My keynote slides from Confluence 2021 – STEAM engines: on building and testing the machines in our students’ minds

STEAM Engines

These are my slides for my keynote talk at the IEEE 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence-2021), hosted by Amity University, India, 28th January 2021. Technically it was 27th January here in Vancouver when I started, but 28th January when I finished. I hate timezones.

The talk winds up being about how to be a (mainly online) teacher in science, technology, engineering, and mathematics (STEM) – not how to teach, as such – but it gets to the point circuitously through discussing some aspects of the nature of technology, using a subset of my coparticipation model. In (very brief) the idea behind that is that ‘technology’ means organizing stuff to do stuff (any stuff), and we are not just users but participants in that organization, either playing our roles correctly (hard technologies) or organizing stuff ourselves (soft technologies). Almost always, thanks to the fact that almost all technologies are assemblies of and with other technologies, it is a mix of the two. In the technologies of learning there are many coparticipants, all playing roles, soft or hard or both. The designated teacher is only one of these, of varying significance.

The talk dwelt on the technological nature of teaching itself, and on the technological nature of the results of teaching. Teaching (as a distributed process) can usefully be seen as a process of building technologies in learners’ minds, some hard (training), some soft (teaching). These technologies can, like all technologies, be assembled together or with others, so our minds are both enacted and extended through technologies with one another and with the constructed world around us.

In STEM subjects there is a tendency to focus a lot more on building hard technologies than on soft technologies, because there tends to be a lot of hard stuff to learn before you can do anything much at all. There are many other subjects like this, including one of the biggest, language learning. The same is actually true in softer disciplines but students tend to come equipped with a lot of the basic hard stuff – especially language, debating skills, etc – already, so a really big part of the machine already exists. However, as much as it is in the liberal arts (the ‘A’ in STEAM), it is actually the soft technologies – what we do with those hard machines in our minds, the soft technologies we assemble with them – that actually matters, personally, in the workplace, and in our social lives. Also, from a motivational perspective it is normally a really bad idea to force people to learn a lot of hard stuff without them actually having a personal need or desire to do so. Training people in the hard stuff without using it in a soft, personally/socially relevant and meaningful context is a recipe for failure, though the fact that hard skills and knowledge can be accurately measured means that assessments of it tend to create an illusion of success. ‘Success’, though, just means that the hard machine works as intended, not that it actually does anything useful.

Avoiding this chicken and egg problem – the need for hard skills before you can do anything, but the uselessness of them in isolation – is not difficult. In fact, it is how we learn to speak, and many other things. It means letting go of the notion that teachers control everything, embracing the distributed nature of teaching, and designing ways of learning that support autonomy, achievable challenge, and relatedness. To do this means making learning (not just its products) visible, creating a culture and tools for sharing, and designing in support processes to help learners overcome obstacles. Basically, from a designated teacher’s perspective, it’s about letting go and staying close. It’s much the same as how we bring up our kids, as it happens.

It was an odd session, a lecture with no direct interaction. In itself, this would not be a great learning experience for anyone. However – and this is one of my big points – it is the assembly that matters, not the individual components, and I was not the one doing that assembly. Seen as a component of learning, attended without coercion or extrinsic goals, my little lecture is something that can be assembled to make something quite useful.

How distance changes everything: slides from my keynote at the University of Ottawa

These are the slides from my keynote at the University of Ottawa’s “Scaffolding a Transformative Transition to Distance and Online Learning” symposium today. In the presentation I discussed why distance learning really is different from in-person learning, focusing primarily on the fact that they are the motivational inverse of one another. In-person teaching methods evolved in response to the particular constraints and boundaries imposed by physics, and consist of many inventions – pedagogical and otherwise – that are counter-technologies designed to cope with the consequences of teaching in a classroom, a lot of which are not altogether wise. Many of those constraints do not exist online, and yet we continue to do very similar things, especially those that control and dictate what students should do, as well as when, and how they should do it. This makes no sense, and is actually antagonistic to the natural flow of online learning. I provided a few simple ideas and prompts for thinking about how to go more with the flow.

The presentation was only 20 minutes of a lively and inspiring hour-long session, which was fantastic fun and provided me with many interesting questions and a chance to expand further on the ideas.


Technology, technique, and teaching

These are the slides from my recent talk with students studying the philosophy of education at Pace University.

