10 inconvenient truths that show ‘diversity’ as wrong-headed

Goran Adamson’s The Trojan Horse (A Leftist critique of Multiculturism in the West) is a searing account of the failure of the diversity driven agenda. His detailed examination of diversity in Sweden is hard hitting. He calls it out as wrong-headed, counterproductive and conservative. It makes one think deeply about the subject, especially the ‘diversity’ industry, touting ‘diversity courses’. Several dimensions of the diversity agenda are identified as wanting, even dangerous. Other research is also damning. Major studies from Dobbin, Kalev and Kochan show that diversity training does not increase productivity and may, in fact, produce a backlash. Most don’t know if it works as evaluations are as rare as unicorns. It’s all feels a bit odd, out of touch and, in terms of evidence, in need of a rethink.

1. Ideology of diversity

‘Diversity’ is a word that often cannot be questioned nor the need for 'courses' in the subject. The rhetoric that surrounds diversity in itself seems to censor debate, a diversity of views being the first victim. The word triggers silence and doesn’t tolerate dissent. One must not question the idea of diversity as an absolute good. As employers and employees we are expected to accept that we are in a state of sin regarding diversity and must go through some sort of confession process, facing up to our weaknesses, through ‘diversity’ courses, to absolve our sin. This is unhealthy, as it is in need of research and evaluation.

In fact, diversity training is largely damned by the research. The evidence shows it has become an end in itself, rather than a means to an end. The vast amount of time and money spent on diversity training, when evaluated, is found wanting, mostly ineffective, even counter-productive. With evidence from large-scale studies, from Dobbin, Kalev and Kochan, as well as many other focused pieces of research, you'd have thought that the message would have got through. The sad truth is that few on either the supply or demand side, even know about the research, whether it works or not. It's become an article of faith.

2. Groupthink

Companies worldwide spend many hundreds of millions of dollars each year on diversity training. The tragic truth is that most of this is wasted. Groupthink seems to be at the heart of the matter. Groupthink among compliance training companies, who simply do what they do without supporting evidence and tout ineffective ‘courses’. Groupthink in HR, who find it easier to just run ‘courses’ rather than tackle real business problems. The whole edifice is a house of cards.

3. Ill-defined

It is not clear that there is a solid definition of ‘diversity’. You can’t just say ‘difference’ that’s too loose. One could invoke the idea that individuals are unique, and this uniqueness is paramount. Unfortunately, it too often focuses on collective ideas of race, ethnicity, gender, sexual orientation, age, physical abilities, religious beliefs, political beliefs, or other ideologies (socio-economic status is often strangely absent or ignored). But ethnicity, gender and so on are terms associated with the collective, not the individual. Yet, when recruiting, the real individual needs are not things you were born with or into, but unique skills or the need to train in those skills.Adamson's research across Swedish Universities, showed a strange absence of definitions. Any definition of diversity is glossed over and replaced with diversity plans.

4. Lazy cultural relativism

Instead, a lazy cultural relativism, the idea thatall cultures are equally as valid or good (moral relativism) descends, disallowing criticism of illiberal cultural norms. Freedom of speech is under attack from ‘trigger theory’, art is censored, honour crime not ruthlessly dealt with, FGM still prevalent. 

5. Not an intrinsic good?

Is the diverse always better than the similar or alike? Is polygamy better than monogamy? Will your coding team always benefit from having an even gender and ethnic mix or a ruthless focus on competence? Diversity rhetoric praises ethnic presence but could be a substitute for excellence and ideas? It is not enough to say that differences are always good, that x+y+z > x+x+x. The heterogeneous is not always better than the homogeneous. It can be but hte diversity myth is that it always does.

6. Diversity as conservatism

One could also argue that diversity is a deeply conservative idea, masquerading as progressive. It replaces meritocracy with multiculturalism. The ideology of diversity has led to a focus on the vertical divisions of ethnicity, at the expense of horizontal divisions of class, even gender. From a feminist point of view, diversity may tolerate attitudes, cultural norms and behaviours that may prevent gender equality. It prevents us from taking a secular view of the world, as we give in to relativism and acceptance. The demotion of ‘equality of opportunity’ by ‘equality of outcome’ is another product of the diversity movement. The group is valued more than the individual. It pits the poor against the poor. Ultimately, it is the dull traditionalism of conservatism.

