I’m (still) in the middle of writing a large summary of my thoughts on education and how can we develop a better way to provide education to as many students as possible. Unsurprisingly, this is a large undertaking and I’m expecting that the final document will be interesting and fairly controversial. I suspect that one of the major problems will stem from things that I believe that we have to assume are true. Now this is always challenging, especially where evidence is lacking, but the reason that I present for some of these things to be held as true is that, if we hold them as false, then we make them false as a self-fulfilling prophecy. This may not be purely because of our theoretical framework but it may be because of what we do in implementation when we implicitly declare that something no longer needs to be worried about.
I am looking to build a better Machine for Education but such a thing is always built on the assumption that better is something that you can achieve.
The reason for making these assumptions of truth is very simple. When I speak of a “Machine for Education”, I am not moving towards some cyberpunk dystopian future, I am recognising that we are already all embedded inside a framework that turns human energy into educational activity, it’s just that the current machine places stress upon its human components, rather than taking the strain in its mechanical/procedural/technological elements. An aeroplane is a machine for flying and it works because it does not require constant human physical effort simply to keep it in the air. We have replaced the flapping wings of early designs with engines, hydraulics, computers and metal. The reason an aeroplane is a good machine is because the stress is taken on the machine itself, which can take it, with sensible constructions of human elements around it that make it a manageable occupation. (When we place airline workers under undue stress, we see the effect on the machine through reduced efficiency in maintenance and decision making, so this isn’t a perfect system.) Similarly, the development of the driverless car is a recognition of two key facts: firstly, that most cars spend most of their time not being driven and, secondly, that the activity of driving for many people is a chore that is neither enjoyable nor efficiently productive. The car is a good machine where most of the wear happens in the machine but we can make it better as a transport device by further removing the human being as a weak point, as a stress accumulator and as a part of the machine that gets worn down but is not easy to repair or rebuild. We also make the machine more efficient by potentially reducing the number required, given the known usage patterns. (Ultimately, the driverless car is the ultimate micro-light urban transit system.)
So what are these assumptions of truth?
- That our educational system can always be improved and, hence, is ready for improvement now.
It has always surprised me when some people look at dull and lifeless chalk-and-talk, based on notes from 20 years ago, and see no need for improvement, instead suggesting punitive measures to force students to sit and pretend to listen. We have more evidence from research as to what works than we have ever had before and, in conjunction with centuries of careful thought, have a great opportunity to make change.
- That everyone on the planet can benefit from an improved educational system. Yes, this means that you have to assume that, one day, we could reach everyone on the planet. We cannot assume that a certain group can be ignored and then move on. This, of course, doesn’t mean that it all has to happen tomorrow but it does mean that any planning for extending our systems must have the potential to reach everyone in the country of origin and, by extension, when we have every country, we have the world.
- That an educational system can develop students in terms of depth of knowledge and skills but also in terms of their scholarship, breadth of knowledge, and range of skills.
We currently focus heavily on training for quite narrowly specified professions in the general case and we do this to the detriment of developing the student as a scholar, as a designer, as a thinker, as a philosopher, as an artist and as a citizen. This will vary from person to person but a rich educational grounding is the foundation for better things in later life, more flexibility in work and the potential for more creativity and autonomy in leisure. Ultimately, we want our graduates to be as free to create as they are to consume, rather than consigning them to work in tight constraint.
- That we can construct environments where all students can legitimately demonstrate that they have achieved the goals of the course. This is a very challenging one so I’ve worded it carefully. I have a problem with curve grading, as everyone probably knows, and it really bothers me that someone can fail because someone else passed. I also think that most of our constraints are highly artificial and they are in place because this is what we did before. If we start from the assumption that we can construct a system where everyone can legitimately pass then we change the nature of the system we build.
- That all outcomes in an educational system can be the combination of personal actions and systemic actions, thus all outcomes must be perceived and solutions developed through both lenses.
So students are handing in their work late? This assumption requires us to look across all of their activity to work out why this is happening. This behaviour may have been set in place earlier on in their educational career so this is a combination of the student activity triggers of value, motivation and instrumentality and a feedback system that is part of an earlier component of the educational system. This does not absolve the student of questionable practices or ‘anti-educational’ behaviour but it requires us to not immediately assume that they are a ‘bad student’ as an easy out.
Ultimately, these are just some of the things I’m looking out and I’m sure that there will be discussion in the comments but I have set these to stop the shortcut thinking that does not lead to a solution because it pushes the problem to a space where it does not have to be solved. If we start from the assumption of no bad students then we have to collect actual evidence to the contrary that survives analysis and peer review to locate where the help needs to be given. And this is very much my focus – support and help to bring people back to a positive educational experience. It’s too easy to assume things are false when it makes the job easier – as well absent a very human response for an over-worked sector. I think it’s time to plant some flags of assumed truths to change the way we talk and think about these things.
This is the second in a set of posts that are critical of current approaches to education. In this post, I’m going to extend the idea of rejecting an industrial revolutionary model of student production and match our new model for manufacturing, additive processes, to a new way to produce students. (I note that this is already happening in a number of places, so I’m not claiming some sort of amazing vision here, but I wanted to share the idea more widely.)
