘Effective for whom?’ (Biesta, 2020: 31). Critics of the World Bank, of whom there are many, argue that its policies serve ‘the interests of corporations by keeping down wages for skilled workers, cause global brain migration to the detriment of developing countries, undermine local cultures, and ensure corporate domination by not preparing school graduates who think critically and are democratically oriented’ (Spring, 2015: 56). Lest this sound a bit harsh, we can turn to the Bank’s own commissioned history: ‘The way in which [the Bank’s] ideology has been shaped conforms in significant degree to the interests and conventional wisdom of its principal stockholders [i.e. bankers and economists from wealthy nations]. International competitive bidding, reluctance to accord preferences to local suppliers, emphasis on financing foreign exchange costs, insistence on a predominant use of foreign consultants, attitudes toward public sector industries, assertion of the right to approve project managers – all proclaim the Bank to be a Western capitalist institution’ (Mason & Asher, 1973: 478 – 479).

The teaching of ‘life skills’, the promotion of data-capturing digital technologies and the push to evaluate teachers’ performance are, then, all closely linked to the agenda of the World Bank, and owe their existence in the ELT landscape, in no small part, to the way that the World Bank has shaped educational discourse. There is, however, one other connection between ELT and the World Bank which must be mentioned.

The World Bank’s foreign language instructional goals are directly related to English as a global language. The Bank urges, ‘Policymakers in developing countries …to ensure that young people acquire a language with more than just local use, preferably one used internationally.’ What is this international language? First, the World Bank mentions that schools of higher education around the world are offering courses in English. In addition, the Bank states, ‘People seeking access to international stores of knowledge through the internet require, principally, English language skills.’ (Spring, 2015: 48).

Without the World Bank, then, there might be a lot less English language teaching than there is. I have written this piece to encourage people to think more about the World Bank, its policies and particular instantiations of those policies. You might or might not agree that the Bank is an undemocratic, technocratic, neoliberal institution unfit for the necessities of today’s world (Klees, 2017). But whatever you think about the World Bank, you might like to consider the answers to Tony Benn’s ‘five little democratic questions’ (quoted in Sardowski, 2020: 17):

• What power has it got?
• Where did it get this power from?
• In whose interests does it exercise this power?
• To whom is it accountable?
• How can we get rid of it?

References

Bau, N. and Das, J. (2017). The Misallocation of Pay and Productivity in the Public Sector : Evidence from the Labor Market for Teachers. Policy Research Working Paper; No. 8050. World Bank, Washington, DC. Retrieved [18 May 2020] from https://openknowledge.worldbank.org/handle/10986/26502

Beech, J. (2009). Who is Strolling Through The Global Garden? International Agencies and Educational Transfer. In Cowen, R. and Kazamias, A. M. (Eds.) Second International Handbook of Comparative Education. Dordrecht: Springer. pp. 341 – 358

Biesta, G. (2020). Educational Research. London: Bloomsbury.

Castro, C. De M., (2009). Can Multilateral Banks Educate The World? In Cowen, R. and Kazamias, A. M. (Eds.) Second International Handbook of Comparative Education. Dordrecht: Springer. pp. 455 – 478

Compton, M. and Weiner, L. (Eds.) (2008). The Global Assault on Teaching, Teachers, and their Unions. New York: Palgrave Macmillan

De Bruyckere, P., Kirschner, P.A. and Hulshof, C. (2020). More Urban Myths about Learning and Education. New York: Routledge.

De Siqueira, A. C. (2012). The 2020 World Bank Education Strategy: Nothing New, or the Same Old Gospel. In Klees, S. J., Samoff, J. and Stromquist, N. P. (Eds.) The World Bank and Education. Rotterdam: Sense Publishers. pp. 69 – 81

Heyneman, S.P. (2003). The history and problems in the making of education policy at the World Bank 1960–2000. International Journal of Educational Development 23 (2003) pp. 315–337. Retrieved [18 May 2020] from https://www.academia.edu/29593153/The_History_and_Problems_in_the_Making_of_Education_Policy_at_the_World_Bank_1960_2000

Jones, P. W. (2007). World Bank Financing of Education. 2^nd edition. Abingdon, Oxon.: Routledge.

Klees, S. (2017). A critical analysis of the World Bank’s World Development Report on education. Retrieved [18 May 2020] from: https://www.brettonwoodsproject.org/2017/11/critical-analysis-world-banks-world-development-report-education/

Mason, E. S. & Asher, R. E. (1973). The World Bank since Bretton Woods. Washington, DC: Brookings Institution.

Menashy, F. (2012). Review of Klees, S J., Samoff, J. & Stromquist, N. P. (Eds) (2012). The World Bank and Education: Critiques and Alternatives .Rotterdam: Sense Publishers. Education Review, 15. Retrieved [18 May 2020] from https://www.academia.edu/7672656/Review_of_The_World_Bank_and_Education_Critiques_and_Alternatives

Rizvi, F. & Lingard, B. (2010). Globalizing Education Policy. Abingdon, Oxon.: Routledge.

Sadowski, J. (2020). Too Smart. Cambridge, MA.: MIT Press.

Sahlberg, P. (2016). The global educational reform movement and its impact on schooling. In K. Mundy, A. Green, R. Lingard, & A. Verger (Eds.), The handbook of global policy and policymaking in education. New York, NY: Wiley-Blackwell. pp.128 – 144

Selwyn, N. (2013). Education in a Digital World. New York: Routledge.

Spring, J. (2015). Globalization of Education 2nd Edition. New York: Routledge.

Trilling, B. & C. Fadel (2009). 21st Century Skills. San Francisco: Wiley

Verger, A. & Bonal, X. (2012). ‘All Things Being Equal?’ In Klees, S. J., Samoff, J. and Stromquist, N. P. (Eds.) The World Bank and Education. Rotterdam: Sense Publishers. pp. 69 – 81

Waslander, S., Pater, C. & van der Weide, M. (2010). Markets in Education: An analytical review of empirical research on market mechanisms in education. OECD EDU Working Paper 52.