This is a mashup of various talks I have given in recent years, with a little new stuff drawn from my in-progress book. It starts with a discussion of the nature of technology, and the distinction between hard and soft technologies that sees relative hardness as the amount of pre-orchestration in a technology (be it a machine or a legal system or whatever). I observe that pedagogical methods (‘pedagogies’ for short) are soft technologies to those who are applying them, if not to those on the receiving end. It is implied (though I forgot to explicitly mention) that hard technologies are always more structurally significant than soft ones: they frame what is possible.

All technologies are assemblies, and (in education), the pedagogies applied by learners are always the most important parts of those assemblies. However, in traditional in-person classrooms, learners are (by default) highly controlled due to the nature of physics – the need to get a bunch of people together in one place at one time, scarcity of resources,  the limits of human voice and hearing, etc – and the consequent power relationships and organizational constraints that occur.  The classroom thus becomes the environment that frames the entire experience, which is very different from what are inaccurately described as online learning environments (which are just parts of a learner’s environment).

Because of physical constraints, the traditional classroom context is inherently very bad for intrinsic motivation. It leads to learners who don’t necessarily want to be there, having to do things they don’t necessarily want to do, often being either bored or confused. By far the most common solution to that problem is to apply externally regulated extrinsic motivation, such as grades, punishments for non-attendance, rules of classroom behaviour, and so on. This just makes matters much worse, and makes the reward (or the avoidance of punishment) the purpose of learning. Intelligent responses to this situation include cheating, short-term memorization strategies, satisficing, and agreeing with the teacher. It’s really bad for learning. Such issues are not at all surprising: all technologies create as well as solve problems, so we need to create counter technologies to deal with them. Thus, what we normally recognize as good pedagogy is, for the most part, a set of solutions to the problems created by the constraints of in-person teaching, to bring back the love of learning that is destroyed by the basic set-up. A lot of good teaching is therefore to do with supporting at least better, more internally regulated forms of extrinsic motivation.

Because pedagogies are soft technologies, skill is needed to use them well. Harder pedagogies, such as Direct Instruction, that are more prescriptive of method tend (on average) to work better than softer pedagogies such as problem-based learning, because most teachers tend towards being pretty average: that’s implicit in the term, after all. Lack of skill can be compensated for through the application of a standard set of methods that only need to be done correctly in order to work. Because such methods can also work for good teachers as well as the merely average or bad, their average effectiveness is, of course, high. Softer pedagogical methods such as active learning, problem-based learning, inquiry-based learning, and so on rely heavily on passionate, dedicated, skilled, time-rich teachers and so, on average, tend to be less successful. However, when done well, they outstrip more prescriptive methods by a large margin, and lead to richer, more expansive outcomes that go far beyond those specified in a syllabus or test. Softer technologies, by definition, allow for greater creativity, flexibility, adaptability, and so on than harder technologies but are therefore difficult to implement. There is no such thing as a purely hard or purely soft technology, though, and all exist on a spectrum,. Because all pedagogies are relatively soft technologies, even those that are quite prescriptive, almost any pedagogical method can work if it is done well: clunky, ugly, weak pedagogies used by a fantastic teacher can lead to great, persistent, enthusiastic learning. As Hattie observes, almost everything works – at least, that’s true of most things that are reported on in educational research studies :-). But (and this is the central message of my book, the consequences of which are profound) it ain’t what you do, it’s the way that you do it, that’s what gets results.

Problems can occur, though, when we use the same methods that work in person in a different context for which they were not designed. Online learning is by far the most dominant mode of learning (for those with an Internet connection – some big social, political, economic, and equity issues here) on the planet. Google, YouTube, Wikipedia, Reddit, StackExchange, Quora, etc, etc, etc, not to mention email, social networking sites, and so on, are central to how most of us in the online world learn anything nowadays. The weird thing about online education (in the institutional sense) is that online learning is far less obviously dominant, and tends to be viewed in a far less favourable light when offered as an option. Given the choice, and without other constraints, most students would rather learn in-person than online. At least in part, this is due to the fact that those of us working in formal online education continue to apply pedagogies and organizational methods that solved problems in in-person classrooms, especially with regard to teacher control: the rewards and punishments of grades, fixed length courses, strictly controlled pathways, and so on are solutions to problems that do not exist or that exist in very different forms for online learners, whose learning environment is never entirely controlled by a teacher.