Traditional statistics is often taught with an example where you try to estimate how well a manufacturing machine is performing by measuring its outputs. You determine the mean and variation of the output and then use some solid calculations to then determine if the machine is going to produce a sufficient number of accurately produced widgets to keep your employers at WidgetCo happy. This is an important measure for things such as getting the weight right across a number of bags of rice or correctly producing bottles that hold the correct volume of wine. (Consumers get cranky if some bags are relatively empty or they have lost a glass of wine due to fill variations.)
If we are measuring this ‘fill’ variation, then we are going to expect deviation from the mean in two directions: too empty and too full. Very few customers are going to complain about too much but the size of the variation can rarely be constrained in just one direction, so we need to limit how widely that fill needle swings. Obviously, it is better to be slightly too full (on average) than too empty (on average) although if we are too generous then the producer loses money. Oh, money, how you make us think in such scrubby, little ways.
When it comes to producing items, rather than filling, we often use a machine milling approach, where a block of something is etched away through mechanical or chemical processes until we are left with what we want. Here, our tolerance for variation will be set based on the accuracy of our mill to reproduce the template.
In both the fill and the mill cases, imagine a production line that travels on a single pass through loading, activity (fill/mill) and then measurement to determine how well this unit conforms to the desired level. What happens to those items that don’t meet requirements? Well, if we catch them early enough then, if it’s cost effective, we can empty the filled items back into a central store and pass them through again – but this is wasteful in terms of cost and energy, not to mention that contents may not be able to be removed and then put back in again. In the milling case, the most likely deviance is that we’ve got the milling process wrong and taken away things in the wrong place or to the wrong extent. Realistically, while some cases of recycling the rejects can occur, a lot of rejected product is thrown away.
If we run our students as if they are on a production line along these lines then, totally unsurprisingly, we start to set up a nice little reject pile of our own. The students have a single pass through a set of assignments, often without the ability to go and retake a particular learning activity. If they fail sufficient of these tests, then they don’t meet our requirements and they are rejected from that course. Now some students will over perform against our expectations and, one small positive, they will then be recognised as students of distinction and not rejected. However, if we consider our student failure rate to reflect our production wastage, then failure rates of 20% or higher start to look a little… inefficient. These failure rates are only economically manageable (let us switch off our ethical brains for a moment) if we have enough students or they are considered sufficiently cheap that we can produce at 80% and still make money. (While some production lines would be crippled by a 10% failure rate, for something like electric drive trains for cars, there are some small and cheap items where there is a high failure rate but the costing model allows the business to stay economical.) Let us be honest – every University in the world is now concerned with their retention and progression rates, which is the official way of saying that we want students to stay in our degrees and pass our courses. Maybe the single pass industrial line model is not the best one.
Enter the additive model, via the world of 3D printing. 3D printing works by laying down the material from scratch and producing something where there is no wastage of material. Each item is produced as a single item, from the ground up. In this case, problems can still occur. The initial track of plastic/metal/material may not adhere to the plate and this means that the item doesn’t have a solid base. However, we can observe this and stop printing as soon as we realise this is occurring. Then we try again, perhaps using a slightly different approach to get the base to stick. In student terms, this is poor transition from the school environment, because nothing is sticking to the established base! Perhaps the most important idea, especially as we develop 3D printing techniques that don’t require us to deposit in sequential layers but instead allows us to create points in space, is that we can identify those areas where a student is incomplete and then build up that area.
In an additive model, we identify a deficiency in order to correct rather than to reject. The growing area of learning analytics gives us the ability to more closely monitor where a student has a deficiency of knowledge or practice. However, such identification is useless unless we then act to address it. Here, a small failure has become something that we use to make things better, rather than a small indicator of the inescapable fate of failure later on. We can still identify those students who are excelling but, now, instead of just patting them on the back, we can build them up in additional interesting ways, should they wish to engage. We can stop them getting bored by altering the challenge as, if we can target knowledge deficiency and address that, then we must be able to identify extension areas as well – using the same analytics and response techniques.
Additive manufacturing is going to change the way the world works because we no longer need to carve out what we want, we can build what we want, on demand, and stop when it’s done, rather than lamenting a big pile of wood shavings that never amounted to a table leg. A constructive educational focus rejects high failure rates as being indicative of missed opportunities to address knowledge deficiencies and focuses on a deep knowledge of the student to help the student to build themselves up. This does not make a course simpler or drop the quality, it merely reduces unnecessary (and uneconomical) wastage. There is as much room for excellence in an additive educational framework – if anything, you should get more out of your high achievers.
We stand at a very interesting point in history. It is time to revisit what we are doing and think about what we can learn from the other changes going on in the world, especially if it is going to lead to better educational results.
This is going to be longer than usual but these thoughts have been running around in my mind for a while and, rather than break them up, I thought I’d put them all together here. My apologies for the long read but, to help you, here’s the executive summary. Firstly, we’re not going to get anywhere until all of us truly accept that University students are not some sort of different species but that they are actually junior versions of ourselves – not inferior, just less advanced. Secondly, education is heavily colonising but what we often tend to pass on to our students are mechanisms for conformity rather than the important aspects of knowledge, creativity and confidence.
