Distinctively human?

1. In his latest presentation, Stephen Downes once more advocated personal learning. Previously I have often wondered if it is just a matter of style and taste whether one emphasizes an individual access to learning or not, i.e., whether one prefers learning in the same way a backpacker individual tourist learns about the world, or if one finds it easier and more effective to go with a guided group tour to get the proven optimized standard sight.

 Photo CC-BY by Flickr user supermoving

As a tourist, I never liked the group style, but for learning, the lazy way of letting stuff just wash over me, did have its charme. And it sounds even more promising now since the optimization is ever more perfected by personalization. And since it seems like a ‘science-based’ way to success.

2. There may be even a third type of learners: the machine ‘students’ who do their machine learning. Previously, I often wondered why machines needed to ‘learn’, at all: they don’t need to gradually accumulate their knowledge because they just do a database lookup; they don’t have to learn their rules because they receive them through programming; and learning for any kind of later independence seemed unimaginable.

Now I know that it is not rules, but patterns, what they learn: they learn from data that comes in large amounts and from data that is randomly distributed — much like lifelong human experiences. So our automated fellow learners are already far away from the group path, and they are rapidly approaching our human styles.

3. In an Australian conference last week, @SBuckShum talked about the Cognitive Automation and recommended that we

“Cultivate those qualities that are distinctively human”

So what will prove as ‘distinctively human’ ?

I think it is exactly the personal approach which cannot be standardized, that cannot be automated. It is the individuality and subjectivity that guarantees sufficient diversity for further evolution and to avoid collapsing into a ‘black hole’ of power law distributions, and that guarantees sufficient embodiment for staying grounded in reality.

By contrast, approaches that only accept objectivity, and strive for algorithms that promise an optimized, rational, assessable, uniform solution — are eligible for cognitive automation, sooner or later.

4. This also applies to the optimization of learning. Only standardized, easily assessable learning can be optimized, and in particular, content knowledge is the ideal example. If we compare learning to carrying a load upstairs to the attic storage, there are two distinct goals. One is that the load should be stored up there, and the other is to work out one’s muscles. If it is only necessary that the stuff is accumulated up there in our brain, a ‘lift’ would be a welcome optimization. But jobs that only rely on such content storage, are probably the first to be eliminated.

In a recent blog post, Stephen calls it an ‘either or’ dichotomy:

“are you providing content knowledge or, are you providing literacies?”

and I think it it really a shame how ignorant our universities are in dealing with this question.

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Seven habits of…

The seven habits of successful people? Well, yes, except that it is only one person (me), and ‘successful’ just means that I am highly content with the workflow that I am going to describe.

  1. I don’t open my email if I could not react, anyway.
  2. I prefer RSS over email news.
  3. I store an item whenever I have reflected on it.
  4. I don’t care much while capturing stuff,
  5. But I care a lot for grouping it later.
  6. Of course, I use my Thought condensr.de think tool.
  7. I take an easy way to prune stuff.

Read more:

1. I don’t open my email if I could not react, anyway.

If I am preoccupied with a larger piece of work, or if I am not near my desk, a newly arrived email must wait. My curiosity about it is smaller than the reluctance to get interrupted. Enjoying an email means for me, to start thinking about a response rightaway, or to take the time to follow its scents.

2. I prefer RSS over email news.

My categorized RSS subscriptions come across to me as offerings rather than urges. They give me a sense of space, as if I was visiting the authors on their respective front porches, and I have already a certain idea of what awaits me there. Thus it is easier to notice the salient, unexpected, relevant news to pick.

Picking from RSS is more like ‘pull’ than ‘push’. It does not urge me to almost synchronously react — not even to decide if I delete an item or keep it or where to keep it: it rests on its web site without bothering me. Push vs. pull is a big issue today where lots of sites try to control our attention and patronize us. Joi Ito’s “Whiplash” has a whole chapter about pull over push.

