I’ve reached the conclusion that a lot of courses have an unrealistically high number of evaluations. We have too many and we pretend that we are going to achieve outcomes for which we have no supporting evidence. Worse, in many cases, we are painfully aware that we cause last-minute lemming-like effects that do anything other than encourage learning. But why do we have so many? Because we’re trying to fit them into the term or semester size that we have: the administrative limit.
One the big challenges for authenticity in Computer Science is the nature of the software project. While individual programs can be small and easy to write, a lot of contemporary programming projects are:
- Large and composed of many small programs.
- Complex to a scale that may exceed one person’s ability to visualise.
- Built on platforms that provide core services; the programmers do not have the luxury to write all of the code in the system.
Many final year courses in Software Engineering have a large project courses, where students are forced to work with a (usually randomly assigned) group to produce a ‘large’ piece of software. In reality, this piece of software is very well-defined and can be constructed in the time available: it has been deliberately selected to be so.
Is a two month software task in a group of six people indicative of real software?
Yes and no. It does give a student experience in group management, except that they still have the safe framework of lecturers over the top. It’s more challenging than a lot of what we do because it is a larger artefact over a longer time.
But it’s not that realistic. Industry software projects live over years, with tens to hundreds of programmers ‘contributing’ updates and fixes… reversing changes… writing documentation… correcting documentation. This isn’t to say that the role of a university is to teach industry skills but these skill sets are very handy for helping programmers to take their code and make it work, so it’s good to encourage them.
I believe finally, that education must be conceived as a continuing reconstruction of experience; that the process and the goal of education are one and the same thing.
from John Dewey, “My Pedagogic Creed”, School Journal vol. 54 (January 1897)
I love the term ‘continuing reconstruction of experience’ as it drives authenticity as one of the aesthetic characteristics of good education.
Authentic, appropriate and effective learning and evaluation activities may not fit comfortably into a term. We already accept this for activities such as medical internship, where students must undertake 47 weeks of work to attain full registration. But we are, for many degrees, trapped by the convention of a semester of so many weeks, which is then connected with other semesters to make a degree that is somewhere between three to five years long.
The semester is an artefact of the artificial decomposition of the year, previously related to season in many places but now taking on a life of its own as an administrative mechanism. Jamming things into this space is not going to lead to an authentic experience and we can now reject this on aesthetic grounds. It might fit but it’s beautiful or true.
But wait! We can’t do that! We have to fit everything into neat degree packages or our students won’t complete on time!
Let’s now look at the ‘so many years degree’. This is a fascinating read and I’ll summarise the reported results for degree programs in the US, which don’t include private colleges and universities:
- Fewer than 10% of reporting institutions graduated a majority of students on time.
- Only 19% of students at public universities graduate on-time.
- Only 36% of state flagship universities graduate on-time
- 5% of community college students complete an associate degree on-time.
The report has a simple name for this: the four-year myth. Students are taking longer to do their degrees for a number of reasons but among them are poorly designed, delivered, administered or assessed learning experiences. And jamming things into semester blocks doesn’t seem to be magically translating into on-time completions (unsurprisingly).
It appears that the way we break up software into little pieces is artificial and we’re also often trying to carry out too many little assessments. It looks like a good model is to stretch our timeline out over more than one course to produce an experience that is genuinely engaging, more authentic and more supportive of long term collaboration. That way, our capstone course could be a natural end-point to a three year process… or however long it takes to get there.
Finally, in the middle of all of this, we need to think very carefully about why we keep using the semester or the term as a container. Why are degrees still three to four years long when everything else in the world has changed so much in the last twenty years?
Earlier, I split the evaluation resources of a course into:
- E1 (the lecturer and course designer),
- E2 (human work that can be based on rubrics, including peer assessment and casual markers),
- E3 (complicated automated evaluation mechanisms)
- E4 (simple automated evaluation mechanisms, often for acceptance testing)
E1 and E2 everyone tends to understand, because the culture of Prof+TA is widespread, as is the concept of peer assessment. In a Computing Course, we can define E3 as complex marking scripts that perform amazing actions in response to input (or even carry out formal analysis if we’re being really keen), with E4 as simple file checks, program compilation and dumb scripts that jam in a set of data and see what comes out.
But let’s get back to my first year, first exposure, programming class. What I want is hands-on, concrete, active participation and constructive activity and lots of it. To support that, I want the best and most immediate feedback I can provide. Now I can try to fill a room with tutors, or do a lot of peer work, but there will come times when I want to provide some sort of automated feedback.