Let me start with some background and look at the primary and secondary schooling system. There is what we often refer to as traditional education: classroom full of students sitting in rows, writing down the words spoken by the person at the front. Assignments test your ability to learn and repeat the words and apply this is well-defined ways to a set of problems. Then we have progressive education that, depending upon your socio-political alignment and philosophical bent, is either a way of engaging students and teachers in the process for better outcomes, more critical thought and a higher degree of creativity; or it is cats and dogs lying down together, panic in the streets, a descent into radicalism and anarchy. (There is, of course, a middle ground, where the cats and dogs sleep in different spots, in rows, but engage in discussions of Foucault.) Dewey wrote on the tension between these two apparatus (seriously, is there anything he didn’t write on?) but, as we know, he was highly opposed to the lining up on students in ranks, like some sort of prison, so let’s examine why.
Simply put, the traditional model is an excellent way to prepare students for factory work but it’s not a great way to prepare them for a job that requires independence or creativity. You sit at your desk, the teacher reads out the instructions, you copy down the instructions, you are assigned piece work to do, you follow the instructions, your work is assessed to determine if it is acceptable, if not, you may have to redo it or it is just rejected. If enough of your work is deemed acceptable, then you are now a successful widget and may take your place in the community. Of course, it will help if your job is very similar to this. However, if your deviation from the norm is towards the unacceptable side then you may not be able to graduate until you conform.
Now, you might be able to argue this on accuracy, were it not for the constraining behavioural overtones in all of this. It’s not about doing the work, it’s about doing the work, quietly, while sitting for long stretches, without complaint and then handing back work that you had no part in defining for someone else to tell you what is acceptable. A pure model of this form cripples independence because there is no scope for independent creation as it must, by definition, deviate and thus be unacceptable.
Progressive models change this. They break up the structure of the classroom, change the way that work is assigned and, in many cases, change the power relationship between student and teacher. The teacher is still authoritative in terms of information but can potentially handle some (controlled for societal reasons) deviation and creativity from their student groups.
The great sad truth of University is that we have a lot more ability to be progressive because we don’t have to worry about too many severe behavioural issues as there is enough traditional education going on below these levels (or too few management resources for children in need) that it is highly unlikely that students with severe behavioural issues will graduate from high school, let alone make it to University with the requisite grades.
But let’s return to the term ‘colonising’, because it is a loaded term. We colonise when we send a group of settlers to a new place and attempt to assert control over it, often implicit in this is the notion that the place we have colonised is now for our own use. Ultimately, those being colonised can fight or they can assimilate. The most likely outcome if the original inhabitants fight is they they are destroyed, if those colonising are technologically superior or greatly outnumber them. Far more likely, and as seen all around the world, is the requirement for the original inhabitants to be assimilated to the now dominant colonist culture. Under assimilation, original cultures shrink to accommodate new rules, requirements, and taboos from the colonists.
In the case of education, students come to a University in order to obtain the benefits of the University culture so they are seeking to be colonised by the rules and values of the University. But it’s very important to realise that any positive colonisation value (and this is a very rare case, it’s worth noting) comes with a large number of negatives. If students come from a non-Western pedagogical tradition, then many requirements at Universities in Australia, the UK and America will be at odds with the way that they have learned previously, whether it’s power distances, collectivism/individualism issues or even in the way that work is going to be assigned and assessed. If students come from a highly traditional educational background, then they will struggle if we break up the desks and expect them to be independent and creative. Their previous experiences define their educational culture and we would expect the same tensions between colonist and coloniser as we would see in any encounter in the past.
I recently purchased a game called “Dog Eat Dog“, which is a game designed to allow you to explore the difficult power dynamics of the colonist/colonised relationship in the Pacific. Liam Burke, the author, is a second-generation half-Filipino who grew up in Hawaii and he developed the game while thinking about his experiences growing up and drawing on other resources from the local Filipino community.
The game is very simple. You have a number of players. One will play the colonist forces (all of them). Each other player will play a native. How do you select the colonist? Well, it’s a simple question: Which player at the table is the richest?
As you can tell, the game starts in uncomfortable territory and, from that point on, it can be very challenging as the the native players will try to run small scenarios that the colonist will continually interrupt, redirect and adjudicate to see how well the natives are playing by the colonist’s rules. And the first rule is:
The (Native people) are inferior to the (Occupation people).
After every scenario, more rules are added and the native population can either conform (for which they are rewarded) or deviate (for which they are punished). It actually lies inside the colonist’s ability to kill all the natives in the first turn, should they wish to do so, because this happened often enough that Burke left it in the rules. At the end of the game, the colonists may be rebuffed but, in order to do that, the natives have become adept at following the rules and this is, of course, at the expense of their own culture.
This is a difficult game to explain in the short form but the PDF is only $10 and I think it’s an important read for just about anyone. It’s a short rule book, with a quick history of Pacific settlement and exemplars, produced from a successful Kickstarter.
Let’s move this into the educational sphere. It would be delightful if I couldn’t say this but, let’s be honest, our entire system is often built upon the premise that:
The students are inferior to the teachers.
Let’s play this out in a traditional model. Every time the students get together in order to do anything, we are there to assess how well they are following the rules. If they behave, they get grades (progress towards graduation). If they don’t conform, then they don’t progress and, because everyone has finite resources, eventually they will drop out, possibly doing something disastrous in the process. (In the original game, the native population can run amok if they are punished too much, which has far too many unpleasant historical precedents.) Every time that we have an encounter with the students, they have to come up with a rule to work out how they can’t make the same mistake again. This new rule is one that they’re judged against.