7. Diversity does not lead to increased productivity.

Thomas Kochan, Professor of management at MIT’s Sloan School of Management’s five year study had previously come to the same conclusions, "The diversity industry is built on sand," he concluded. "The business case rhetoric for diversity is simply naive and overdone. There are no strong positive or negative effects of gender or racial diversity on business performance." The problem, according to Kochan, is the bogus claim that diversity training leads to increased productivity. This is simply unproven as there is little or no hard data on the subject.

8. Diversity shows virtually no effect

Harvard’s Frank Dobbin conducted the first major, systematic study of diversity programmes across 708 private sector companies, using employment data and surveys on employment practices. His research concluded that, “Practices that target managerial bias through…diversity training, show virtually no effect.” The research is a very thorough piece of work, and well worth reading, which is why it was completely ignored.

9. More harm than good

Dobbins research went further. “Research to date suggests that… training often generates a backlash.” Many other studies show that diversity training has activated, rather than reduced diversity (Kidder et al 2004, Rynes and Rosen 1995, Sidanias et al 2001, Naff and Kellough 2003, Benedict et al 1998, Nelson et al 1996). These are all referenced in the report. Louise Pendry of Exeter University claims that there’s no evaluative evidence showing that these programmes work. Even worse, many may do more harm than good. Tracie Stewart, a professor at Georgia University, has identified "backlash" or "victim blame", after some courses, where the learners harbour resentment against other minority groups for the way they are made to feel. Rather than bringing people together, it may be reinforcing differences.

10. No evaluation

Most diversity training is not evaluated at all or languishes in the Kirkpatrick Level 1, la la land of ‘happy sheets’. So check out Alexandra Kalev’s study from the University of Arizona. 31 years of data from 830 companies – how’s that for a Level 4 evaluative study! Her latest study found, after the delivery of diversity training, a 7.5% DROP in women managers, 10% DROP in black women managers and a 12% DROP in black men in senior management positions. There were similar DROPS among Latinos and Asians. Kochan found that none of the companies he contacted for his study had carried out any systematic evaluation of diversity training. Evidence around productivity is mostly anecdotal and repeated as a mantra by interested parties.

The strength of this study comes from the quantity and integrity of the data. It relies on compulsory federal EEOC (Equal Employment Opportunity Commission) filings on the number of women and people of colour in management, along with details of diversity training programmes.

The bottom line is that the vast majority of diversity courses are useless, especially when driven by HRs perception of avoiding prosecution. The problem centres around courses run in response to legislative and external pressures. Kalev found that, "Most employers….force their managers and workers to go through training, and this is the least effective option in terms of increasing diversity. . . . Forcing people to go through training creates a backlash against diversity." Diversity courses are “more symbolic than substantive" says University of California LAW Professor, Lauren Edelman, She independently reviewed Kalev's study and concluded that the problem was training in "response to the general legal environment and the fact that organizations copy one another."

Solutions

One of the problems, that Dobbin, Kalev and Kochan found, was the focus on ‘sensitivity training’ where people are often forced to focus on interpersonal conflict. These were the training courses that produced a backlash, as they were intrinsically accusatory. One bright spot was the finding that some diversity initiatives, namely those that were voluntary and aligned with business goals, were successful. This is similar to Professor Frank Dobbin’s study at Harvard, who showed, in his massive study that ‘training’ was not the answer, and that other management interventions were much better, such as mentoring.

The trick, I feel, is to drop diversity courses and look at other direct actions. Start with blind recruitment, removing names and other details fro applications. Force boards to recruit openly. Demand that apprenticeship schemes be adopted. Focus on competence not culture I training. Finally, except in cases where it is necessary, drop the ‘graduate’ requirement. The trick is to have a more open door of opportunity, not a closed door of pre-determined outcomes.

Collaborative AI is learning from shared experience – collective intelligence is here and it’s terrifying

AI is moving fast, scoring one victory after another in specific domains. However, the main problem AI has is in moving from one domain to another. It may be great at playing chess or GO, or other rules based games, but when it comes to other simple but different problems, it is not flexible. This problem, getting AI to be more general in terms of its skills or learn to apply what it learned in one domain to another, is a serious limitation, perhaps the greatest limitation of current AI.

Human-all-too-human

We humans have a different but no less debilitating problem – our brains. We literally have to spend up to 20 years or more in classrooms being painstakingly taught by other humans to acquire knowledge and skills. Even then, it’s only a start on the long road that is lifelong learning. That’s because we cannot efficiently transfer knowledge and skills directly from one brain to another. It cannot be uploaded and downloaded. In addition to this limitation, we forget most of what we are taught, sleep 8 hours a day, are largely inattentive for much of the remaining 16 hours, get ill and die. Artificial intelligence has none of these constraints.