Other kinds of notifications feel like a terribly linear assembly line. Once I get into such an unstructured linear list, I feel that it will mean a long time-span with almost no chance to interrupt and digress and still find my way back, and this is somehow daunting. ‘The stream’ is an ineffective construct just made to lure us into filter recommendations. It eats up power to connect items to their contexts rather than to relevant similar ones. Small bloggers are just ‘flushed’ down the stream. See also what Mike Caulfield writes about the stream.

3. I store an item whenever I have reflected on it.

Sometimes I am not sure if I have stored something in my ‘knowledge base’ of my harddisk. Then this little simple rule of thumb helps: If I have thought for some time about an item, I will store its url or a note. Even if it turned out to be rubbish (which I might note in brackets in the file name), I will vaguely remember it as somehow relevant and then I might try to find it again, so it is better to save a cue rather than search it in vain.

4. I don’t care much while capturing stuff.

Capturing an idea must be quick rather than optimized, to minimize the distraction from the task at hand. I don’t think about what would be the most appropriate medium for the note. I fire up Notepad and store the idea in a single separate file. I don’t think about the filename either, and I abbreviate ruthlessly. For my idea notes, the names are almost like a ‘private language’: typically, such a note is a one- or two-liner saying “xxxxxxxx has something to do with yyyyyyyy?”, and then the filename will be xxxxYyyy.txt which I will forget before I revisit my notes.

And I don’t care too much about the folder names. My filing is not optimized for directly searching, but for browsing in context and seeing what is there. So the folder names just need to be recognized within the context of the containing folder. Whenever I was looking in vain for something in one folder, I create a folder shortcut there, pointing me to the other folder next time. So over time, a dense network emerges, the traversing of which strengthens my awareness of the relationships.

And it is not important to find the optimized procedure having the shortest click path. Rather, a default procedure that is well-rehearsed will do to minimize distraction. So, my idea note files are saved to the desktop and dropped to a folder called ‘new’ — via a folder shortcut on the desktop. This minimally invasive note-taking guarantees that no tiny humble idea gets ignored and can be processed later — see next section.

5. But I care a lot for grouping it later.

In contrast to the careless capturing, I really cultivate my notes collection later. The crucial thing for me is not to classify them prematurely, but to be open for new similarities, connections, and patterns, and for new emerging categories becoming necessary. Once I put the notes into the disjoint categories of my private localhost WordPress, I have shuffled them around for so long that I know them well, and I rarely need cross-links.

6. Of course, I use my Thought condensr.de think tool

Whenever I start to produce some output, I drop all pertinent stuff into my condensr.de which helps me with the step between stuff collection and outline.

7. I take an easy way to prune stuff.

Sometimes I notice that a digression needs pruning, only after I have struggled quite a while with a difficult text, and I would not want to delete it entirely. Then I am glad about this well-rehearsed habit: I cut the text and paste it into a little text file called xxxxYyyy+1.txt — plus 1, for some distant next attempt that may never happen.

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Week 1 of new cMOOC

Finally I was accepted at the Federica.EU platform for the new cMOOC “Connectivism and Learning”. (Two earlier attempts failed even though I clicked on “I did not receive activation email”, and their support email address did not (yet?) work. But now the activation email was successfully received in my spam folder.) On the ‘plattaforma’, I did not find anything except Downes’s excellent videos and slides, but this should not be a problem in a cMOOC with blogs and all that. And the only remaining problem, the missing hashtag, can be added later on. So let’s start.


Stephen offered plenty of great questions for reflection, e.g. “Reflecting on your own learning, can you identify a process that you currently follow?” or “Do you think you could improve your learning if you had a better understanding of how you learn?” These questions remind me that I wanted to compile some notes about my workflows, hopefully soon.

Another question was about interactivity:

“Why do we need it ? […] second, we need to know that other people are in the learning experience with us.”

IMHO, this includes that we are watching our co-learners in their stage of recognizing, which is much more similar to the recognizing that we need to do ourselves, than the settled, packaged, resources presented by a traditional teacher. If the co-learners ‘teach’ us what they have just come to understand, they are ‘modeling and demonstrating’ their own understanding process.