When I realised how close a parallel this, a very cold shiver went down my spine. But I also realised how much I’d been doing to break out of this system, by treating students as equals with mutual respect, by listening and trying to be more flexible, by interpreting a more rigid pedagogical structure through filters that met everyone’s requirements. But unless I change the system, I am merely one of the “good” overseers on a penal plantation. When the students leave my care, if I know they are being treated badly, I am still culpable.
As I started with, valuing knowledge, accuracy, being productive (in an academic sense), being curious and being creative are all things that we should be passing on from our culture but these are very hard things to pass on with a punishment/reward modality as they are all cognitive in aspect. What is far easier to do is to pass on culture such as sitting silently, being bound by late penalties, conformity to the rules and the worst excesses of the Banking model of education (after Freire) where students are empty receiving objects that we, as teachers, fill up. There is no agency in such a model, nor room for creativity. The jug does not choose the liquid that fills it.
It is easy to see examples all around us of the level of disrespect levelled at colonised peoples, from the mindless (and well-repudiated) nonsense spouted in Australian newspapers about Aboriginal people to the racist stereotyping that persists despite the overwhelming evidence of equality between races and genders. It is also as easy to see how badly students can be treated by some staff. When we write off a group of students because they are ‘bad students’ then we have made them part of a group that we don’t respect – and this empowers us to not have to treat them as well as we treat ourselves.
We have to start from the basic premise that our students are at University because they want to be like us, but like the admirable parts of us, not the conformist, factory model, industrial revolution prison aspects. They are junior lawyers, young engineers, apprentice architects when they come to us – they do not have to prove their humanity in order to be treated with respect. However, this does have to be mutual and it’s important to reflect upon the role that we have as a mentor, someone who has greater knowledge in an area and can share it with a more junior associate to bring them up to the same level one day.
If we regard students as being worthy of respect, as being potential peers, then we are more likely to treat them with a respect that engenders a reciprocal relationship. Treat your students like idiots and we all know how that goes.
The colonial mindset is poisonous because of the inherent superiority and because of the value of conformity to imposed rules above the potential to be gained from incorporating new and useful aspects of other cultures. There are many positive aspects of University culture but they can happily coexist with other educational traditions and cultures – the New Zealand higher educational system is making great steps in this direction to be able to respect both Maori tradition and the desire of young people to work in a westernised society without compromising their traditions.
We have to start from the premise that all people are equal, because to do otherwise is to make people unequal. We then must regard our students as ourselves, just younger, less experienced and only slightly less occasionally confused than we were at that age. We must carefully examine how we expose students to our important cultural aspects and decide what is and what is not important. However, if all we turn out at the end of a 3-4 year degree is someone who can perform a better model of piece work and is too heavily intimidated into conformity that they cannot do anything else – then we have failed our students and ourselves.
The game I mentioned, “Dog Eat Dog”, starts with a quote by a R. Zamora Linmark from his poem “They Like You Because You Eat Dog”. Linmark is a Filipino American poet, novelist, and playwright, who was educated in Honolulu. His challenging poem talks about the ways that a second-class citizenry are racially classified with positive and negative aspects (the exoticism is balanced against a ‘brutish’ sexuality, for example) but finishes with something that is even more challenging. Even when a native population fully assimilates, it is never enough for the coloniser, because they are still not quite them.
“They like you because you’re a copycat, want to be just like them. They like you because—give it a few more years—you’ll be just like them.
And when that time comes, will they like you more?”
R. Zamora Linmark, “They Like You Because You Eat Dog”, from “Rolling the R’s”
I had a discussion once with a remote colleague who said that he was worried the graduates of his own institution weren’t his first choice to supervise for PhDs as they weren’t good enough. I wonder whose fault he thought that was?
There’s a lot of discussion around a government’s use of metadata at the moment, where instead of looking at the details of your personal data, government surveillance is limited to looking at the data associated with your personal data. In the world of phone calls, instead of taping the actual call, they can see the number you dialled, the call time and its duration, for example. CBS have done a fairly high-level (weekend-suitable) coverage of a Stanford study that quickly revealed a lot more about participants than they would have thought possible from just phone numbers and call times.
But how much can you tell about a person or an organisation without knowing the details? I’d like to show you a brief, but interesting, example. I write fiction and I’ve recently signed up to “The Submission Grinder“, which allows you to track your own submissions and, by crowdsourcing everyone’s success and failures, to also track how certain markets are performing in terms of acceptance, rejection and overall timeliness.
Now, I have access to no-one else’s data but my own (which is all of 5 data points) but I’ll show you how assembling these anonymous data results together allows me to have a fairly good stab at determining organisational structure and, in one case, a serious organisational transformation.
Let’s start by looking at a fairly quick turnover semi-pro magazine, Black Static. It’s a short fiction market with horror theming. Here’s their crowd-sourced submission graph for response times, where rejections are red and acceptances are green. (Sorry, Damien.)
Black Static has a web submission system and, as you can see, most rejections happen in the first 2-3 weeks. There is then a period where further work goes on. (It’s very important to note that this is a sample generated by those people who are using Submission Grinder, which is a subset of all people submitting to Black Static.) What this looks like, given that it is unlikely that anyone could read a lot 4,000-7,000 manuscripts in detail at a time, is that the editor is skimming the electronic slush pile to determine if it’s worth going to other readers. After this initial 2 week culling, what we are seeing is the result of further reading so we’d probably guess that the readers’ reviews are being handled as they come in, with some indication that this is one roughly weekly – maybe as a weekend job? It’s hard to say because there’s not much data beyond 21 days so we’re guessing.