Cloud robotics

One solution to the learning problem in AI, now being practised in robotics, is ‘cloud robotics’, where one robot can literally ‘teach’ another. By teach, I mean, pass and share its acquired skills on to other robots. This is a bit mind blowing, as it is something we cannot do as humans.

Google and others have been experimenting with cloud robotics for some years, where robots learn how to do something, through neural networks and reinforcement learning (trial and error) and once they have acquired that skill, it can be uploaded to as many other robots as you want. They literally share experiences and therefore learning. Not only do the sets of robot learn quickly, they instantly share that learning with other networked robots. This whole idea of AI learning from collective or shared experience is fascinating.

Google have been research three different (but not mutually exclusive) techniques for teaching or training multiple robots collectively, by allowing them to share experiences and learn general purpose skills:

1. learning motion skills directly from experience

2. learning internal models of physics

3. learning skills with human assistance

Shared experience, in all three of these forms, clearly takes less time than a single robot acquiring its own experiences. But it’s not only time that shrinks, you also get the benefit of variation in those experiences, more diversity of experience. Deeper learning, in terms of both quantity and quality, takes place that can cope with more complex environments and problems. This sharing of experience, whether trial and error tasks, models that are built or human data that is used to train robots can be networked, shared and seen as a form of pooled, collective intelligence.
Why is this frightening? Robots are already decimating the manufacturing sector. The possibility that generalpurpose robots will be more flexible and able to do all sorts of tasks as they learn from each other, is frightening in the sense that this takes them beynd the specifics of manufacturing.

Collective intelligence

Collective intelligence, a term coined by Pierre Levy, was defined by him as,

“a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills"

But Levy’s theory of collective intelligence is now dated and inadequate. Firstly, it has an inadequate definition of ‘collective’ that has been superseded by recent developments, not only in social media but in other forms of technology such as AI. Secondly, it has an inadequate definition of ‘intelligence’ that has been superseded by recent thinking about ‘networks’ and ‘intelligence’.

Networks and intelligence

More attention needs to be given to the nature and role of ‘networks’ in collective intelligence. Some philosophers posit the idea that networks are intelligent, to a degree, simply by virtue of being networks. Our brains are networks, indeed the most complex networks we know of, and artificial intelligence uses that same (or similar) networked power to interact with our brains. We do not learn in a linear fashion, like video recordings nor do we remember things alphabetically or hierarchically. Our brains are networks with pre-existing knowledge, and intelligence, that needs to fit with other forms of knowledge from networks.

Connectivism

The theory of connectivism, proposed by Stephen Downes and George Siemens posits ‘Connectivism’, as a theory where “knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks”. It is an alternative to behaviourism, cognitivism and constructionism. ‘Connectivism’ focuses on the connections, not the meanings or structures connected across networks. Intelligence, existing and acquired, is the practices, by both teachers and learners, that result in the formation and use of effective networks with properties such as diversity, autonomy, openness, and connectivity. This challenges the existing paradigms, that do not take into account the explosion of network technology, as well as presenting a new perspective on collective use and intelligence. Connectivism can also be used to bring in newer technological advances and newer agents - such as artificial intelligence.

Koch argues that the line for consciousness and intelligence has changed to include animals, even insects, indeed anything with a network of neurons. He takes includes any communicating network. We have evidence that consciousness and intelligence is related to networked activity in both organic brains and non-organic neural networks. Could it be that intelligence is simply a function of this networking and that all networked entities are, to some degree, intelligent?

AI and collective intelligence

There are several recent technological developments that open up the possibility of collective intelligence. The most important new technologies is AI (Artificial Intelligence). Artificial Intelligence is embedded in many online media experiences. This takes us beyond the current flat, largely text-based, hyperlinked world of text and images, such as Wikipedia, or even social media, into forms of media that are closer to Lévy’s original idea of collective intelligence. Networks store knowledge but, with the advent of online AI, they can also be said to BE intelligent. AI is intelligence that resides in a network and is intelligent ‘in itself’ but also adds to the sum of collective intelligence when used by humans.

Machine learning (code that learns and creates code), with the aid of collective human and machine created data, actually becomes more ‘intelligent’. The more it is used the more intelligent it becomes, sometimes surpassing the intelligence of humans, in specific domains. As we saw at the start of this article, it can now also be shared. This is a new species of shared intelligence that is shared in realtime, direct  and scalable.