Weiner, L. (2012). Social Movement Unionism: Teachers Can Lead the Way. Reimagine, 19 (2) Retrieved [18 May 2020] from: https://www.reimaginerpe.org/19-2/weiner-fletcher

Williamson, B., Bayne, S. & Shay, S. (2020). The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811

World Bank. (2013). Life skills : what are they, why do they matter, and how are they taught? (English). Adolescent Girls Initiative (AGI) learning from practice series. Washington DC ; World Bank. Retrieved [18 May 2020] from: http://documents.worldbank.org/curated/en/569931468331784110/Life-skills-what-are-they-why-do-they-matter-and-how-are-they-taught

The EdTech Imaginary in ELT

In my last post , I asked why it is so easy to believe that technology (in particular, technological innovations) will offer solutions to whatever problems exist in language learning and teaching. A simple, but inadequate, answer is that huge amounts of money have been invested in persuading us. Without wanting to detract from the significance of this, it is clearly not sufficient as an explanation. In an attempt to develop my own understanding, I have been turning more and more to the idea of ‘social imaginaries’. In many ways, this is also an attempt to draw together the various interests that I have had since starting this blog.

The Canadian philosopher, Charles Taylor, describes a ‘social imaginary’ as a ‘common understanding that makes possible common practices and a widely shared sense of legitimacy’ (Taylor, 2004: 23). As a social imaginary develops over time, it ‘begins to define the contours of [people’s] worlds and can eventually come to count as the taken-for-granted shape of things, too obvious to mention’ (Taylor, 2004: 29). It is, however, not just a set of ideas or a shared narrative: it is also a set of social practices that enact those understandings, whilst at the same time modifying or solidifying them. The understandings make the practices possible, and it is the practices that largely carry the understanding (Taylor, 2004: 25). In the process, the language we use is filled with new associations and our familiarity with these associations shapes ‘our perceptions and expectations’ (Worster, 1994, quoted in Moore, 2015: 33). A social imaginary, then, is a complex system that is not technological or economic or social or political or educational, but all of these (Urry, 2016). The image of the patterns of an amorphous mass of moving magma (Castoriadis, 1987), flowing through pre-existing channels, but also, at times, striking out along new paths, may offer a helpful metaphor.

Technology, of course, plays a key role in contemporary social imaginaries and the term ‘sociotechnical imaginary’ is increasingly widely used. The understandings of the sociotechnical imaginary typically express visions of social progress and a desirable future that is made possible by advances in science and technology (Jasanoff & Kim, 2015: 4). In education, technology is presented as capable of overcoming human failings and the dark ways of the past, of facilitating a ‘pedagogical utopia of natural, authentic teaching and learning’ (Friesen, forthcoming). As such understandings become more widespread and as the educational practices (platforms, apps, etc.) which both shape and are shaped by them become equally widespread, technology has come to be seen as a ‘solution’ to the ‘problem’ of education (Friesen, forthcoming). We need to be careful, however, that having shaped the technology, it does not comes to shape us (see Cobo, 2019, for a further exploration of this idea).

As a way of beginning to try to understand what is going on in edtech in ELT, which is not so very different from what is taking place in education more generally, I have sketched a number of what I consider key components of the shared understandings and the social practices that are related to them. These are closely interlocking pieces and each of them is itself embedded in much broader understandings. They evolve over time and their history can be traced quite easily. Taken together, they do, I think, help us to understand a little more why technology in ELT seems so seductive.

1 The main purpose of English language teaching is to prepare people for the workplace

There has always been a strong connection between learning an additional living language (such as English) and preparing for the world of work. The first modern language schools, such as the Berlitz schools at the end of the 19^th century with their native-speaker teachers and monolingual methods, positioned themselves as primarily vocational, in opposition to the kinds of language teaching taking place in schools and universities, which were more broadly humanistic in their objectives. Throughout the 20^th century, and especially as English grew as a global language, the public sector, internationally, grew closer to the methods and objectives of the private schools. The idea that learning English might serve other purposes (e.g. cultural enrichment or personal development) has never entirely gone away, as witnessed by the Council of Europe’s list of objectives (including the promotion of mutual understanding and European co-operation, and the overcoming of prejudice and discrimination) in the Common European Framework, but it is often forgotten.

The clarion calls from industry to better align education with labour markets, present and future, grow louder all the time, often finding expression in claims that ‘education is unfit for purpose.’ It is invariably assumed that this purpose is to train students in the appropriate skills to enhance their ‘human capital’ in an increasingly competitive and global market (Lingard & Gale, 2007). Educational agendas are increasingly set by the world of business (bodies like the OECD or the World Economic Forum, corporations like Google or Microsoft, and national governments which share their priorities (see my earlier post about neo-liberalism and solutionism ).

One way in which this shift is reflected in English language teaching is in the growing emphasis that is placed on ‘21^st century skills’ in teaching material. Sometimes called ‘life skills’, they are very clearly concerned with the world of work, rather than the rest of our lives. The World Economic Forum’s 2018 Future of Jobs survey lists the soft skills that are considered important in the near future and they include ‘creativity’, ‘critical thinking’, ‘emotional intelligence’ and ‘leadership’. (The fact that the World Economic Forum is made up of a group of huge international corporations (e.g. J.P. Morgan, HSBC, UBS, Johnson & Johnson) with a very dubious track record of embezzlement, fraud, money-laundering and tax evasion has not resulted in much serious, public questioning of the view of education expounded by the WEF.)

Without exception, the ELT publishers have brought these work / life skills into their courses, and the topic is an extremely popular one in ELT blogs and magazines, and at conferences. Two of the four plenaries at this year’s international IATEFL conference are concerned with these skills. Pearson has a wide range of related products, including ‘a four-level competency-based digital course that provides engaging instruction in the essential work and life skills competencies that adult learners need’. Macmillan ELT made ‘life skills’ the central plank of their marketing campaign and approach to product design, and even won a British Council ELTon (see below) Award for ‘Innovation in teacher resources) in 2015 for their ‘life skills’ marketing campaign. Cambridge University Press has developed a ‘Framework for Life Competencies’ which allows these skills to be assigned numerical values.

The point I am making here is not that these skills do not play an important role in contemporary society, nor that English language learners may not benefit from some training in them. The point, rather, is that the assumption that English language learning is mostly concerned with preparation for the workplace has become so widespread that it becomes difficult to think in another way.

2 Technological innovation is good and necessary

The main reason that soft skills are deemed to be so important is that we live in a rapidly-changing world, where the unsubstantiated claim that 85% (or whatever other figure comes to mind) of current jobs won’t exist 10 years from now is so often repeated that it is taken as fact . Whether or not this is true is perhaps less important to those who make the claim than the present and the future that they like to envisage. The claim is, at least, true-ish enough to resonate widely. Since these jobs will disappear, and new ones will emerge, because of technological innovations, education, too, will need to innovate to keep up.