Given how inexperienced these students are, it could be a quite a lot to expect them to get their code together and then submit it to a separate evaluation system, then interpret the results. (Remember I noted earlier on how code tracing correlates with code ability.)
Thus, the best way to get that automated feedback is probably working with the student in place. And that brings us to the Integrated Development Environment (IDE). An IDE is an application that provides facilities to computer programmers and helps them to develop software. They can be very complicated and rich (Eclipse), simple (Processing) or aimed at pedagogical support (Scratch, BlueJ, Greenfoot et al) but they are usually made up of a place in which you can assemble code (typing or dragging) and a set of buttons or tools to make things happen. These are usually quite abstract for early programmers, built on notional machines rather than requiring a detailed knowledge of hardware.
Even simple IDEs will tell you things that provide immediate feedback. We know how these environments can have positive reception, with some demonstrated benefits, although I recommend reading Sorva et al’s “A Review of Generic Program Visualization Systems for Introductory Programming Education” to see the open research questions. In particular, people employing IDEs in teaching often worry about the time to teach the environment (as well as the language), software visualisations, concern about time on task, lack of integration and the often short lifespan of many of the simpler IDEs that are focused on pedagogical outcomes. Even for well-established systems such as BlueJ, there’s always concern over whether the investment of time in learning it is going to pay off.
In academia, time is our currency.
But let me make an aesthetic argument for IDEs, based on the feedback that I’ve already put into my beautiful model. We want to maximise feedback in a useful way for early programmers. Early programmers are still learning the language, still learning how to spell words, how to punctuate, and are building up to a grammatical understanding. An IDE can provide immediate feedback as to what the computer ‘thinks’ is going on with the program and this can help the junior programmer make immediate changes. (Some IDEs have graphical representations for object systems but we won’t discuss these any further here as the time to introduce objects is a subject of debate.)
Now there’s a lot of discussion over the readability of computer error messages but let me show you an example. What’s gone wrong in this program?
See where that little red line is, just on the end of the first line? Down the bottom there’s a message that says “missing a semicolon”. In the Processing language, almost all lines end with a “;” so that section of code should read:
Did you get that? That missing semicolon problem has been an issue for years because many systems report the semicolon missing on the next line, due to the way that compilers work. Here, Processing is clearly saying: Oi! Put a semi-colon on the red squiggle.
I’m an old programmer, who currently programs in Java, C++ and Processing, so typing “;” at the end of a line is second nature to me. But it’s an easy mistake for a new programmer to make because, between all of the ( and the ) and the , and the numbers and the size and the rect… what do I do with the “;”?
The Processing IDE is functioning in at least an E4 mode: simple acceptance testing that won’t let anything happen until you fix that particular problem. It’s even giving you feedback as to what’s wrong. Now this isn’t to say that it’s great but it’s certainly better than a student sitting there with her hand up for 20 minutes waiting for a tutor to have the time to come over and say “Oh, you’re missing a semicolon.”
We don’t want shotgun coding, where random fixes and bashed-in attempts are made desperately to solve a problem. We want students to get used to getting feedback on how they’re going and using this to improve what they do.
Because of Processing’s highly visual mode, I think it’s closer to E3 (complex scripting) in many ways because it can tell you if it doesn’t understand what you’re trying to do at all. Beyond just not doing something, it can clearly tell you what’s wrong.
But what if it works and then the student puts something up on the screen, a graphic of some sort and it’s not quite right? Then the student has started to become their own E2, evaluating what has happened in response to the code and using human insight to address the shortfall and make changes. Not as an expert but, with support and encouragement, a developing expertise.
Feedback is good. Immediacy is good. Student involvement is good. Code tracing is linked to coding ability. A well-designed IDE can be simple and engage the student to an extent that is potentially as high as E2, although it won’t be as rich, without using any other human evaluation resources. Even if there is no other benefit, the aesthetic argument is giving us a very strong nudge to adopt an appropriate IDE.
Maybe it’s time to hang up the command line and live in a world where IDEs can help us to get things done faster, support our students better and make our formal human evaluation resources go further.
What do you think?
One of the problems with any model that builds in more feedback is that we incur both the time required to produce the feedback and we also have an implicit requirement to allow students enough time to assimilate and make use of it. This second requirement is still there even if we don’t have subsequent attempts at work, as we want to build upon existing knowledge. The requirement for good feedback makes no sense without a requirement that it be useful.
But let me reiterate that pretty much all evaluation and feedback can be very valuable, no matter how small or quick, if we know what we are trying to achieve. (I’ll get to more complicated systems in later posts.)