Collective Artificial Intelligence, let’s call it CAI, can be said to reside in and be an emergent feature of all networks, human and artificial, organic and non-organic. In other words, the agents of collective intelligence have to be widened, as does the nature of that intelligence and the interaction between them all.

Conclusion

We are only just beginning to see and practically explore and build new forms of collective intelligence that allow teaching and learning to be done by machines very quickly and on a massive scale. This is exhilarating and frightening at the same time. We, as humans, are now part of a networked nexus of human teachers, human learners, AI teachers, AI learners, networked knowledge, and networked skills. The world has suddenly become a lot more complex.

(Thanks To Callum Clark for the ideas on Levi and collective intelligence.)

7 reasons why teachers believe, wrongly, in ‘Learning Styles’

Now I don’t want to do another piece on the evidence that learning styles do NOT exist but I do want to expose the reasons for their widespread belief. Surveys consistently show that the vast majority of teachers, trainers and lecturers believe in learning styles. Despite decades of research showing that the theories are bogus, the belief persists. It is so ubiquitous that it’s hard to attend an educational conference without hearing the phrase being repeatedly parroted. Seasoned campaigners shake their heads in disbelief every time they hear the term but it is so deep-rooted it seems to be impossible to shift. This is a real conundrum.

1. Villain is intuition

One could argue that these professions are in a pre-Copernican state, believing that the sun moves around the earth. Their only appeal is the same as the pre-Copernicans - look the sun moves across the sky, it feels right to me. Another analogy would be flat-earthers – everywhere they look they see that the land is flat, so the entire planet must be flat. There is always a villain and in this case it is intuition. In both cases ‘intuition’ trumps reality, where limited personal intuition fools the mind.

2. Category mistake

Puzzlingly, even when the evidence is presented, that the truth is the opposite of what one thinks, it is ignored. I get this. It feels as though learners are different. They are. But the non sequitur is to think that they should learn differently. The differences are in ‘personality’, which are real. These are then translated into the fiction of learning styles. It is a category mistake. There are many complex issues at play here but the simple fact that people differ in terms of well–researched  personality traits, is mistaken for ‘learning styles’. There is one complication here, in that some learners have conditions that can inhibit and distort learning; learning difficulties, disabilities, dyslexia, autism and so on. But these should not be confused with generalised learning styles.

3. Simple models

Learning styles is a set of theories. Coffield found 71 of them, surely a sign that something is amiss? But the appeal of some of the more common theories seems to come down to two things. First, they are represented as researched, evidenced and science, when they are not. Second, they are simple models, such as VAK or Honey and Mumford’s 4 categories, which are simplistic, easy to learn, easy to put on a training PoewerPoint slide and all too easy to explain. The danger here is that their categories are treated as fixed entities with no statistical distribution or overlap. The data gathering to decide what style a person has is also woeful. This promotes a lack of critical thinking.

4. Anti-intellectualism

Other forces are also at work here. Teaching, as practised by teachers, trainers and lecturers, is not, like ‘medicine’ or ‘engineering’ - evidence or even research-based. In fact, the research is treated with great suspicion. Many who teach, especially in higher education, in research institutions where they should know better, have no real knowledge of what good teaching entails or how people learn. The defence you often hear is that teaching is a ‘practice’ and not the application of theory, evidence-based or otherwise. The problem with this defence, is that it simple begs the question ‘What practice?’ We still need some way to distinguish good from bad practice. This anti-intellectualism allows those who teach to literally do what they want even down to believing and applying false and damaging theories.

5. Professional bodies

Professional bodies are also to blame, having blindly regurgitated old theory in courses (which they sell), for decades. One really does have to ask what teacher training has been up to for all these years, when their student-teachers come out as flat-earthers? Why don’t they come out and say what needs to be said? For many years professional bodies, such as the CIPD and ASTD, who survive on running courses, promoted these practices. In practice, they tended to reinforce these faddish theories, as they made money from them, and it was left to researchers and bloggers to do the hard work. To be fair some have moved on, especially the CIPD.

6. Poor CPD

Few read much in the field and CPD is scarce and often faddish. Courses often contain the standard memes such as Maslow and Learning Styles, as there is no real intellectual rigour in their design. Those that pass for experts have actually cobbled together their courses from previous courses. Paschler is right in identifying a vast industry of conferences, workshops, courses, books and CPD around learning styles, that perpetuate the myths.