Let’s look at Black Static’s sister SF magazine, Interzone, now semi-pro but still very highly regarded.
Lots more data here! Again, there appears to be a fairly fast initial cut-off mechanism from skimming the web submission slush pile. (And I can back this up with actual data as Interzone rejected one of my stories in 24 hours.) Then there appears to be a two week period where some thinking or reading takes place and then there’s a second round of culling, which may be an editorial meeting or a fast reader assignment. Finally we see two more fortnightly culls as the readers bring back their reviews. I think there’s enough data here to indicate that Interzone’s editorial group consider materials most often every fortnight. Also the acceptances generated by positive reviews appear to be the same quantity as those from the editors – although there’s so little data here we’re really grabbing at tempting looking straws.
Now let’s look at two pro markets, starting with the Magazine of Fantasy & Science Fiction.
This doesn’t have the same initial culling process that the other two had, although it appears that there is a period of 7-14 days when a lot of work has been reviewed and then rejected – we don’t see as much work rejected again until the 35 day mark, when it looks like all reader reviews are back. Notably, there is a large gap between the initial bunch of acceptances (editor says ‘yes’) and then acceptances supported by reviewers. I’m speculating now but I wonder if what we’re seeing between that first and second group of acceptances are reviewers who write back in and say “Don’t bother” quickly, rather than assembling personalised feedback for something that could be salvaged. Either way, the message here is simple. If you survive the first four weeks in F&SF system, then you are much less likely to be rejected and, with any luck, this may translate (worse case) into personal suggestions for improvement.
F&SF has a postal submission system, which makes it far more likely that the underlying work is going to batched in some way, as responses have to go out via mail and doing this in a more organised fashion makes sense. This may explain why this is such a high level of response overall for the first 35 days, as you can’t easily click a button to send a response electronically and there’re a finite number of envelopes any one person wants to prepare on any given day. (I have no idea how right I am but this is what I’m limited to by only observing the metadata.)
Tor.com has a very interesting graph, which I’ll show below.
Tor.com pays very well and has an on-line submission system via e-mail. As a result, it is positively besieged with responses and their editorial team recently shut down new submissions for two months while they cleared backlog. What interested me in this data was the fact that the 150 day spike was roughly twice as high as the 90 and 120. Hmm – 90, 120, 150 as dominant spikes. Does that sound like a monthly editors’ meeting to anyone else? By looking at the recency graph (which shows activity relative to today) we can see that there has been an amazing flurry of activity at Tor.com in the past month. Tor.com has a five person editorial team (from their website) with reading and support from two people (plus occasional others). It’s hard for five people to reach consensus without discussion so that monthly cycle looks about right. But it will take time for 7 people to read all of that workload, which explains the relative silence until 3 months have elapsed.
What about that spike at 150? It could be the end of the initial decisions and the start of “worth another look” pile so let’s see if their web page sheds any light on it. Aha!
Have you read my story? We reply to everything we’ve finished evaluating, so if you haven’t heard from us, the answer is “probably not.” At this point the vast majority of stories greater than four months old are in our second-look pile, and we respond to almost everything within seven months.
I also wonder if we are seeing previous data where it was taking longer to get decisions made – whether we are seeing two different time management strategies of Tor.com at the same time, being the 90+120 version as well as the 150 version. Looking at the website again.
Response times have improved quite a bit with the expansion of our first reader team (emphasis mine), and we now respond to the vast majority of stories within three months. But all of the stories they like must then be read by the senior editorial staff, who are all full-time editors with a lot on our plates.
So, yes, the size of Tor.com’s slush pile and the number of editors that must agree basically mean that people are putting time aside to make these decisions, now aiming at 90 days, with a bit of spillover. It looks like we are seeing two regimes at once.
All of this information is completely anonymous in terms of the stories, the authors and any actual submission or acceptance patterns that could relate data together. But, by looking at this metadata on the actual submissions, we can now start to get an understanding of the internal operations of an organisation, which in some cases we can then verify with publicly held information.
Now think about all the people you’ve phoned, the length of time that you called them and what could be inferred about your personal organisation from those facts alone. Have a good night’s sleep!
Sometimes the only exposure my students will have to the study of ethics is (sorry, ethical philosophers) me and my “freeze-dried, snap-frozen, instant peas” version of the study of ethical issues. (In the land of the unethical, the mono-principled man is king?)
Here are a quick five things that loosely summarise my loose summaries.
- Ethics, Morals and Truth are Different Things. Morals are a person’s standards of belief concerning acceptable behaviour (we often throw around words like good and bad here). Ethics are the set of moral principles that guide a person’s behaviour or that of a group. Truth is the set of things that are real and factual, or those things that are accepted as true. Does that clear it up? Things that are true can be part of an unethical set of beliefs put together by immoral people. Immoral people can actually behave ethically consistently while still appear unethical and immoral from your group. Ethics often require you to start juggling things to work out a best or most consistent course of action, which is a luxury that we generally don’t have with the truth.
- Being Good is Not the Same Thing as Trying to Do the Right Thing. Trying to do the right thing is the field where your actions are guided by your ethical principles. Trying to be the best person you can be (Hello, Captain America) is virtue ethics. Both being good and doing the right thing can be guided by rules or by looking at outcomes but one is concerned who you are trying to be and the other is concerned with what you are trying to do. Yes, this means you can be a total ratbag as long as you behave the right way in the face of every ethical dilemma. (My apologies to any rats with bags.)