English language teaching has not been slow to celebrate innovation. There were coursebooks called ‘Cutting Edge’ (1998) and ‘Innovations’ (2005), but more recently the connections between innovation and technology have become much stronger. The title of the recent ‘Language Hub’ (2019) was presumably chosen, in part, to conjure up images of digital whizzkids in fashionable co-working start-up spaces. Technological innovation is explicitly promoted in the Special Interest Groups of IATEFL and TESOL. Despite a singular lack of research that unequivocally demonstrates a positive connection between technology and language learning, the former’s objective is ‘to raise awareness among ELT professionals of the power of learning technologies to assist with language learning’. There is a popular annual conference, called InnovateELT , which has the tagline ‘Be Part of the Solution’, and the first problem that this may be a solution to is that our students need to be ‘ready to take on challenging new careers’.

Last, but by no means least, there are the annual British Council ELTon awards  with a special prize for digital innovation. Among the British Council’s own recent innovations are a range of digitally-delivered resources to develop work / life skills among teens.

Again, my intention (here) is not to criticise any of the things mentioned in the preceding paragraphs. It is merely to point to a particular structure of feeling and the way that is enacted and strengthened through material practices like books, social groups, conferences and other events.

3 Technological innovations are best driven by the private sector

The vast majority of people teaching English language around the world work in state-run primary and secondary schools. They are typically not native-speakers of English, they hold national teaching qualifications and they are frequently qualified to teach other subjects in addition to English (often another language). They may or may not self-identify as teachers of ‘ELT’ or ‘EFL’, often seeing themselves more as ‘school teachers’ or ‘language teachers’. People who self-identify as part of the world of ‘ELT or ‘TEFL’ are more likely to be native speakers and to work in the private sector (including private or semi-private language schools, universities (which, in English-speaking countries, are often indistinguishable from private sector institutions), publishing companies, and freelancers). They are more likely to hold international (TEFL) qualifications or higher degrees, and they are less likely to be involved in the teaching of other languages.

The relationship between these two groups is well illustrated by the practice of training days, where groups of a few hundred state-school teachers participate in workshops organised by publishing companies and delivered by ELT specialists. In this context, state-school teachers are essentially in a client role when they are in contact with the world of ‘ELT’ – as buyers or potential buyers of educational products, training or technology.

Technological innovation is invariably driven by the private sector. This may be in the development of technologies (platforms, apps and so on), in the promotion of technology (through training days and conference sponsorship, for example), or in training for technology (with consultancy companies like ELTjam or The Consultants-E, which offer a wide range of technologically oriented ‘solutions’).

As in education more generally, it is believed that the private sector can be more agile and more efficient than state-run bodies, which continue to decline in importance in educational policy-setting. When state-run bodies are involved in technological innovation in education, it is normal for them to work in partnership with the private sector.

4 Accountability is crucial

Efficacy is vital. It makes no sense to innovate unless the innovations improve something, but for us to know this, we need a way to measure it. In a previous post , I looked at Pearson’s ‘Asking More: the Path to Efficacy’ by CEO John Fallon (who will be stepping down later this year). Efficacy in education, says Fallon, is ‘making a measurable impact on someone’s life through learning’. ‘Measurable’ is the key word, because, as Fallon claims, ‘it is increasingly possible to determine what works and what doesn’t in education, just as in healthcare.’ We need ‘a relentless focus’ on ‘the learning outcomes we deliver’ because it is these outcomes that can be measured in ‘a systematic, evidence-based fashion’. Measurement, of course, is all the easier when education is delivered online, ‘real-time learner data’ can be captured, and the power of analytics can be deployed.

Data is evidence, and it’s as easy to agree on the importance of evidence as it is hard to decide on (1) what it is evidence of, and (2) what kind of data is most valuable. While those questions remain largely unanswered, the data-capturing imperative invades more and more domains of the educational world.

English language teaching is becoming data-obsessed. From language scales, like Pearson’s Global Scale of English to scales of teacher competences, from numerically-oriented formative assessment practices (such as those used on many LMSs) to the reporting of effect sizes in meta-analyses (such as those used by John Hattie and colleagues), datafication in ELT accelerates non-stop.

The scales and frameworks are all problematic in a number of ways (see, for example, this post on ‘The Mismeasure of Language’) but they have undeniably shaped the way that we are able to think. Of course, we need measurable outcomes! If, for the present, there are privacy and security issues, it is to be hoped that technology will find solutions to them, too.

REFERENCES

Castoriadis, C. (1987). The Imaginary Institution of Society. Cambridge: Polity Press.

Cobo, C. (2019). I Accept the Terms and Conditions. Montevideo: International Development Research Centre / Center for Research Ceibal Foundation. https://adaptivelearninginelt.files.wordpress.com/2020/01/41acf-cd84b5_7a6e74f4592c460b8f34d1f69f2d5068.pdf

Friesen, N. (forthcoming) The technological imaginary in education, or: Myth and enlightenment in ‘Personalized Learning’. In M. Stocchetti (Ed.) The Digital Age and its Discontents. University of Helsinki Press. Available at https://www.academia.edu/37960891/The_Technological_Imaginary_in_Education_or_Myth_and_Enlightenment_in_Personalized_Learning_

Jasanoff, S. & Kim, S.-H. (2015). Dreamscapes of Modernity. Chicago: University of Chicago Press.

Lingard, B. & Gale, T. (2007). The emergent structure of feeling: what does it mean for critical educational studies and research?, Critical Studies in Education, 48:1, pp. 1-23

Moore, J. W. (2015). Capitalism in the Web of Life. London: Verso.

Robbins, K. & Webster, F. (1989]. The Technical Fix. Basingstoke: Macmillan Education.

Taylor, C. (2014). Modern Social Imaginaries. Durham, NC: Duke University Press.

Urry, J. (2016). What is the Future? Cambridge: Polity Press.

 

EdTech: a cautionary tale

When the startup, AltSchool, was founded in 2013 by Max Ventilla, the former head of personalization at Google, it quickly drew the attention of venture capitalists and within a few years had raised $174 million from the likes of the Zuckerberg Foundation, Peter Thiel, Laurene Powell Jobs and Pierre Omidyar. It garnered gushing articles in a fawning edtech press which enthused about ‘how successful students can be when they learn in small, personalized communities that champion project-based learning, guided by educators who get a say in the technology they use’. It promised ‘a personalized learning approach that would far surpass the standardized education most kids receive’.