7. Groupthink

Lastly, we have groupthink. People hear the terms so often that they believe them to be true. They become memes in a community, uncritically used and deeply embedded in a culture, a culture prone to taking things at face value. Questioning these sacred cows becomes an act of betrayal. Teachers feel good in themselves because they feel as though they are treating learners as unique individuals, when what they are actually doing is the very opposite – stereotyping and destroying learning.

Stereotyping

Finally, and this is the killer argument for me, even if learning styles were true, stereotyping learners is dangerous, if not counterproductive. Let’s suppose I do have a disposition towards not ‘reading’. This could be because I come from a background (like me) where there is no culture of reading, a household that has no books. This disposition should not be used to focus on visual, auditory or kinaesthetic learning. What a good teacher should do is teach that child to read, perhaps put even more effort into that practice. The learners that lose out here are the poor and disadvantaged who are stereotyped into low achieving channels. It is easy to feel as though learners are different (they are) but to categorise them as VAK, or some equally as vakous schema, is a big mistake. At the University of Illinois, they found that students who had been fed the myth of Learning Styles at school, were held back by this at University. It was regularly quoted by students as a reason for their poor grades, used as an excuse for failure.

Conclusion

For me, this is a touchstone issue. The fact that it has persisted for so long is a damning indictment on our professions, practices and professional bodies. Learning styles do not exist - let me repeat – learning styles do not exist. To believe in learning styles is to believe that the sun goes round the earth or that the earth is flat. It’s an intuition gone bad – a fail. Worse still, is to apply this theory in practice. If you categorise children as VAK or adults to Honey and Mumford or any of the other dozens of learning styles theories, and yes there are dozens, you’re doing learners a disservice. You may even be ruining their education.

Evidence

We have 35 years of evidence against learning styles. This includes individual studies, systematic reviews and books. People like Pedro de Bruyckere, Wil Thalheimer and I have been talking about this for decades. Chapter 1 of Pedro de Bruyckere’s book ‘Urban Myths’ is an excellent summary of the research. A critique of Fleming’s VAK can be found here and a critique of Honey and Mumford’s theory can be found here.

To get specific for a moment. Kratzig and Arbuthnott’s took learning styles as identified using self-report and a questionnaire. Less than 50% of the participants identified the same learning style using both assessments, raising serious questions about their validity. 40% of participants self-identified as visual learners, and 60% were identified as visual learners through the questionnaire, but only 23% performed best on the visual test. The percentages were 16% and 8% for kinesthetic, yet 52% performed best with the tactile test. This research show no significant correlation between learning style and objective memory performance.

Systematic reviews

Coffield, F., Moseley, D., Hall, E., Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review. London: Learning and Skills Research Centre.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9, 105-119.

Beere, Jackie; Swindells, Maggie; Wise, Derek; Desforges, Charles; Goswami, Usha; Wood, David; Horne, Matthew; Lownsbrough, Hannah; Hargreaves, David (2005). About learning: report of the Learning Working Group

Research papers

Clark R. E. (1982) Antagonism between achievement and enjoyment in ATI studies. Educational Psychologist, 17(2), 92-101.

Cuevas, Joshua (November 2015). "Is learning styles-based instruction effective?: a comprehensive analysis of recent research on learning styles". Theory and Research in Education. 13 (3): 308–333

Husmann, P (2018) Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical Sciences Education
Kratzig and Arbuthnott (2016) Perceptual Learning Style and Learning Proficiency: A Test of the Hypothesis. Journal of Educational Psychology. 2006, Vol. 98, No. 1, 238–246

Rayner, Stephen G. (July 2013). "Problematising style differences theory and professional learning in educational psychology". The Australian Educational and Developmental Psychologist. 30 (Special Issue 1): 13–35. doi:10.1017/edp.2013.2.

Ritter, Leonora (October 2007). "Unfulfilled promises: how inventories, instruments and institutions subvert discourses of diversity and promote commonality". Teaching in Higher Education. 12 (5-6): 569–579.

Scott, Catherine (April 2010). "The enduring appeal of 'learning styles'" (PDF). Australian Journal of Education. 54 (1): 5–17

Stahl, S. A. (2002). Different strokes for different folks? In L. Abbeduto (Ed.), Taking sides: Clashing on controversial issues in educational psychology (pp. 98-107). Guilford, CT, USA: McGraw-Hill.

Riener, Cedar; Willingham, Daniel T. (August 2010). "The myth of learning styles" (PDF). Change: The Magazine of Higher Learning. 42 (5): 32–35.