- You Can Follow Rules Or You Can Aim For The Best Outcome (Or Do Both, Actually). There are two basic breakdowns I’ve mentioned before: one follows rules and by doing that then the outcome doesn’t matter, the other tries to get the best outcome and this excuses any rules you break on the way to your good outcome. Or you can mix them together and hybridise it, even throwing in virtue ethics, which is what we tend to do because very few of us are moral philosophers and most of us are human beings. 🙂
- Consistency is Important. If you make decisions one way when it’s you and another way when it’s someone else then there’s a very good chance that you’re not applying a consistent ethical framework, you’re rationalising. (Often referred to as special pleading because you are special and different.) If you treat one group of people one way, and another completely differently, then I think you can guess that your ethics are too heavily biassed to actually be considered consistent – or all that ethical.
- Questioning Your Existing Frameworks Can Be Very Important. The chances that you managed to get everything right as you moved into adulthood is, really, surprisingly low, especially as most ethical and moral thinking is done in response to situations in your life. However, it’s important to think about how you can change your thinking in a way that forms a sound and consistent basis to build your ethical thinking upon. This can be very, very challenging, especially when the situation you’re involved in is particular painful or terrifying.
And that’s it. A rapid, shallow run through a deeply complex and rewarding area that everyone should delve into at some stage in their lives.
I was referred some time ago to a great site called “Unstuck”, which has some accompanying iPad software, that helps you to think about how to move past those stuck moments in your life and career to get things going. They recently posted an interesting item on “How to work like a human” and I thought that a lot of what they talked about had direct relevance to how we treat students and how we work with them to achieve things. The article is by Julie Felner and I strongly suggest that you read it, but here are my thoughts on her headings, as they apply to education and students.
Ultimately, if we all work together like human beings, we’re going to get on better than if we treat our students as answer machines and they treat us as certification machines. Here’s what optimising for one thing, mechanistically, can get you:
But if we’re going to be human, we need to be connected. Here are some signs that you’re not really connected to your students.
- Anything that’s not work you treat with a one word response. A student comes to see you and you don’t have time to talk about anything but assignment X or project Y. I realise time is scarce but, if we’re trying to build people, we have to talk to people, like people.
- You’re impatient when they take time to learn or adjust. Oh yeah, we’ve all done this. How can they not pick it up immediately? What’s wrong with them? Don’t they know I’m busy?
- Sleep and food are for the weak – and don’t get sick. There are no human-centred reasons for not getting something done. I’m scheduling all of these activities back-to-back for two months. If you want it, you’ll work for it.
- We never ask how the students are doing. By which I mean, asking genuinely and eking out a genuine response, if some prodding is required. Not intrusively but out of genuine interest. How are they doing with this course?
- We shut them down. Here’s the criticism. No, I don’t care about the response. No, that’s it. We’re done. End of discussion. There are times when we do have to drawn an end to a discussion but there’s a big difference between closing off something that’s going nowhere and delivering everything as if no discussion is possible.
Here is my take on Julie’s suggestions for how we can be more human at work, which works for the Higher Ed community just as well.
- Treat every relationship as one that matters. The squeaky wheels and the high achievers get a lot of our time but all of our students are actually entitled to have the same level of relationship with us. Is it easy to get that balance? No. Is it a worthwhile goal? Yes.
- Generously and regularly express your gratitude. When students do something well, we should let them know- as soon as possible. I regularly thank my students for good attendance, handing things in on time, making good contributions and doing the prep work. Yes, they should be doing it but let’s not get into how many things that should be done aren’t done. I believe in this strongly and it’s one of the easiest things to start doing straight away.
- Don’t be too rigid about your interactions. We all have time issues but maybe you can see students and talk to them when you pass them in the corridor, if both of you have time. If someone’s been trying to see you, can you grab them from a work area or make a few minutes before or after a lecture? Can you talk with them over lunch if you’re both really pressed for time? It’s one thing to have consulting hours but it’s another to make yourself totally unavailable outside of that time. When students are seeking help, it’s when they need help the most. Always convenient? No. Always impossible to manage? No. Probably useful? Yes.
- Don’t pretend to be perfect. Firstly, students generally know when you’re lying to them and especially when you’re fudging your answers. Don’t know the answer? Let them know, look it up and respond when you do. Don’t know much about the course itself? Well, finding out before you start teaching is a really good idea because otherwise you’re going to be saying “I don’t know a lot” and there’s a big, big gap between showing your humanity and obviously not caring about your teaching. Fix problems when they arise and don’t try to make it appear that it wasn’t a problem. Be as honest as you can about that in your particular circumstances (some teaching environments have more disciplinary implications than others and I do get that).
- Make fewer assumptions about your students and ask more questions. The demographics of our student body have shifted. More of my students are in part-time or full-time work. More are older. More are married. Not all of them have gone through a particular elective path. Not every previous course contains the same materials it did 10 years ago. Every time a colleague starts a sentence with “I would have thought” or “Surely”, they are (almost always) projecting their assumptions on to the student body, rather than asking “Have you”, “Did you” or “Do you know”?