Ventilla was an impressive money-raiser who used, and appeared to believe, every cliché in the edTech sales manual. Dressed in regulation jeans, polo shirt and fleece, he claimed that schools in America were ‘stuck in an industrial-age model, [which] has been in steady decline for the last century’ . What he offered, instead, was a learner-centred, project-based curriculum providing real-world lessons. There was a focus on social-emotional learning activities and critical thinking was vital.

The key to the approach was technology. From the start, software developers, engineers and researchers worked alongside teachers everyday, ‘constantly tweaking the Personalized Learning Plan, which shows students their assignments for each day and helps teachers keep track of and assess student’s learning’. There were tablets for pre-schoolers, laptops for older kids and wall-mounted cameras to record the lessons. There were, of course, Khan Academy videos. Ventilla explained that “we start with a representation of each child”, and even though “the vast majority of the learning should happen non-digitally”, the child’s habits and preferences gets converted into data, “a digital representation of the important things that relate to that child’s learning, not just their academic learning but also their non-academic learning. Everything logistic that goes into setting up the experience for them, whether it’s who has permission to pick them up or their allergy information. You name it.” And just like Netflix matches us to TV shows, “If you have that accurate and actionable representation for each child, now you can start to personalize the whole experience for that child. You can create that kind of loop you described where because we can represent a child well, we can match them to the right experiences.”

AltSchool seemed to offer the possibility of doing something noble, of transforming education, ‘bringing it into the digital age’, and, at the same time, a healthy return on investors’ money. Expanding rapidly, nine AltSchool microschools were opened in New York and the Bay Area, and plans were afoot for further expansion in Chicago. But, by then, it was already clear that something was going wrong. Five of the schools were closed before they had really got started and the attrition rate in some classrooms had reached about 30%. Revenue in 2018 was only $7 million and there were few buyers for the AltSchool platform. Quoting once more from the edTech bible, Ventilla explained the situation: ‘Our whole strategy is to spend more than we make,’ he says. Since software is expensive to develop and cheap to distribute, the losses, he believes, will turn into steep profits once AltSchool refines its product and lands enough customers.

The problems were many and apparent. Some of the buildings were simply not appropriate for schools, with no playgrounds or gyms, malfunctioning toilets, among other issues. Parents were becoming unhappy and accused AltSchool of putting ‘its ambitions as a tech company above its responsibility to teach their children. […] We kind of came to the conclusion that, really, AltSchool as a school was kind of a front for what Max really wants to do, which is develop software that he’s selling,’ a parent of a former AltSchool student told Business Insider. ‘We had really mediocre educators using technology as a crutch,’ said one father who transferred his child to a different private school after two years at AltSchool. […] We learned that it’s almost impossible to really customize the learning experience for each kid.’ Some parents began to wonder whether AltSchool had enticed families into its program merely to extract data from their children, then toss them aside?

With the benefit of hindsight, it would seem that the accusations were hardly unfair. In June of this year, AltSchool announced that its four remaining schools would be operated by a new partner, Higher Ground Education (a well-funded startup founded in 2016 which promotes and ‘modernises’ Montessori education). Meanwhile, AltSchool has been rebranded as Altitude Learning, focusing its ‘resources on the development and expansion of its personalized learning platform’ for licensing to other schools across the country.

Quoting once more from the edTech sales manual, Ventilla has said that education should drive the tech, not the other way round. Not so many years earlier, before starting AltSchool, Ventilla also said that he had read two dozen books on education and emerged a fan of Sir Ken Robinson. He had no experience as a teacher or as an educational administrator. Instead, he had ‘extensive knowledge of networks, and he understood the kinds of insights that can be gleaned from big data’.

Behaviourism and adaptive learning

In ELT circles, ‘behaviourism’ is a boo word. In the standard history of approaches to language teaching (characterised as a ‘procession of methods’ by Hunter & Smith 2012: 432[1]), there were the bad old days of behaviourism until Chomsky came along, savaged the theory in his review of Skinner’s ‘Verbal Behavior’, and we were all able to see the light. In reality, of course, things weren’t quite like that. The debate between Chomsky and the behaviourists is far from over, behaviourism was not the driving force behind the development of audiolingual approaches to language teaching, and audiolingualism is far from dead. For an entertaining and eye-opening account of something much closer to reality, I would thoroughly recommend a post on Russ Mayne’s Evidence Based ELT blog, along with the discussion which follows it. For anyone who would like to understand what behaviourism is, was, and is not (before they throw the term around as an insult), I’d recommend John A. Mills’ ‘Control: A History of Behavioral Psychology’ (New York University Press, 1998) and John Staddon’s ‘The New Behaviorism 2^nd edition’ (Psychology Press, 2014).

There is a close connection between behaviourism and adaptive learning. Audrey Watters, no fan of adaptive technology, suggests that ‘any company touting adaptive learning software’ has been influenced by Skinner. In a more extended piece, ‘Education Technology and Skinner’s Box’, Watters explores further her problems with Skinner and the educational technology that has been inspired by behaviourism. But writers much more sympathetic to adaptive learning, also see close connections to behaviourism. ‘The development of adaptive learning systems can be considered as a transformation of teaching machines,’ write Kara & Sevim[2] (2013: 114 – 117), although they go on to point out the differences between the two. Vendors of adaptive learning products, like DreamBox Learning©, are not shy of associating themselves with behaviourism: ‘Adaptive learning has been with us for a while, with its history of adaptive learning rooted in cognitive psychology, beginning with the work of behaviorist B.F. Skinner in the 1950s, and continuing through the artificial intelligence movement of the 1970s.’

That there is a strong connection between adaptive learning and behaviourism is indisputable, but I am not interested in attempting to establish the strength of that connection. This would, in any case, be an impossible task without some reductionist definition of both terms. Instead, my interest here is to explore some of the parallels between the two, and, in the spirit of the topic, I’d like to do this by comparing the behaviours of behaviourists and adaptive learning scientists.