The final section of the presentation is concerned with what – in very broad terms – native distance pedagogies might look like. Distance pedagogies need to acknowledge the inherently greater freedoms of distance learners and the inherently distributed nature of distance learning. Truly learner-centric teaching does not seek to control, but to support, and to acknowledge the massively distributed nature of the activity, in which everyone (including emergent collective and networked forms arising from their interactions) is part of the gestalt teacher, and each learner is – from their perspective – the most important part of all of that. To emphasize that none of this is exactly new (apart from the massive scale of connection, which does matter a lot), I include a slide of Leonardo’s to-do list that describes much the same kinds of activity as those that are needed of modern learners and teachers.

For those seeking more detail, I list a few of what Terry Anderson and I described as ‘Connectivist-generation’ pedagogical models. These are far more applicable to native online learning than earlier pedagogical generations that were invented for an in-person context. In my book I am now describing this new, digitally native generation as ‘complexivist’ pedagogies, which I think is a more accurate and less confusing name. It also acknowledges that many theories and models in the family (such as John Seely Brown’s distributed cognitive apprenticeship) predate Connectivism itself. The term comes from Davis’s and Sumara’s 2006 book, ‘Complexity and Education‘, which is a great read that deserves more attention than it received when it was published.

Slides: Technology, technique and teaching

Beyond learning outcomes

What we teach, what a student learns, what we assess This is a slide deck for a talk I’m giving today, at a faculty workshop, on the subject of learning outcomes.

I think that well-considered learning outcomes can be really helpful when planning and designing learning activities, especially where there is a need to assess learning. They can help keep a learning designer focused, and to remember to ensure that assessment activities actually make a positive contribution to learning. They can also be helpful to teachers while teaching, as a framework to keep them on track (if they wish to remain on track).  However, that’s about it. Learning outcomes are not useful when applied to bureaucratic ends, they are very poor descriptors of what learning actually happens, as a rule, and they are of very little (if any) use to students under most circumstances (there are exceptions – it’s a design issue, not a logical flaw).

The big point of my talk, though, is that we should be measuring what students have actually learned, not whether they have learned what we think we have taught, and that the purpose of everything we do should be to support learning, not to support bureaucracy.

I frame this in terms of the relationships between:

  • what we teach (what we actually teach, not just what we think we are teaching, including stuff like attitudes, beliefs, methods of teaching, etc),
  • what a student learns in the process (an individual student, not students as a whole), and
  • what we assess (formally and summatively, not necessarily as part of the learning process).

There are many things that we teach that any given student will not learn, albeit that (arguably) we wouldn’t be teaching at all if learning were not happening for someone. Most students get a small subset of that. There are also many things that we teach without intentionally teaching, not all of them good or useful.

There are also very many things that students learn that we do not teach, intentionally or otherwise. In fact, it is normal for us to mandate this as part of a learning design: any mildly creative or problem-solving/inquiry-oriented activity will lead to different learning outcomes for every learner. Even in the most horribly regimented teaching contexts, students are the ones that connect everything together, and that’s always going to include a lot more than what their teachers teach.

Similarly, there are lots of things that we assess that we do not teach, even with great constructive alignment. For example, the students’ ability to string a sentence together tends to be not just a prerequisite but something that is actively graded in typical assessments.

My main points are that, though it is good to have a teaching plan (albeit that it should be flexible,  reponsive to student needs, and should accommodate serendipity)learning :

  • students should be participants in planning outcomes and
  • we should assess what students actually learn, not what we think we are teaching.

From a learning perspective, there’s less than no point in summatively judging what learners have not learned. However, that’s exactly what most institutions actually do. Assessment should be about how learners have positively changed, not whether they have met our demands.

This also implies that students should be participants in the planning and use of learning outcomes: they should be able to personalize their learning, and we should recognize their needs and interests. I use andragogy to frame this, because it is relatively uncontroversial, is easily understood, and doesn’t require people to change everything in their world view to become better teachers, but I could have equally used quite a large number of other models. Connectivism, Communities of Practice, and most constructivist theories, for instance, force us to similar conclusions.

I suggest that appreciative inquiry may be useful as an approach to assessment, inasmuch as the research methodology is purpose-built to bring about positive change, and its focus on success rather than failure makes sense in a learning context.

I also suggest the use of outcome mapping (and its close cousin, outcome harvesting) as a means of capturing unplanned as well as planned outcomes. I like these methods because they only look at changes, and then try to find out what led to those changes. Again, it’s about evaluation rather than judgment.