Julie made the final point that sometimes we can’t get things done to the deadline. In her words:
You sometimes have to sacrifice a deadline in order to preserve something far more important — a relationship, a person’s well-being, the quality of the work
I completely agree because deadlines are a tool but, particularly in academia, the deadline is actually rarely as important as people. If our goal is to provide a good learning environment, working our students to zombie status because “that’s what happened to us” is bordering on a cycle of abuse, rather than a commitment to quality of education.
We all want to be human with our students because that’s how we’re most likely to get them to engage with us as a human too! I liked this article and I hope you enjoyed my take on it. Thank you, Julie Felner!
A recent study has shown that crowdsourcing activities are prone to bringing out the competitors’ worst competitive instincts.
“[T]he openness makes crowdsourcing solutions vulnerable to malicious behaviour of other interested parties,” said one of the study’s authors, Victor Naroditskiy from the University of Southampton, in a release on the study. “Malicious behaviour can take many forms, ranging from sabotaging problem progress to submitting misinformation. This comes to the front in crowdsourcing contests where a single winner takes the prize.” (emphasis mine)
You can read more about it here but it’s not a pretty story. Looks like a pretty good reason to be very careful about how we construct competitive challenges in the classroom!
I teach a variety of courses, including one called Puzzle-Based Learning, where we try to teach think and problem-solving techniques through the use of simple puzzles that don’t depend on too much external information. These domain-free problems have most of the characteristics of more complicated problems but you don’t have to be an expert in the specific area of knowledge to attempt them. The other thing that we’ve noticed over time is that a good puzzle is fun to solve, fun to teach and gets passed on to other people – a form of infectious knowledge.
Some of the most challenging areas to try and teach into are those that deal with probability and statistics, as I’ve touched on before in this post. As always, when an area is harder to understand, it actually requires us to teach better but I do draw the line at trying to coerce students into believing me through the power of my mind alone. But there are some very handy ways to show students that their assumptions about the nature of probability (and randomness) so that they are receptive to the idea that their models could need improvement (allowing us to work in that uncertainty) and can also start to understand probability correctly.
We are ferociously good pattern matchers and this means that we have some quite interesting biases in the way that we think about the world, especially when we try to think about random numbers, or random selections of things.
So, please humour me for a moment. I have flipped a coin five times and recorded the outcome here. But I have also made up three other sequences. Look at the four sequences for a moment and pick which one is most likely to be the one I generated at random – don’t think too much, use your gut:
- Tails Tails Tails Heads Tails
- Tails Heads Tails Heads Heads
- Heads Heads Tails Heads Tails
- Heads Heads Heads Heads Heads
Have you done it?
I’m just going to put a bit more working in here to make sure that you’ve written down your number…
I’ve run this with students and I’ve asked them to produce a sequence by flipping coins then produce a false sequence by making subtle changes to the generated one (turns heads into tails but change a couple along the way). They then write the two together on a board and people have to vote on which one is which. As it turns out, the chances of someone picking the right sequence is about 50/50, but I engineered that by starting from a generated sequence.
This is a fascinating article that looks at the overall behaviour of people. If you ask people to write down a five coin sequence that is random, 78% of them will start with heads. So, chances are, you’ve picked 3 or 4 as you’re starting sequence. When it comes to random sequences, most of us equate random with well-shuffled, and, on the large scale, 30 times as many people would prefer option 3 to option 4. (This is where someone leaps into the comments to say “A-ha” but, it’s ok, we’re talking about overall behavioural trends. Your individual experience and approach may not be the dominant behaviour.)
From a teaching point of view, this is a great way to break up the concepts of random sequences and some inherent notion that such sequences must be disordered. There are 32 different ways of flipping 5 coins in a strict sequence like this and all of them are equally likely. It’s only when we start talking about the likelihood of getting all heads versus not getting all heads that the aggregated event of “at least one head” starts to be more likely.
How can we use this? One way is getting students to write down their sequences and then asking them to stand up, then sit down when your ‘call’ (from a script) goes the other way. If almost everyone is still standing at heads then you’ve illustrated that you know something about how their “randomisers” work. A lot of people (if your class is big enough) should still be standing when the final coin is revealed and this we can address. Why do so many people think about it this way? Are we confusing random with chaotic?
The Law of Small Numbers (Tversky and Kahneman), also mentioned in the post, which is basically that people generalise too much from small samples and they expect small samples to act like big ones. In your head, if the grand pattern over time could be resorted into “heads, tails, heads, tails,…” then small sequences must match that or they just don’t look right. This is an example of the logical fallacy called a “hasty generalisation” but with a mathematical flavour. We are strongly biassed towards the the validity of our experiences, so when we generate a random sequence (or pick a lucky door or win the first time at poker machines) then we generalise from this small sample and can become quite resistant to other discussions of possible outcomes.
If you have really big classes (367 or more) then you can start a discussion on random numbers by asking people what the chances are that any two people in the room share a birthday. Given that there are only 366 possible birthdays, the Pigeonhole principle states that two people must share a birthday as, in a class of 367, there are only 366 birthdays to go around so one must be repeated! (Note for future readers: don’t try this in a class of clones.) There are lots of other, interesting thinking examples in the link to Wikipedia that helps you to frame randomness in a way that your students might be able to understand it better.
I’ve used a lot of techniques before, including the infamous card shouting, but the new approach from the podcast is a nice and novel angle to add some interest to a class where randomness can show up.