Data and theory

Both behaviourism and adaptive learning (in its big data form) are centrally concerned with behaviour – capturing and measuring it in an objective manner. In both, experimental observation and the collection of ‘facts’ (physical, measurable, behavioural occurrences) precede any formulation of theory. John Mills’ description of behaviourists could apply equally well to adaptive learning scientists: theory construction was a seesaw process whereby one began with crude outgrowths from observations and slowly created one’s theory in such a way that one could make more and more precise observations, building those observations into the theory at each stage. No behaviourist ever considered the possibility of taking existing comprehensive theories of mind and testing or refining them.[3]

Positivism and the panopticon

Both behaviourism and adaptive learning are pragmatically positivist, believing that truth can be established by the study of facts. J. B. Watson, the founding father of behaviourism whose article ‘Psychology as the Behaviorist Views It’ set the behaviourist ball rolling, believed that experimental observation could ‘reveal everything that can be known about human beings’[4]. Jose Ferreira of Knewton has made similar claims: We get five orders of magnitude more data per user than Google does. We get more data about people than any other data company gets about people, about anything — and it’s not even close. We’re looking at what you know, what you don’t know, how you learn best. […] We know everything about what you know and how you learn best because we get so much data. Digital data analytics offer something that Watson couldn’t have imagined in his wildest dreams, but he would have approved.

The revolutionary science

Big data (and the adaptive learning which is a part of it) is presented as a game-changer: The era of big data challenges the way we live and interact with the world. […] Society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality[5]. But the reverence for technology and the ability to reach understandings of human beings by capturing huge amounts of behavioural data was adumbrated by Watson a century before big data became a widely used term. Watson’s 1913 lecture at Columbia University was ‘a clear pitch’[6] for the supremacy of behaviourism, and its potential as a revolutionary science.

Prediction and control

The fundamental point of both behaviourism and adaptive learning is the same. The research practices and the theorizing of American behaviourists until the mid-1950s, writes Mills[7] were driven by the intellectual imperative to create theories that could be used to make socially useful predictions. Predictions are only useful to the extent that they can be used to manipulate behaviour. Watson states this very baldly: the theoretical goal of psychology is the prediction and control of behaviour[8]. Contemporary iterations of behaviourism, such as behavioural economics or nudge theory (see, for example, Thaler & Sunstein’s best-selling ‘Nudge’, Penguin Books, 2008), or the British government’s Behavioural Insights Unit, share the same desire to divert individual activity towards goals (selected by those with power), ‘without either naked coercion or democratic deliberation’[9]. Jose Ferreira of Knewton has an identical approach: We can predict failure in advance, which means we can pre-remediate it in advance. We can say, “Oh, she’ll struggle with this, let’s go find the concept from last year’s materials that will help her not struggle with it.” Like the behaviourists, Ferreira makes grand claims about the social usefulness of his predict-and-control technology: The end is a really simple mission. Only 22% of the world finishes high school, and only 55% finish sixth grade. Those are just appalling numbers. As a species, we’re wasting almost four-fifths of the talent we produce. […] I want to solve the access problem for the human race once and for all.

Ethics

Because they rely on capturing large amounts of personal data, both behaviourism and adaptive learning quickly run into ethical problems. Even where informed consent is used, the subjects must remain partly ignorant of exactly what is being tested, or else there is the fear that they might adjust their behaviour accordingly. The goal is to minimise conscious understanding of what is going on[10]. For adaptive learning, the ethical problem is much greater because of the impossibility of ensuring the security of this data. Everything is hackable.

Marketing

Behaviourism was seen as a god-send by the world of advertising. J. B. Watson, after a front-page scandal about his affair with a student, and losing his job at John Hopkins University, quickly found employment on Madison Avenue. ‘Scientific advertising’, as practised by the Mad Men from the 1920s onwards, was based on behaviourism. The use of data analytics by Google, Amazon, et al is a direct descendant of scientific advertising, so it is richly appropriate that adaptive learning is the child of data analytics.

[1] Hunter, D. and Smith, R. (2012) ‘Unpacking the past: “CLT” through ELTJ keywords’. ELT Journal, 66/4: 430-439.

[2] Kara, N. & Sevim, N. 2013. ‘Adaptive learning systems: beyond teaching machines’, Contemporary Educational Technology, 4(2), 108-120

[3] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.5

[4] Davies, W. (2015) The Happiness Industry. London: Verso. p.91

[5] Mayer-Schönberger, V. & Cukier, K. (2013) Big Data. London: John Murray, p.7

[6] Davies, W. (2015) The Happiness Industry. London: Verso. p.87

[7] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.2

[8] Watson, J. B. (1913) ‘Behaviorism as the Psychologist Views it’ Psychological Review 20: 158

[9] Davies, W. (2015) The Happiness Industry. London: Verso. p.88

[10] Davies, W. (2015) The Happiness Industry. London: Verso. p.92

Researching research: part 3

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

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

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

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

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

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

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

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

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

Researching research: part 2

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

I suggested in the previous post that the research of Jayaprakash et al had confirmed something that we already knew concerning the reasons why some students drop out of college. However, predictive analytics are only part of the story. As the authors of this paper point out, they ‘do not influence course completion and retention rates without being combined with effective intervention strategies aimed at helping at-risk students succeed’. The point of predictive analytics is to facilitate the deployment of effective and appropriate interventions strategies, and to do this sooner than would be possible without the use of the analytics. So, it is to these intervention strategies that I now turn.

Interventions to help at-risk students included the following:

• Sending students messages to inform them that they are at risk of not completing the course (‘awareness messaging’)
• Making students more aware of the available academic support services (which could, for example, direct them to a variety of campus-based or online resources)
• Promoting peer-to-peer engagement (e.g. with an online ‘student lounge’ discussion forum)
• Providing access to self-assessment tools

The design of these interventions was based on the work that had been done at Purdue, which was, in turn, inspired by the work of Vince Tinto, one of the world’s leading experts on student retention issues.

The work done at Purdue had shown that simple notifications to students that they were at risk could have a significant, and positive, effect on student behaviour. Jayaprakash and the research team took the students who had been identified as at-risk by the analytics and divided them into three groups: the first were issued with ‘awareness messages’, the second were offered a combination of the other three interventions in the bullet point list above, and the third, a control group, had no interventions at all. The results showed that the students who were in treatment groups (of either kind of intervention) showed a statistically significant improvement compared to those who received no treatment at all. However, there seemed to be no difference in the effectiveness of the different kinds of intervention.

So far, so good, but, once again, I was left thinking that I hadn’t really learned very much from all this. But then, in the last five pages, the article suddenly got very interesting. Remember that the primary purpose of this whole research project was to find ways of helping not just at-risk students, but specifically socioeconomically disadvantaged at-risk students (such as those receiving Pell Grants). Accordingly, the researchers then focussed on this group. What did they find?