Novice programmers often struggle with programming and this early stage of development is often going to influence if they start off thinking that they can program or not. Given that automated evaluation only really provides useful feedback once the student has got something working, novice programming classes are an ideal place to put human markers. If we can make students think “Yes, I can do this” early on, this is the emotion that they will remember. We need to get to big problems quickly, turn them into manageable issues that can be overcome, and then let motivation and curiosity take the rest.
There’s an excellent summary paper on computer programming visualisation systems aimed at novice programmers, which discusses some of the key problems novices face on their path to mastery:
- Novices can see some concepts as code rather than the components of a dynamic process. For example, they might see objects as simply a way of containing things rather than modelling objects and their behaviours. These static perceptions prevent the students from understanding that they are designing behaviours, not just writing magic formulas.
- There can be significant difficulties in understanding the computer, seeing the notional machine that is the abstraction, forming a basis upon which knowledge of one language or platform could be used elsewhere.
- Misunderstanding fundamental concepts is common and such misconceptions can easily cause weak understanding, leaving the students in the liminal state, unable to assimilate a threshold concept and move on.
- Students struggle to trace programs and work out what state the program should be in. In my own community, Raymond Lister, Donna Teague, Simon, and others have clearly shown that many students struggle with the tracing of even simple programs.
If we have put human markers (E1 or E2) into a programming class and identified that these are the problems we’re looking for, we can provide immediate targeted evaluation that is also immediate constructive feedback. On the day, in response to actual issues, authentic demonstration of a solution process that students can model. This is the tightest feedback and reward loop we can offer. How does this work?
- Program doesn’t work because of one of the key problem areas.
- Human evaluator intervenes with student and addresses the issue, encouraging discovery inside the problem area.
- Student tries to identify problem and explains it to evaluator in context, modelling evaluator and based on existing knowledge.
- Evaluator provides more guidance and feedback.
- Student continues to work on problem.
- We hope that the student will come across the solution (or think towards it) but we may have to restart this loop.
Note that we’re not necessarily giving the solution here but we can consider leading towards this if the student is getting visibly frustrated. I’d suggest never telling a student what to type as it doesn’t address any of the problems, it just makes the student dependent upon being told the answer. Not desirable. (There’s an argument here for rich development environments that I’ll expand on later.)
Evaluation like this is formative, immediate and rich. We can even streamline it with guidelines to help the evaluators although much of this will amount to supporting students as they learn to read their own code and understand the key concepts. We should develop students simple to complex, concrete to abstract, so some problems with abstraction are to be expected, especially if we are playing near any threshold concepts.
But this is where learning designers have to be ready to say “this may cause trouble” and properly brief the evaluators who will be on the ground. If we want our evaluators to work efficiently and effectively, we have to brief them on what to expect, what to do, and how to follow up.
If you’ve missed it so far, one of our big responsibilities is training our evaluation team. It’s only by doing this that we can make sure that our evaluators aren’t getting bogged down in side issues or spending too much time with one student and doing the work for them. This training should include active scenario-based training to allow the evaluators to practise with the oversight of the educators and designers.
We have finite resources. If we want to support a room full of novices, we have to prepare for the possibility of all of them having problems at once and the only way to support that at scale is to have an excellent design and train for it.
I’ve been talking about why late penalties are not only not useful but they don’t work, yet I keep talking about getting work in on time and tying it to realistic resource allocation. Does this mean I’m really using late penalties?
No, but let me explain why, starting from the underlying principle of fairness that is an aesthetic pillar of good education. One part of this is that the actions of one student should not unduly affect the learning journey of another student. That includes evaluation (and associated marks).
This is the same principle that makes me reject curve grading. It makes no sense to me that someone else’s work is judged in the context of another, when we have so little real information with which we could establish any form of equivalence of human experience and available capacity.
I don’t want to create a market economy for knowledge, where we devaluate successful demonstrations of knowledge and skill for reasons that have nothing to do with learning. Curve grading devalues knowledge. Time penalties devalue knowledge.
I do have to deal with resource constraints, in that I often have (some) deadlines that are administrative necessities, such as degree awards and things like this. I have limited human resources, both personally and professionally.
Given that I do not have unconstrained resources, the fairness principle naturally extends to say that individual students should not consume resources to the detriment of others. I know that I have a limited amount of human evaluation time, therefore I have to treat this as a constrained resource. My E1 and E2 evaluations resources must be, to a degree at least, protected to ensure the best outcome for the most students. (We can factor equity into this, and should, but this stops this from being a simple linear equivalence and makes the terms more complex than they need to be for explanation, so I’ll continue this discussion as if we’re discussing equality.)