And watching our co-humans, puts us automatically in a mode which is more open to immediate, contextual, multipoint, ‘all-at-once’ experience than consuming a slice of canned information. Frankly, I was long sceptical about the value of ‘social’ learning, and I suspected that it just helps those who are not willing to interact directly with the resources, and (of course) benefits those who repeat the stuff by explaining it to the weaker students. But in the meantime I have understood that there is more to it.

Posted in Learning | 3 Comments

Lighthearted Exercise

In time for New Year’s Eve, Magdalena Böttger has invented a hilarious-profound oracle game: You set goals for the following years, and throw the dice to determine your achievements.

I look forward to playing it, and in the meantime I noticed that her 20 interesting goals can also be used for something entirely different.

You can try to find connections between them. E.g., some are similar to others, or impact others in positive or negative ways, seem to include others, or be otherwise related. And thinking about their complex relationships can be too difficult if you only look at a linear list and most of the connections are only in your head. This might be a good opportunity to demonstrate the benefit of a think tool.

Select all the text in the list, drag and drop it into my tool — and start connecting and rearranging. (But don’t miss midnight…)

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Magic of Zettelkasten

My think tool has now a new functionality when exporting to other note-taking systems: It can preserve hierarchical (tree) structures found in imported note collections (such as Mindmaps, or OPML including Scrivener exports), and it can even derive such a tree structure from my own network maps.

zettelkastenMy reason for the improvement was that there are systems which are not limiting their users to narrow-minded “box” thinking, but still use tree structures. One of them is iMappingTool which I have long admired for its aesthetically pleasing visual overview. Using my tool, you can now import your notes there and start imapping.

Another one is a system that has been invented long before software systems, and has always been surrounded by an aura of magic: The Zettelkasten (note box, card index) of the sociologist Niklas Luhmann. In a 1989 video, he shows us (minute 37:25, with English subtitles) how he worked with it, but it is only now that the University of Bielefeld analyzes his legacy and researches its magic.

What I understood is that, besides a dense network of cross references, the distinctive feature is the following: He allowed for arbitrary branching at every point in his hierarchical numbering scheme, in other words, a child note did not have to belong to the same topic category as its original ancestor notes. It was sufficient that its content was somehow connected to its immediate parent.

Why did this have such a powerful effect? Luhmann just recorded “which thoughts come to my mind and in what context”. So, the wider context enabled serendipitous inspirations. But it is very difficult to understand the effects of the small nuances of note-taking techniques if they are only discussed and not practically tried out. This is why my tool offers many import and export options — such that you can play with different systems and understand their differences.

Update 2017-06-14:

See the blue/ green cluster in the diagram: It turned the red/ yellow tree structure into a structure that was no longer purely hierarchical. zettelkasten-schemaFrom the orange ‘Foo 3.2’, a new idea “came to mind”: the green ‘Bar 1’, and from there a larger substructure emerged.

Posted in Knowledge management | Tagged | 3 Comments

New page: Recognizing

The new page is a remix of all my blog posts about McGilchrist’s (@divided_brain) and @Downes’ ideas on recognizing: https://x28newblog.wordpress.com/recognizing-2/

Posted in Uncategorized | 4 Comments

Sequential requirements

Does a neural network need domain knowledge? Stephen Downes shows how important this question is, so I’ll try my guess.

“if we require domain knowledge in order to learn, then we require memorization”

According to the traditional learning theories, we construct knowledge upon existing knowledge, for example by linking the new stuff into the known. When we see knowledge as representation, all the higher order concepts are nicely grown from lower level concepts. But at the very bottom there must be some special knowledge, sort of “seed” knowledge, to enable this recursive mechanism because otherweise we would run into an infinite regression.

Even if we use the less abstract idea of “recognition” we might misunderstand it and ask: how can we recognize what we do not already know? The trick is that recognition needs only a part of the features of a pattern to see the whole pattern.