Once again, interventions proved more effective at raising student scores than no intervention at all. However, the averages of final scores are inevitably affected by drop-out rates (since students who drop out do not have final scores which can be included in the averages). At Purdue, the effect of interventions on drop-out rates had not been found to be significant. Remember that Purdue has a relatively well-off student demographic. However, in this research, which focussed on colleges with a much higher proportion of students on Pell Grants, the picture was very different. Of the Pell Grant students who were identified as at-risk and who were given some kind of treatment, 25.6% withdrew from the course. Of the Pell Grant students who were identified as at-risk but who were not ‘treated’ in any way (i.e. those in the control group), only 14.1% withdrew from the course. I recommend that you read those numbers again!

The research programme had resulted in substantially higher drop-out rates for socioeconomically disadvantaged students – the precise opposite of what it had set out to achieve. Jayaprakash et al devote one page of their article to the ethical issues this raises. They suggest that early intervention, resulting in withdrawal, might actually be to the benefit of some students who were going to fail whatever happened. It is better to get a ‘W’ (withdrawal) grade on your transcript than an ‘F’ (fail), and you may avoid wasting your money at the same time. This may be true, but it would be equally true that not allowing at-risk students (who, of course, are disproportionately from socioeconomically disadvantaged backgrounds) into college at all might also be to their ‘benefit’. The question, though, is: who has the right to make these decisions on behalf of other people?

The authors also acknowledge another ethical problem. The predictive analytics which will prompt the interventions are not 100% accurate. 85% accuracy could be considered a pretty good figure. This means that some students who are not at-risk are labelled as at-risk, and other who are at-risk are not identified. Of these two possibilities, I find the first far more worrying. We are talking about the very real possibility of individual students being pushed into making potentially life-changing decisions on the basis of dodgy analytics. How ethical is that? The authors’ conclusion is that the situation forces them ‘to develop the most accurate predictive models possible, as well as to take steps to reduce the likelihood that any intervention would result in the necessary withdrawal of a student’.

I find this extraordinary. It is premised on the assumption that predictive models can be made much, much more accurate. They seem to be confusing prediction and predeterminism. A predictive model is, by definition, only predictive. There will always be error. How many errors are ethically justifiable? And, the desire to reduce the likelihood of unnecessary withdrawals is a long way from the need to completely eliminate the likelihood of unnecessary withdrawals, which seems to me to be the ethical position. More than anything else in the article, this sentence illustrates that the a priori assumption is that predictive analytics can be a force for good, and that the only real problem is getting the science right. If a number of young lives are screwed up along the way, we can at least say that science is getting better.

In the authors’ final conclusion, they describe the results of their research as ‘promising’. They do not elaborate on who it is promising for. They say that relatively simple intervention strategies can positively impact student learning outcomes, but they could equally well have said that relatively simple intervention strategies can negatively impact learning outcomes. They could have said that predictive analytics and intervention programmes are fine for the well-off, but more problematic for the poor. Remembering once more that the point of the study was to look at the situation of socioeconomically disadvantaged at-risk students, it is striking that there is no mention of this group in the researchers’ eight concluding points. The vast bulk of the paper is devoted to technical descriptions of the design and training of the software; the majority of the conclusions are about the validity of that design and training. The ostensibly intended beneficiaries have got lost somewhere along the way.

How and why is it that a piece of research such as this can so positively slant its results? In the third and final part of this mini-series, I will turn my attention to answering that question.

Researching research: part 1

In the 8^th post on this blog (‘Theory, Research and Practice’), I referred to the lack of solid research into learning analytics. Whilst adaptive learning enthusiasts might disagree with much, or even most, of what I have written on this subject, here, at least, was an area of agreement. May of this year, however, saw the launch of the inaugural issue of the Journal of Learning Analytics, the first journal ‘dedicated to research into the challenges of collecting, analysing and reporting data with the specific intent to improve learning’. It is a peer-reviewed, open-access journal, available here , which is published by the Society for Learning Analytics Research (SoLAR), a consortium of academics from 9 universities in the US, Canada, Britain and Australia.

I decided to take a closer look. In this and my next two posts, I will focus on one article from this inaugural issue. It’s called Early Alert of Academically At‐Risk Students: An Open Source Analytics Initiative and it is co-authored by Sandeep M. Jayaprakash, Erik W. Moody, Eitel J.M. Lauría, James R. Regan, and Joshua D. Baron of Marist College in the US. Bear with me, please – it’s more interesting than it might sound!

The background to this paper is the often referred to problem of college drop-outs in the US, and the potential of learning analytics to address what is seen as a ‘national challenge’. The most influential work that has been done in this area to date was carried out at Purdue University. Purdue developed an analytical system, called Course Signals, which identified students at risk of course failure and offered a range of interventions (more about these in the next post) which were designed to improve student outcomes. I will have more to say about the work at Purdue in my third post, but, for the time being, it is enough to say that, in the field, it has been considered very successful, and that the authors of the paper I looked at have based their approach on the work done at Purdue.

Jayaprakash et al developed their own analytical system, based on Purdue’s Course Signals, and used it at their own institution, Marist College. Basically, they wanted to know if they could replicate the good results that had been achieved at Purdue. They then took the same analytical system to four different institutions, of very different kinds (public, as opposed to private; community colleges offering 2-year programmes rather than universities) to see if the results could be replicated there, too. They also wanted to find out if the interventions with students who had been signalled as at-risk would be as effective as they had been at Purdue. So far, so good: it is clearly very important to know if one particular piece of research has any significance beyond its immediate local context.

So, what did Jayaprakash et al find out? Basically, they learnt that their software worked as well at Marist as Course Signals had done at Purdue. They collected data on student demographics and aptitude, course grades and course related data, data on students’ interactions with the LMS they were using and performance data captured by the LMS. Oh, yes, and absenteeism. At the other institutions where they trialled their software, the system was 10% less accurate in predicting drop-outs, but the authors of the research still felt that ‘predictive models developed based on data from one institution may be scalable to other institutions’.

But more interesting than the question of whether or not the predictive analytics worked is the question of which specific features of the data were the most powerful predictors. What they discovered was that absenteeism was highly significant. No surprises there. They also learnt that the other most powerful predictors were (1) the students’ cumulative grade point average (GPA), an average of a student’s academic scores over their entire academic career, and (2) the scores recorded by the LMS of the work that students had done during the course which would contribute to their final grade. No surprises there, either. As the authors point out, ‘given that these two attributes are such fundamental aspects of academic success, it is not surprising that the predictive model has fared so well across these different institutions’.