DT&L2018 spotlight presentation: The Teaching Gestalt

The teaching gestalt  presentation slides (PDF, 9MB)

This is my Spotlight Session from the 34th Distance Teaching & Learning Conference, at Wisconsin Madison, August 8th, 2018. Appropriately enough, I did this online and at a distance thanks to my ineptitude at dealing with the bureaucracy of immigration. Unfortunately my audio died as we moved to the Q&A session so, if anyone who was there (or anyone else) has any questions or observations, do please post them here! Comments are moderated.

The talk was concerned with how online learning is fundamentally different from in-person learning, and what that means for how (or even whether) we teach, in the traditional formal sense of the word.

Teaching is always a gestalt process, an emergent consequence of the actions of many teachers, including most notably the learners themselves, which is always greater than (and notably different from) the sum of its parts. This deeply distributed process is often masked by the inevitable (thanks to physics in traditional classrooms) dominance of an individual teacher in the process. Online, the mask falls off. Learners invariably have both far greater control and far more connection with the distributed gestalt. This is great, unless institutional teachers fight against it with rewards and punishments, in a pointless and counter-productive effort to try to sustain the level of control that is almost effortlessly attained by traditional in-person teachers, and that is purely a consequence of solving problems caused by physical classroom needs, not of the needs of learners. I describe some of the ways that we deal with the inherent weaknesses of in-person teaching especially relating to autonomy and competence support, and observe how such pedagogical methods are a solution to problems caused by the contingent side effects of in person teaching, not to learning in general.

The talk concludes with some broad characterization of what is different when teachers choose to let go of that control.  I observe that what might have been Leonardo da Vinci’s greatest creation was his effective learning process, without which none of the rest of his creations could have happened. I am hopeful that now, thanks to the connected world that we live in, we can all learn like Leonardo, if and only if teachers can learn to let go.

Turns out the STEM ‘gender gap’ isn’t a gap at all

Grace Hopper and Univac, image from least in Ontario, it seems that there are about as many women as men taking STEM programs at undergraduate level. This represents a smaller percentage of women taking STEM subjects overall because there are way more women entering university in the first place. A more interesting reading of this, therefore, is not that we have a problem attracting women to science, technology, engineering, and mathematics, but that we have a problem attracting men to the humanities, social sciences, and the liberal arts. As the article puts it:

“it’s not that women aren’t interested in STEM; it’s that men aren’t interested in poetry—or languages or philosophy or art or all the other non-STEM subjects.”

That’s a serious problem.

As someone with qualifications in both (incredibly broad) areas, and interests in many sub-areas of each,  I find the arbitrary separation between them to be ludicrous, leading to no end of idiocy at both extremes, and little opportunity for cross-fertilization in the middle. It bothers me greatly that technology subjects like computing or architecture should be bundled with sciences like biology or physics, but not with social sciences or arts, which are way more relevant and appropriate to the activities of most computer professionals. In fact, it bothers me that we feel the need to separate out large fields like this at all. Everyone plays lip service to cross-disciplinary work but, when we try to take that seriously and cross the big boundaries, there is so much polarization between the science and arts communities that they usually don’t even understand one another, let alone work in harmony. We don’t just need more men in the liberal arts – we need more scientists, engineers, and technologists to cross those boundaries, whatever their gender. And, vice versa, we need more liberal artists (that sounds odd, but I have no better term) and social scientists in the sciences and, especially, in technology.

But it’s also a problem of category errors in the other direction. This clumping together of the whole of STEM conceals the fact that in some subjects – computing, say – there actually is a massive gender imbalance (including in Ontario), no matter how you mess with the statistics. This is what happens when you try to use averages to talk about specifics: it conceals far more than it reveals.

I wish I knew how to change that imbalance in my own designated field of computing, an area that I deliberately chose precisely because it cuts across almost every other field and did not limit me to doing one kind of thing. I do arts, science, social science, humanities, and more, thanks to working with machines that cross virtually every boundary.

I suspect that fixing the problem has little to do with marketing our programs better, nor with any such surface efforts that focus on the symptoms rather than the cause. A better solution is to accept and to celebrate the fact that the field of computing is much broader and vastly more interesting than the tiny subset of it that can be described as computer science, and to build up from there. It’s especially annoying that the problem exists at Athabasca where a wise decision was made long ago not to offer a computer science program. We have computing and information systems programs, but not any programs in computer science. Unfortunately, thanks to a combination of lazy media and computing profs (suffering from science envy) that promulgate the nonsense, even good friends of mine that should know better sometimes describe me as a computer scientist (I am emphatically not), and even some of our own staff think of what we do as computer science. To change that perception means not just a change in nomenclature, but a change in how and what we, at least in Athabasca, teach. For example, we might mindfully adopt an approach that contextualizes computing around projects and applications, rather than its theory and mechanics. We might design a program that doesn’t just lump together a bunch of disconnected courses and call it a minor but that, in each course (if courses are even needed), actively crosses boundaries – to see how code relates to poetry, how art can inform and be informed by software, how understanding how people behave can be used in designing better systems, how learning is changed by the tools we create, and so on.