You’ve noticed that the E3 and E4 evaluation systems are pretty much always available to students. That’s deliberate. If we can automate something, we can scale it. No student is depriving another of timely evaluation and so there’s no limitation of access to E3 and E4, unless it’s too late for it to be of use.
If we ask students to get their work in at time X, it should be on the expectation that we are ready to leap into action at second X+(prep time), or that the students should be engaged in some other worthwhile activity from X+1, because otherwise we have made up a nonsense figure. In order to be fair, we should release all of our evaluations back at the same time, to avoid accidental advantages because of the order in which things were marked. (We may wish to vary this for time banking but we’ll come back to this later.) As many things are marked in surname or student number order, the only way to ensure that we don’t accidentally keep granting an advantage is to release everything at the same time.
Remember, our whole scheme is predicated on the assumption that we have designed and planned for how long it will take to go through the work and provide feedback in time for modification before another submission. When X+(prep time) comes, we should know, roughly to the hour or day, at worst, when this will be done.
If a student hands up fifteen minutes late, they have most likely missed the preparation phase. If we delay our process to include this student, then we will delay feedback to everyone. Here is a genuine motivation for students to submit on time: they will receive rich and detailed feedback as soon as it is ready. Students who hand up late will be assessed in the next round.
That’s how the real world actually works. No-one gives you half marks for something that you do a day late. It’s either accepted or not and, often, you go to the back of the queue. When you miss the bus, you don’t get 50% of the bus. You just have to wait for the next opportunity and, most of the time, there is another bus. Being late once rarely leaves you stranded without apparent hope – unlucky Martian visitors aside.
But there’s more to this. When we have finished with the first group, we can immediately release detailed feedback on what we were expecting to see, providing the best results to students and, from that point on, anyone who submits would have the benefit of information that the first group didn’t have before their initial submission. Rather than make the first group think that they should have waited (and we know students do), we give them the best possible outcome for organising their time.
The next submission deadline is done by everyone with the knowledge gained from the first pass but people who didn’t contribute to it can’t immediately use it for their own benefit. So there’s no free-riding.
There is, of course, a tricky period between the submission deadline and the release, where we could say “Well, they didn’t see the feedback” and accept the work but that’s when we think about the message we want to send. We would prefer students to improve their time management and one part of this is to have genuine outcomes from necessary deadlines.
If we let students keep handing in later and later, we will eventually end up having these late submissions running into our requirement to give feedback. But, more importantly, we will say “You shouldn’t have bothered” to those students who did hand up on time. When you say something like this, students will learn and they will change their behaviour. We should never reinforce behaviour that is the opposite of what we consider to be valuable.
Fairness is a core aesthetic of education. Authentic time management needs to reflect the reality of lost opportunity, rather than diminished recognition of good work in some numerical reduction. Our beauty argument is clear: we can be firm on certain deadlines and remove certain tasks from consideration and it will be a better approach and be more likely to have positive outcomes than an arbitrary reduction scheme already in use.
In my earlier post, I wrote:
Even where we are using mechanical or scripted human [evaluators], the hand of the designer is still firmly on the tiller and it is that control that allows us to take a less active role in direct evaluation, while still achieving our goals.
and I said I’d discuss how we could scale up the evaluation scheme to a large first year class. Finally, thank you for your patience, here it is.
The first thing we need to acknowledge is that most first-year/freshman classes are not overly complex nor heavily abstract. We know that we want to work concrete to abstract, simple to complex, as we build knowledge, taking into account how students learn, their developmental stages and the mechanics of human cognition. We want to focus on difficult concepts that students struggle with, to ensure that they really understand something before we go on.
In many courses and disciplines, the skills and knowledge we wish to impart are fundamental and transformative, but really quite straight-forward to evaluate. What this means, based on what I’ve already laid out, is that my role as a designer is going to be crucial in identifying how we teach and evaluate the learning of concepts, but the assessment or evaluation probably doesn’t require my depth of expert knowledge.
The model I put up previously now looks like this:
My role (as the notional E1) has moved entirely to design and oversight, which includes developing the E3 and E4 tests and training the next tier down, if they aren’t me.