Agreed, it is not surprising at all that students with lower scores and a history of lower scores are more likely to drop out of college than students with higher scores. But, I couldn’t help wondering, do we really need sophisticated learning analytics to tell us this? Wouldn’t any teacher know this already? They would, of course, if they knew their students, but if the teacher: student ratio is in the order of 1: 100 (not unheard of in lower-funded courses delivered primarily through an LMS), many teachers (and their students) might benefit from automated alert systems.

But back to the differences between the results at Purdue and Marist and at the other institutions. Why were the predictive analytics less successful at the latter? The answer is in the nature of the institutions. Essentially, it boils down to this. In institutions with low drop-out rates, the analytics are more reliable than in institutions with high drop-out rates, because the more at-risk students there are, the harder it is to predict the particular individuals who will actually drop out. Jayaprakash et al provide the key information in a useful table. Students at Marist College are relatively well-off (only 16% receive Pell Grants, which are awarded to students in financial need), and only a small number (12%) are from ‘ethnic minorities’. The rate of course non-completion in normal time is relatively low (at 20%). In contrast, at one of the other institutions, the College of the Redwoods in California, 44% of the students receive Pell Grants and 22% of them are from ‘ethnic minorities’. The non-completion rate is a staggering 96%. At Savannah State University, 78% of the students receive Pell Grants, and the non-completion rate is 70%. The table also shows the strong correlation between student poverty and high student: faculty ratios.

In other words, the poorer you are, the less likely you are to complete your course of study, and the less likely you are to know your tutors (these two factors also correlate). In other other words, the whiter you are, the more likely you are to complete your course of study (because of the strong correlations between race and poverty). While we are playing the game of statistical correlations, let’s take it a little further. As the authors point out, ‘there is considerable evidence that students with lower socio-economic status have lower GPAs and graduation rates’. If, therefore, GPAs are one of the most significant predictors of academic success, we can say that socio-economic status (and therefore race) is one of the most significant predictors of academic success … even if the learning analytics do not capture this directly.

Actually, we have known this for a long time. The socio-economic divide in education is frequently cited as one of the big reasons for moving towards digitally delivered courses. This particular piece of research was funded (more about this in the next posts) with the stipulation that it ‘investigated and demonstrated effective techniques to improve student retention in socio-economically disadvantaged populations’. We have also known for some time that digitally delivered education increases the academic divide between socio-economic groups. So what we now have is a situation where a digital technology (learning analytics) is being used as a partial solution to a problem that has always been around, but which has been exacerbated by the increasing use of another digital technology (LMSs) in education. We could say, then, that if we weren’t using LMSs, learning analytics would not be possible … but we would need them less, anyway.

My next post will look at the results of the interventions with students that were prompted by the alerts generated by the learning analytics. Advance warning: it will make what I have written so far seem positively rosy.

Efficacy and evidence

Pearson’s ‘Efficacy’ initiative is a series of ‘commitments designed to measure and increase the company’s impact on learning outcomes around the world’. The company’s dedicated website  offers two glossy brochures with a wide range of interesting articles, a good questionnaire tool that can be used by anyone to measure the efficacy of their own educational products or services, as well as an excellent selection of links to other articles, some of which are critical of the initiative. These include Michael Feldstein’s long blog post  ‘Can Pearson Solve the Rubric’s Cube?’ which should be a first port of call for anyone wanting to understand better what is going on.

What does it all boil down to? The preface to Pearson’s ‘Asking More: the Path to Efficacy’ by CEO John Fallon provides a succinct introduction. Efficacy in education, says Fallon, is ‘making a measurable impact on someone’s life through learning’. ‘Measurable’ is the key word, because, as Fallon continues, ‘it is increasingly possible to determine what works and what doesn’t in education, just as in healthcare.’ We need ‘a relentless focus’ on ‘the learning outcomes we deliver’ because it is these outcomes that can be measured in ‘a systematic, evidence-based fashion’. Measurement, of course, is all the easier when education is delivered online, ‘real-time learner data’ can be captured, and the power of analytics can be deployed.

Pearson are very clearly aligning themselves with recent moves towards a more evidence-based education. In the US, Obama’s Race to the Top is one manifestation of this shift. Britain (with, for example, the Education Endowment Foundation) and France (with its Fonds d’Expérimentation pour la Jeunesse ) are both going in the same direction. Efficacy is all about evidence-based practice.

Both the terms ‘efficacy’ and ‘evidence-based practice’ come originally from healthcare. Fallon references this connection in the quote two paragraphs above. In the UK last year, Ben Goldacre (medical doctor, author of ‘Bad Science’ and a relentless campaigner against pseudo-science) was commissioned by the UK government to write a paper entitled ‘Building Evidence into Education’ . In this, he argued for the need to introduce randomized controlled trials into education in a similar way to their use in medicine.

As Fallon observed in the preface to the Pearson ‘Efficacy’ brochure, this all sounds like ‘common sense’. But, as Ben Goldacre discovered, things are not so straightforward in education. An excellent article in The Guardian outlined some of the problems in Goldacre’s paper.

With regard to ELT, Pearson’s ‘Efficacy’ initiative will stand or fall with the validity of their Global Scale of English, discussed in my March post ‘Knowledge Graphs’ . However, there are a number of other considerations that make the whole evidence-based / efficacy business rather less common-sensical than might appear at first glance.

• The purpose of English language teaching and learning (at least, in compulsory education) is rather more than simply the mastery of grammatical and lexical systems, or the development of particular language skills. Some of these other purposes (e.g. the development of intercultural competence or the acquisition of certain 21^st century skills, such as creativity) continue to be debated. There is very little consensus about the details of what these purposes (or outcomes) might be, or how they can be defined. Without consensus about these purposes / outcomes, it is not possible to measure them.
• Even if we were able to reach a clear consensus, many of these outcomes do not easily lend themselves to measurement, and even less to low-cost measurement.
• Although we clearly need to know what ‘works’ and what ‘doesn’t work’ in language teaching, there is a problem in assigning numerical values. As the EduThink blog observes, ‘the assignation of numerical values is contestable, problematic and complex. As teachers and researchers we should be engaging with the complexity [of education] rather than the reductive simplicities of [assigning numerical values]’.
• Evidence-based medicine has resulted in unquestionable progress, but it is not without its fierce critics. A short summary of the criticisms can be found here .  It would be extremely risky to assume that a contested research procedure from one discipline can be uncritically applied to another.
• Kathleen Graves, in her plenary at IATEFL 2014, ‘The Efficiency of Inefficiency’, explicitly linked health care and language teaching. She described a hospital where patient care was as much about human relationships as it was about medical treatment, an aspect of the hospital that went unnoticed by efficiency experts, since this could not be measured. See this blog for a summary of her talk.