The artificial neuronal networks described in the above post, apply such pattern recognition to learn solely from conversations. How far can this approach be extended? To the bottom, to eliminate the need for domain knowledge?

Let’s first look at human neuronal networks. The conversations begin long before the human can talk, when the baby has their first gaze “conversations” with their mother, at the age of just a few weeks. It is here that they recognize the world around them, long before they learn propositions about “he, she, it, they”, and even the “I” is learned only via the “thou” in these first bodily conversations.

(If it seems far-fetched to compare this kind of conversations with the sophisticated concepts of a given knowledge domain, consider how language covers a long scale of words: While it ends with isolated concepts coded in specialized, domain-specific terminology, it starts with basic ideas that suggest a very bodily context, and a full-senses/ all-at-once recognition, such as the deictic notions of “I”, “now”, and “here”, or other simple spatial or temporal descriptions which are gradually extended via synaesthetic metaphors, or with the modal words that extend from “wanting” to deontic use to epistemic use.)

Similarly, how do neuronal networks learn how to find out which response should be trusted? For the little human neuronetworks, the seed trust is given before they need to start reasoning, and it will enable them to add more trust criteria over time.

Artificial neuronal networks, by contrast, should always depend on some human who decides if their response is correct, or at least if their underlying algorithm is acceptable. So, there are limitations of what they can learn on their own. (This may be a consolation, but still it is unsettling how few humans might be controlling them.)

So, I think, while artificial networks need some prerequisite input, human neuronal networks use recognizing from the very beginning and require no indispensable prerequisites.


This sheds different light on the idea of prerequisites, and it also makes the idea of a “course” much different from the traditional temporal sequential arrangement. If I encounter prerequisites later, there is still time to cover them (like a rhizome is not necessarily pulled out from the top to the root but often in the reverse direction), as long as the course does not build the stuff of later weeks upon the stuff of previous weeks but uses the weeks’ skeleton only as a schedule of when people can simultaneously discuss a certain topic (i.e., if it is organized as a cMOOC rather than an xMOOC), or even lets the community “be” the curriculum.

This is the first, obvious meaning of

“how education may be linear, but learning certainly isn’t.”

(which Doug Belshaw spends “five minutes explaining”). Recognition explains the deeper mechanism of learning as not linear/ sequential (not via fixed isolated representations) but as laminar/ all-at-once (multiple connected features of a pattern). Thanks so much Stephen Downes for the idea of recognition.

The image above shows the logical dependencies of the chapters of an important textbook of my study (B. L. v. d. Waerden, Algebra I, 8th ed., 1971) which I encountered again as an illustration of linearity in the first German book on Hypertext by R. Kuhlen, 1991.

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Tangible Associations

After experimenting with other think tools, I understand better how the “magic” of my tool works: it makes associations tangible. It turns elusive mental relationships into “hands on” experience, and it compensates for the abstractness of some thought links, with a drawn line “at our fingertips”.


Many tools aim at a similar effect with their representation of our ideas, concepts, notes, text snippets and thought fragments: Each single idea is reified by one individual virtual index card on a cork board, or by a yellow rectangle that evokes the impression of a post-it which can be grasped and moved. One thought, one object.

The haptic impression caters to our worldly nature, and it mitigates the alienating virtual constructs that plague our everyday life as knowledge workers. And there are many occasions where people even insist upon the genuinely haptic tools such as their paper notebook, their fountain pen, or the brainstorming with paper post-its. I understand this preference even better since I can use a pen again on my Surface tablet, for handwriting and drawing, and to substitute both the mouse and (mostly) the keyboard.

The haptic representation of individual ideas is a useful thing, and Heiko’s iMapping (see my last post) does this better than any other tool I know. The items feel like “on your fingertips”.

But what about the links between the items? A complex network consists of nodes and relationships. Since I have encountered DeepaMehta many years ago, I have learned that these relationships can be recorded and manipulated in a similarly tangible way.