Time for some pretty shameless self-promotion. Feel free to stop reading if that will bother you.
My colleagues, Ed Meyer from BWU, Raja Sooriamurthi from CMU and Zbyszek Michalewicz (emeritus from my own institution) and I have just released a new book, called “A Guide to Teaching Puzzle-based learning.” What a labour of love this has been and, better yet, we are still still talking to each other. In fact, we’re planning some follow-up events next year to do some workshops around the book so it’ll be nice to work with the team again.
Here’s a slightly sleep-deprived and jet-lagged picture of me holding the book as part of my “wow, it got published” euphoria!
The book is a resource for the teacher, although it’s written for teachers from primary to tertiary and it should be quite approachable for the home school environment as well. We spent a lot of time making it approachable, sharing tips for students and teachers alike, and trying to get all of our knowledge about how to teach well with puzzles down into the one volume. I think we pretty much succeeded. I’ve field-tested the material here at Universities, schools and businesses, with very good results across the board. We build on a good basis and we love sound practical advice. This is, very much, a book for the teaching coalface.
It’s great to finally have it all done and printed. The Springer team were really helpful and we’ve had a lot of patience from our commissioning editors as we discussed, argued and discussed again some of the best ways to put things into the written form. I can’t quite believe that we managed to get 350 pages down and done, even with all of the time that we had.
If you or your institution has a connection to SpringerLink then you can read it online as part of your subscription. Otherwise, if you’re keen, feel free to check out the preview on the home page and then you may find that there are a variety of prices available on the Web. I know how tight budgets are at the moment so, if you do feel like buying, please buy it at the best price for you. I’ve already had friends and colleagues ask what benefits me the most and the simple answer is “if people read it and find it useful”.
To end this disgraceful sales pitch, we’re actually quite happy to run workshops and the like, although we are currently split over two countries (sometimes three or even four), so some notice is always welcome.
That’s it, no more self-promotion to this extent until the next book!
One of the big focuses at our University is the Small-Group Discovery Experience, an initiative from our overall strategy document, the Beacon of Enlightenment. You can read all of the details here, but the essence is that a small group of students and an experienced research academic meet regularly to start the students down the path of research, picking up skills in an active learning environment. In our school, I’ve run it twice as part of the professional ethics program. This second time around, I think it’s worth sharing what we did, as it seems to be working well.
Why ethics? Well, this is first year and it’s not all that easy to do research into Computing if you don’t have much foundation, but professional skills are part of our degree program so we looked at an exploration of ethics to build a foundation. We cover ethics in more detail in second and third year but it’s basically a quick “and this is ethics” lecture in first year that doesn’t give our students much room to explore the detail and, like many of the more intellectual topics we deal with, ethical understanding comes from contemplation and discussion – unless we just want to try to jam a badly fitting moral compass on to everyone and be done.
Ethical issues present the best way to talk about the area as an introduction as much of the formal terminology can be quite intimidating for students who regard themselves as CS majors or Engineers first, and may not even contemplate their role as moral philosophers. But real-world situations where ethical practice is more illuminating are often quite depressing and, from experience, sessions in medical ethics, and similar, rapidly close down discussion because it can be very upsetting. We took a different approach.
The essence of any good narrative is the tension that is generated from the conflict it contains and, in stories that revolve around artificial intelligence, robots and computers, this tension often comes from what are fundamentally ethical issues: the machine kills, the computer goes mad, the AI takes over the world. We decided to ask the students to find two works of fiction, from movies, TV shows, books and games, to look into the ethical situations contained in anything involving computers, AI and robots. Then we provided them with a short suggested list of 20 books and 20 movies to start from and let them go. Further guidance asked them to look into the active ethical agents in the story – who was doing what and what were the ethical issues?
I saw the students after they had submitted their two short paragraphs on this and I was absolutely blown out of the water by their informed, passionate and, above all, thoughtful answers to the questions. Debate kept breaking out on subtle points. The potted summary of ethics that I had given them (follow the rules, aim for good outcomes or be a good person – sorry, ethicists) provided enough detail for the students to identify issues in rule-based approaches, utilitarianism and virtue ethics, but I could then introduce terms to label what they had already done, as they were thinking about them.
I had 13 sessions with a total of 250 students and it was the most enjoyable teaching experience I’ve had all year. As follow-up, I asked the students to enter all of their thoughts on their entities of choice by rating their autonomy (freedom to act), responsibility (how much we could hold them to account) and perceived humanity, using a couple of examples to motivate a ranking system of 0-5. A toddler is completely free to act (5) and completely human (5) but can’t really be held responsible for much (0-1 depending on the toddler). An aircraft autopilot has no humanity or responsibility but it is completely autonomous when actually flying the plane – although it will disengage when things get too hard. A soldier obeying orders has an autonomy around 5. Keanu Reeves in the Matrix has a humanity of 4. At best.
They’ve now filled the database up with their thoughts and next week we’re going to discuss all of their 0-5 ratings as small groups, then place them on a giant timeline of achievements in literature, technology, AI and also listing major events such as wars, to see if we can explain why authors presented the work that they did. When did we start to regard machines as potentially human and what did the world seem like them to people who were there?
This was a lot of fun and, while it’s taken a little bit of setting up, this framework works well because students have seen quite a lot, the trick is just getting to think about with our ethical lens. Highly recommended.