These issues need to be discussed much further before we get swept away by the evidence-based bandwagon. If they are not, the real danger is that, as John Fallon cautions, we end up counting things that don’t really count, and we don’t count the things that really do count. Somehow, I doubt that an instrument like the Global Scale of English will do the trick.

Part 4: big data and analytics

In order to understand more complex models of adaptive learning, it is necessary to take a temporary step sideways away from the world of language learning. Businesses have long used analytics – the analysis of data to find meaningful patterns – in insurance, banking and marketing. With the exponential growth in computer processing power and memory capacity, businesses now have access to volumes of data of almost unimaginable size. This is known as ‘big data’ and has been described as ‘a revolution that will transform how we live, work and think’ (Mayer-Schönberger & Cukier, ‘Big Data’, 2013). Frequently cited examples of the potential of big data are the success of Amazon to analyze and predict buying patterns and the use of big data analysis in Barack Obama’s 2012 presidential re-election. Business commentators are all singing the same song on the subject. This will be looked at again in later posts. For the time being, it is enough to be aware of the main message. ‘The high-performing organisation of the future will be one that places great value on data and analytical exploration’ (The Economist Intelligence Unit, ‘In Search of Insight and Foresight: Getting more out of big data’ 2013, p.15). ‘Almost no sphere of business activity will remain untouched by this movement,’ (McAfee & Brynjolfsson, ‘Big Data: The Management Revolution’, Harvard Business Review (October 2012), p. 65).

With the growing bonds between business and education (another topic which will be explored later), it is unsurprising that language learning / teaching materials are rapidly going down the big data route. In comparison to what is now being developed for ELT, the data that is analyzed in the adaptive learning models I have described in an earlier post is very limited, and the algorithms used to shape the content are very simple.

The volume and variety of data and the speed of processing are now of an altogether different order. Jose Ferreira, CEO of Knewton, one of the biggest players in adaptive learning in ELT, spells out the kind of data that can be tapped[1]:

At Knewton, we divide educational data into five types: one pertaining to student identity and onboarding, and four student activity-based data sets that have the potential to improve learning outcomes. They’re listed below in order of how difficult they are to attain:

1) Identity Data: Who are you? Are you allowed to use this application? What admin rights do you have? What district are you in? How about demographic info?

2) User Interaction Data: User interaction data includes engagement metrics, click rate, page views, bounce rate, etc. These metrics have long been the cornerstone of internet optimization for consumer web companies, which use them to improve user experience and retention. This is the easiest to collect of the data sets that affect student outcomes. Everyone who creates an online app can and should get this for themselves.

3) Inferred Content Data: How well does a piece of content “perform” across a group, or for any one subgroup, of students? What measurable student proficiency gains result when a certain type of student interacts with a certain piece of content? How well does a question actually assess what it intends to? Efficacy data on instructional materials isn’t easy to generate — it requires algorithmically normed assessment items. However it’s possible now for even small companies to “norm” small quantities of items. (Years ago, before we developed more sophisticated methods of norming items at scale, Knewton did so using Amazon’s “Mechanical Turk” service.)

4) System-Wide Data: Rosters, grades, disciplinary records, and attendance information are all examples of system-wide data. Assuming you have permission (e.g. you’re a teacher or principal), this information is easy to acquire locally for a class or school. But it isn’t very helpful at small scale because there is so little of it on a per-student basis. At very large scale it becomes more useful, and inferences that may help inform system-wide recommendations can be teased out.

5) Inferred Student Data: Exactly what concepts does a student know, at exactly what percentile of proficiency? Was an incorrect answer due to a lack of proficiency, or forgetfulness, or distraction, or a poorly worded question, or something else altogether? What is the probability that a student will pass next week’s quiz, and what can she do right this moment to increase it?

Software of this kind keeps complex personal profiles, with millions of variables per student, on as many students as necessary. The more student profiles (and therefore students) that can be compared, the more useful the data is. Big players in this field, such as Knewton, are aiming for student numbers in the tens to hundreds of millions. Once data volume of this order is achieved, the ‘analytics’, or the algorithms that convert data into ‘actionable insights’ (J. Spring, ‘Education Networks’ (New York: Routledge, 2012), p.55) become much more reliable.

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[1] http://www.knewton.com/blog/knewton/from-jose/2013/07/18/big-data-in-education/ (last accessed 9 December 2013)

Part 3: gamification

An integral part of adaptive learning programs, both the simple models already described and the much more complex systems that are currently under development, is an element of gamification. The term refers to the incorporation of points, levels (analogous to the levels in a typical computer game) and badges into the learning experience. In Duolingo, for example, users have a certain number of ‘lives’ that they can afford to lose without failing an exercise. In addition, they can compare their performance with that of other users, and they can win ‘lingots’, a kind of in-game currency which allows them to ‘buy’ lost ‘lives or to compensate for a day of inactivity.

Gamification and adaptive learning go together like hand in glove because of the data that is generated by the adaptive software (see the next post: Big data, analytics and adaptive learning). The whole thing is premised on comparing the performance of different students, so score cards and leader boards and so on are hardly surprising.

The idea behind this, in case it needs pointing out, is that it can make learning fun and, so, students will be more motivated to do the work, which seems more like play. It is a much hyped idea in education: eltjam referred to the ‘snowballing sexiness’ of the term. In an ELT context, most references to gamification are very positive. See, for example, eltjam’s blog post on the subject or Graham Stanley’s conference presentation on the subject. An excellent infographic summary of and advertisement for the benefits of gamification can be found at the Knewton website.

Not everyone, however, is so positive. Gamification has been described by some writers and researchers as the ‘pointsification’ of everything – the reductionist process of regarding all actions with points and increased personal scores (see, for example, Neil Selwyn, 2013, Distrusting Educational Technology, p.101). The motivation it may generate is clearly extrinsic, and this may not be a good long-term bet. Adults (myself included) get bored of gamification elements very quickly. For both adults and younger learners, once you’ve figured out how to play the system and get extra points (and there’s always a way of finding shortcuts to do this), interest can wane quickly. And once gamification becomes a standard feature of educational experiences (and not just English language learning), its novelty value will disappear.