As an example, I’ve put in two feedback points, suitable for some sort of worked output in response to an assignment. Remember that the E2 evaluation is scripted (or based on rubrics) yet provides human nuance and insight, with personalised feedback. That initial feedback point could be peer-based evaluation, group discussion and demonstration, or whatever you like. The key here is that the evaluation clearly indicates to the student how they are travelling; it’s never just “8/10 Good”. If this is a first year course then we can capture much of the required feedback with trained casuals and the underlying automated systems, or by training our students on exemplars to be able to evaluate each other’s work, at least to a degree.
The same pattern as before lies underneath: meaningful timing with real implications. To get access to human evaluation, that work has to go in by a certain date, to allow everyone involved to allow enough time to perform the task. Let’s say the first feedback is a peer-assessment. Students can be trained on exemplars, with immediate feedback through many on-line and electronic systems, and then look at each other’s submissions. But, at time X, they know exactly how much work they have to do and are not delayed because another student handed up late. After this pass, they rework and perhaps the next point is a trained casual tutor, looking over the work again to see how well they’ve handled the evaluation.
There could be more rework and review points. There could be less. The key here is that any submission deadline is only required because I need to allocate enough people to the task and keep the number of tasks to allocate, per person, at a sensible threshold.
Beautiful evaluation is symmetrically beautiful. I don’t overload the students or lie to them about the necessity of deadlines but, at the same time, I don’t overload my human evaluators by forcing them to do things when they don’t have enough time to do it properly.
As for them, so for us.
Throughout this process, the E1 (supervising evaluator) is seeing all of the information on what’s happening and can choose to intervene. At this scale, if E1 was also involved in evaluation, intervention would be likely last-minute and only in dire emergency. Early intervention depends upon early identification of problems and sufficient resources to be able to act. Your best agent of intervention is probably the person who has the whole vision of the course, assisted by other human evaluators. This scheme gives the designer the freedom to have that vision and allows you to plan for how many other people you need to help you.
In terms of peer assessment, we know that we can build student communities and that students can appreciate each other’s value in a way that enhances their perceptions of the course and keeps them around for longer. This can be part of our design. For example, we can ask the E2 evaluators to carry out simple community-focused activities in classes as part of the overall learning preparation and, once students are talking, get them used to the idea of discussing ideas rather than having dualist confrontations. This then leads into support for peer evaluation, with the likelihood of better results.
Some of you will be saying “But this is unrealistic, I’ll never get those resources.” Then, in all likelihood, you are going to have to sacrifice something: number of evaluations, depth of feedback, overall design or speed of intervention.
You are a finite resource. Killing you with work is not beautiful. I’m writing all of this to speak to everyone in the community, to get them thinking about the ugliness of overwork, the evil nature of demanding someone have no other life, the inherent deceit in pretending that this is, in any way, a good system.
We start by changing our minds, then we change the world.
In yesterday’s post, I laid out an evaluation scheme that allocated the work of evaluation based on the way that we tend to teach and the availability, and expertise, of those who will be evaluating the work. My “top” (arbitrary word) tier of evaluators, the E1s, were the teaching staff who had the subject matter expertise and the pedagogical knowledge to create all of the other evaluation materials. Despite the production of all of these materials and designs already being time-consuming, in many cases we push all evaluation to this person as well. Teachers around the world know exactly what I’m talking about here.
Our problem is time. We move through it, tick after tick, in one direction and we can neither go backwards nor decrease the number of seconds it takes to perform what has to take a minute. If we ask educators to undertake good learning design, have engaging and interesting assignments, work on assessment levels well up in the taxonomies and we then ask them to spend day after day standing in front of a class and add marking on top?
Forget it. We know that we are going to sacrifice the number of tasks, the quality of the tasks or our own quality of life. (I’ve written a lot about time before, you can search my blog for time or read this, which is a good summary.) If our design was good, then sacrificing the number of tasks or their quality is going to compromise our design. If we stop getting sleep or seeing our families, our work is going to suffer and now our design is compromised by our inability to perform to our actual level of expertise!
When Henry Ford refused to work his assembly line workers beyond 40 hours because of the increased costs of mistakes in what were simple, mechanical, tasks, why do we keep insisting that complex, delicate, fragile and overwhelmingly cognitive activities benefit from us being tired, caffeine-propped, short-tempered zombies?
We’re not being honest. And thus we are not meeting our requirement for truth. A design that gets mangled for operational reasons without good redesign won’t achieve our outcomes. That’s not going to achieve our results – so that’s not good. But what of beauty?
