Ending the Milling MindsetPosted: November 17, 2014 Filed under: Education, Opinion | Tags: advocacy, authenticity, community, curriculum, design, education, educational problem, educational research, ethics, failure rate, grand challenge, higher education, in the student's head, learning, measurement, reflection, resources, student perspective, students, teaching, teaching approaches, thinking, tools, universal principles of design 7 Comments
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.
You and I are like-minded on this. Your hand-drawn infographic is on my fridge at home, and a copy of it is in my classroom. Your comments here are not just for educators and policy-makers. Students–right down to the wee ones–need to know this. Ultimately, students should be able to make decisions, to build themselves when the education system does not. Perhaps I should have a Socrative series on this idea in my classroom…
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Sounds great to me! Everyone should know that just because they don’t know something _now_, it doesn’t mean that they won’t know it forever.
I see something similar in the HPC space. We have analytics to tell us when jobs on the system are suspicious. The problem is that investigating each sub par job is labor intensive and requires cooperation from the end user. We really struggle to help everyone we identify as having a problem with the system. Do you have similar worries for learning analytics?
Very much so. We risk moving to a system where we are ignorant of the problems but just scraping by on resources (most of the time) to one where we are aware of all the problems but don’t have the resources to help! One thing we have done to work with this is to introduce new streams of degrees that encourage the students who are more advanced to provide some leadership and guidance while still being part of the community. Another thing that we will probably do more of is using senior students to teach back into more junior streams, as this will help to more rapidly bridge some of the problem areas.
Our one advantage is that our “system jobs” are sentient and junior professionals. 🙂
Sounds awesome. One thing we are poised to do it to expose all of our data to the users who submitted the jobs as well as any automated conclusions we had the computer draw about them. Thus if we flag a job as problematic, we can show that information directly to the user rather than having to have a staff member get in the loop. If the user can figure it out on their own, then we don’t have to spend time on it.
Do you use the advanced students in the same class to help the less advanced ones? (And by class I mean a single college-level course like “Data structures” or “Programming in C” and not an entire course of study like “Computer Science” or an entire year’s cohort like the “Class of 2014” (English is hard).) In the US, I’d be afraid that showing one student’s learning analytics data to another would be a FERPA violation. How does that work in Australia? Peer-based evaluation, community building, etc seems like a great way to solve some of the problems you mention, but I wonder how hamstrung you might be.
One problem with your analogy here is that 3D printing currently has a much higher failure rate than traditional manufacturing processes, is slower per item, and costs a lot more as well. It is good for creating one-of-a-kind products or for prototyping, but it does not scale up to producing large quantities.
If you want to make an analogy, 3D printing is the equivalent of hiring a horde of incompetent tutors to teach a rich kid one-on-one. You can get a very customized education that way, but not necessarily a high quality one, and certainly not a generally affordable one.
I think perhaps you’re being a little too literal in your interpretation of the current state as the definitive state – we are in the realm of metaphor after all. The key phrase is “especially as we develop 3D printing techniques that don’t require us to deposit in sequential layers” which indicates an additive mode beyond these very early stages.
I don’t actually care about scale if my wastage is measured in people. I’m not prepared to accept that as a reasonable outcome.