We don’t need disciplines any more, especially not in a technology field. We need connections. We don’t need to change our image. We need to change our reality. I’m finding that to be quite a difficult challenge right now.


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terra0 – a forest that will one day buy itself

I love this art project – a forest that owns itself and that makes money on its own behalf, eventually with no human control or ownership. From the blurb…

“The Project emerged from research in the fields of crypto governance, smart contracts, economics and questions regarding representations of natural systems in the techno-sphere. It creates a framework whereby a forest is able to sell licences to log its own trees through automated processes, smart contracts and blockchain technology. “

But it gets better…

“The terra0 project creates a scenario whereby the forest, augmented through automated processes, utilitizes itself and thereby accumulates capital. A shift from valorisation through third parties to a self-utilization makes it possible for the forest to procure its real counter-value and eventually buy itself. The augmented forest is not only owner of itself, but is thus in the position to buy more ground and therefore to expand.”

Wonderful, immensely thought-provoking, deeply subversive.

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A Universe Explodes: A Blockchain Book, from Editions At Play

A Universe Explodes A really nice project from the Editions at Play team at Google, in which blockchain is used both to limit supply to a digital book (only 100 copies made) and, as the book is passed on, to make it ‘age,’ in the sense that each reader must remove two words from each page and add one of their own before passing it on (that they are obliged to do). Eventually, it decays to the point of being useless, though I think the transitional phases might be very interesting in their own right.

I was thinking something very vaguely along these lines would be an interesting idea and had started making notes about how it would work, but it seemed so blindingly obvious that somebody must have already done it. Blockchain technologies for publishing are certainly being considered by many people, and some are being implemented.   The Alliance of Independent Authors seems to have the most practical plans for using Blockchain for that purpose. Another similar idea comes with the means to partially compensate publishers for such things (as though they needed even more undeserved profits). Another interesting idea is to use Blockchain Counterparty tokens to replace ISBN numbers. However, A Universe Explodes is the only example I have so far found of building in intentional decay. It’s one of a range of wonderfully inventive and inspiring books that could only possibly exist in digital media at the brilliant Editions at Play site.

Though use of Blockchain for publishing is a no-brainer, it’s the decay part that I like most, and that I was thinking about before finding this. Removing and adding words is not an accurate representation of the typical decay of a physical book, and it is not super-practical at a large scale, delightful though it is. My first thoughts were, in a pedestrian way, to build in a more authentic kind of decay. It might, for instance, be possible to simply overlay a few more pixels with each reading, or to incrementally grey-out or otherwise visually degrade the text (which might have some cognitive benefits too, as it happens). That relies, however, on a closed application system, or a representation that would be a bit inflexible (e.g. a vector format like SVG to represent the text, or even a bitmap) otherwise it would be too easy to remove such additions simply by using a different application. And, of course, it would be bad for people with a range of disabilities, although I guess you could perform similar mutilations of other representations of the text just as easily. That said, it could be made to work. There’s no way it is even close to being as good as making something free of DRM, of course, but it’s a refinement that might be acceptable to greedy publishers that would at least allow us to lend, give, or sell books that we have purchased to others.

My next thought was that you could, perhaps more easily and certainly more interestingly, make marginalia (graphics and text) a permanent feature of the text once ownership was transferred, which would be both annoying and enlightening, as it is in physical books. One advantage would be that it reifies the concept of ownership – the intentional marks made on the book are a truer indication of the chain of owners than anything more abstract or computer-generated. It could also be a really interesting and useful way to tread a slightly more open path than most ugly DRM implementations, inasmuch as it could allow the creation of deliberately annotated editions (with practical or artistic intent) without the need for publisher permission. That would be good for textbooks, and might open up big untapped markets: for instance, I’d quite often rather buy an ebook annotated by one of my favourite authors or artists than the original, even if it cost more. It could be interestingly subversive, too. I might even purchase one of Trump’s books if it were annotated (and re-sold) by journalists from the Washington Post or Michael Moore, for example. And it could make a nice gift to someone to provide a personally embellished version of a text. Combined with the more prosaic visual decay approach, this could become a conversation between annotators and, eventually, become a digital palimpsest in which the original text all but disappears under generations of annotation. I expect someone has already thought of that but, if not, maybe this post can be used to stop someone profiting from it with a patent claim.