What are the aesthetics of good work? In Petts’ essay on the Arts and Crafts movement, he speaks of William Morris, Dewey and Marx (it’s a delightful essay) and ties the notion of good work to work that is authentic, where such work has aesthetic consequences (unsurprisingly given that we were aiming for beauty), and that good (beautiful) work can be the result of human design if not directly the human hand. Petts makes an interesting statement, which I’m not sure Morris would let pass un-challenged. (But, of course, I like it.)
It is not only the work of the human hand that is visible in art but of human design. In beautiful machine-made objects we still can see the work of the “abstract artist”: such an individual controls his labor and tools as much as the handicraftsman beloved of Ruskin.
Jeffrey Petts, Good Work and Aesthetic Education: William Morris, the Arts and Crafts Movement, and Beyond, The Journal of Aesthetic Education, Vol. 42, No. 1 (Spring, 2008), page 36
Petts notes that it is interesting that Dewey’s own reflection on art does not acknowledge Morris especially when the Arts and Crafts’ focus on authenticity, necessary work and a dedication to vision seems to be a very suitable framework. As well, the Arts and Crafts movement focused on the rejection of the industrial and a return to traditional crafting techniques, including social reform, which should have resonated deeply with Dewey and his peers in the Pragmatists. However, Morris’ contribution as a Pragmatist aesthetic philosopher does not seem to be recognised and, to me, this speaks volumes of the unnecessary separation between cloister and loom, when theory can live in the pragmatic world and forms of practice can be well integrated into the notional abstract. (Through an Arts and Crafts lens, I would argue that there is are large differences between industrialised education and the provision, support and development of education using the advantages of technology but that is, very much, another long series of posts, involving both David Bowie and Gary Numan.)
But here is beauty. The educational designer who carries out good design and manages to hold on to enough of her time resources to execute the design well is more aesthetically pleasing in terms of any notion of creative good works. By going through a development process to stage evaluations, based on our assessment and learning environment plans, we have created “made objects” that reflect our intention and, if authentic, then they must be beautiful.
We now have a strong motivating factor to consider both the often over-looked design role of the educator as well as the (easier to perceive) roles of evaluation and intervention.
I’ve revisited the diagram from yesterday’s post to show the different roles during the execution of the course. Now you can clearly see that the course lecturer maintains involvement and, from our discussion above, is still actively contributing to the overall beauty of the course and, we would hope, it’s success as a learning activity. What I haven’t shown is the role of the E1 as designer prior to the course itself – but that’s another post.
Even where we are using mechanical or scripted human markers, the hand of the designer is still firmly on the tiller and it is that control that allows us to take a less active role in direct evaluation, while still achieving our goals.
Do I need to personally look at each of the many works all of my first years produce? In our biggest years, we had over 400 students! It is beyond the scale of one person and, much as I’d love to have 40 expert academics for that course, a surplus of E1 teaching staff is unlikely anytime soon. However, if I design the course correctly and I continue to monitor and evaluate the course, then the monster of scale that I have can be defeated, if I can make a successful argument that the E2 to E4 marker tiers are going to provide the levels of feedback, encouragement and detailed evaluation that are required at these large-scale years.
Tomorrow, we look at the details of this as it applies to a first-year programming course in the Processing language, using a media computation approach.
This is a great TED talk. Joi Ito, director of the MIT media lab, talks about the changes that technological innovation have made to the ways that we can work on problems and work together.
I don’t agree with everything, especially the pejorative cast on education, but I totally agree that the way that we construct learning environments has to take into the way that our students will work, rather than trying to prepare them for the world that we (or our parents) worked in. Pretending that many of our students will have to construct simple things by hand, when that is what we were doing fifty years ago, takes up time that we could be using for more authentic and advanced approaches that cover the same material. Some foundations are necessary. Some are tradition. Being a now-ist forces us to question which is which and then act on that knowledge.
Your students will be able to run enterprises from their back rooms that used to require the resources of multinational companies. It’s time to work out what they actually need to get from us and, once we know that, deliver it. There is a place for higher education but it may not be the one that we currently have.
A lot of what I talk about on this blog looks as if I’m being progressive but, really, I’m telling you what we already know to be true right now. And what we have known to be true for decades, if not centuries. I’m not a futurist, at all. I’m a now-ist with a good knowledge of history who sees a very bleak future if we don’t get better at education.
(Side note: yes, this is over twelve minutes long. Watch our around the three minute mark for someone reading documents on an iPad up the back, rather than watching him talk. I think this is a little long and staged, when it could have been tighter, but that’s the TED format for you. You know what you’re getting into and, because it’s not being formally evaluated, it doesn’t matter as much if you recall high-level rather than detail.)