In passing, while searching, I also came across which is both cunning and evil: it lets publishers embed Bitcoin bounties in ebooks that ‘pirates’ can claim and, in the process, alert the publisher to the identity of the person responsible. Ugly, but very ingenious. As the creators claim, it turns pirates on other pirates by offering incentives, yet keeping the whole process completely anonymous. Eeugh.

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Evidence mounts that laptops are terrible for students at lectures. So what?

The Verge reports on a variety of studies that show taking notes with laptops during lectures results in decreased learning when compared with notes taken using pen and paper. This tells me three things, none of which is what the article is aiming to tell me:

  1. That the institutions are teaching very badly. Countless decades of far better evidence than that provided in these studies shows that giving lectures with the intent of imparting information like this is close to being the worst way to teach. Don’t blame the students for poor note taking, blame the institutions for poor teaching. Students should not be put in such an awful situation (nor should teachers, for that matter). If students have to take notes in your lectures then you are doing it wrong.
  2. That the students are not skillful laptop notetakers. These studies do not imply that laptops are bad for notetaking, any more than giving students violins that they cannot play implies that violins are bad for making music. It ain’t what you do, it’s the way that you do it. If their classes depend on effective notetaking then teachers should be teaching students how to do it. But, of course, most of them probably never learned to do it well themselves (at least using laptops). It becomes a vicious circle.
  3. That laptop and, especially, software designers have a long way to go before their machines disappear into the background like a pencil and paper. This may be inherent in the medium, inasmuch as a) they are vastly more complex toolsets with much more to learn about, and b) interfaces and apps constantly evolve so, as soon as people have figured out one of them, everything changes under their feet. It becomes a vicious cycle.

The extra cognitive load involved in manipulating a laptop app (and stopping the distractions that manufacturers seem intent on providing even if you have the self-discipline to avoid proactively seeking them yourself) can be a hindrance unless you are proficient to the point that it becomes an unconscious behaviour. Few of us are. Tablets are a better bet, for now, though they too are becoming overburdened with unsought complexity and unwanted distractions. I have for a couple of years now been taking most of my notes at conferences etc with an Apple Pencil and an iPad Pro, because I like the notetaking flexibility, the simplicity, the lack of distraction (albeit that I have to actively manage that), and the tactile sensation of drawing and doodling. All of that likely contributes to making it easier to remember stuff that I want to remember. The main downside is that, though I still gain laptop-like benefits of everything being in one place, of digital permanence, and of it being distributed to all my devices, I have, in the process, lost a bit in terms of searchability and reusability. I may regret it in future, too, because graphic formats tend to be less persistent over decades than text. On the bright side, using a tablet, I am not stuck in one app. If I want to remember a paper or URL (which is most of what I normally want to remember other than my own ideas and connections that are sparked by the speaker) I tend to look it up immediately and save it to Pocket so that I can return to it later, and I do still make use of a simple notepad for things I know I will need later. Horses for courses, and you get a lot more of both with a tablet than you do with a pencil and paper. And, of course, I can still use pen and paper if I want a throwaway single-use record – conference programs can be useful for that.





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Signal : now with proper desktop apps

Signal is arguably the most open, and certainly the most secure, privacy-preserving instant messaging/video or voice-calling system available today. It is open source, ad-free, standards-based, simple, and very well designed. Though not filled with bells and whistles, for most purposes it is a far better alternative to Facebook-owned WhatsApp or other near-competitors like Viber, FaceTime, Skype, etc, especially if you have any concerns about your privacy. Like all such things, Metcalfe’s Law means its value increases with every new user added to the network. It’s still at the low end of the uptake curve, but you can help to change that – get it now and tell your friends!

Like most others of its ilk it hooks into your cellphone number rather than a user name but, once you have installed it on your smartphone, you can associate that number (via a simple 2D barcode) with a desktop client. Until recently it only supported desktop machines via a Chrome browser (or equivalent – I used Vivaldi) but the new desktop clients are standalone, so you don’t have to grind your system to a halt or share data with Google to install it. It is still a bit limited when it comes to audio (simple messaging only) and there still appears to be no video support (which is available on smartphone clients) but this is good progress.

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