Now I tried this in an even more manifest way: Draw the lines with the pen. It is not as optimal as with the mouse (since I cannot use the third button and not even the Alt key), but it is excitingly “haptic”.

A similar tradeoff must be made when it comes to prioritizing nodes versus links. In Heiko’s tool, the notes feel a bit more tangible than my little circles which are 1 click and an eye saccade movement away from their detail. But in return, my links feel easier, and closer to the thoughts they are connecting, because the map is optimized for conserving space, since it is not intended as a long-term store (of items) but as an ad-hoc overview where you drop various text snippets to connect them. Because, you know, connectivism discovered that connections are more interesting than mere items.

Just try it out.

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Im-/Export from/to other think tools

Finally, my tool is able to export to, and import from, two other great think tools, iMapping.info and DenkWerkZeug.org.

The work allowed me to thoroughly compare the various needs and approaches, and I would encourage everyone interested in highly effective tools to explore these difference, too.

To demonstrate them, I chose a very simple example of a small family and their neighbor. iMapping focusses on the texts that are hierarchically arranged, but allows for arbitrary cross connections.


DenkWerkZeug, by contrast, focusses on the relationships and their own big hierarchy, such that the reasoning engine can draw conclusions.


Comparing them with my own tool one can say that they are rather suited as the big long-term storage “cupboard” while my own tool is more like the “table” where things are put for a temporary large overview.


Sometimes it is necessary to pull together many items from multiple sources. (See below a metaphorical example of this diversity: if the two maps are combined, the nephew finds more neighbors with daugthers.)


I realized that, for such big stores, I need some hierarchical relations such that I can browse through them. And I need browsing because I have always been bad at searching.

If you want to try out the two tools without typing in your data, you may drag them into my tool, and export them into the two other tools that currently don’t have an import function. Enjoy.

For more details on the design decisions of my import/ export, see my documentation on Github. Disclosure: I know the authors of both tools since a long time.

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I read this great book in February and I am not surprised that its ideas are now widely spreading.

Jenny Mackness draws the connections to McGilchrist’s work, and I want to add to this.

Usually, the styles spectrum is depicted as “linear” vs. wholistic, which sounds rather value-laden. By contrast, Sousanis’ notions of sequential vs. simultaneous are much more conducive to the necessary awareness about the two fundamental modes of thinking.

“This spatial interplay of sequential and simultaneous — imbues comics with a dual nature — both tree-like, hierarchical, and rhizomatic, interwoven in a single form.” (p. 83)

I like the distinction that nicely adds to the dichotomies of tree vs. network, or nodes vs. edges (as in Deuleuze & Guattari, and in Connectivism, respectively, see this old post).

For me, the gem was that the book does a great job explaining why the right hemisphere mode (all-at-once) lives from relations: Basically, it argues that the eye is “dancing and darting”, i.e. by its saccadic motion (palpation by means of the gaze) it captures only small fragments at a time, and it is our imagination that needs to combine them into vision. It quotes R. Arnheim “To see is to see in relation.” Other explanations draw on rather optical phenomena, like the distance of our eyes, and the refraction at the contact of two media, that yield different but related images. In particular, they remind me of the binocularity that enables an owl to recognize in the darkness what a single eye would never alone identify.

Then, in this week’s #gridsgestures exercises, I learned how much I struggle with the sequential. When the assignment was to sketch the shape of my day, my first attempt was to draw the day vertically upwards, like a carpet lying in front of me, not as a real grid. And I completely missed (repressed?) the part of the task about “gestural lines, marks of some sort that […] represent […] activity”, because I have no idea how to depict gestures and movement. (Maybe there will be some examples of elements that could serve as a sort of the ‘alphabet’ that Dave Gray often shows?)

The restriction were, not to draw things, and to use a pencil or pen, i.e. monochrome, and like many participants I gradually let go of some restrictions, but I still struggle with depicting my imagination — which seems to be just too static. I also have problems to interprete the drawings of others, much like there are problems to understand the mindmaps or concept maps of someone else. For me, the most benefit is not in the communication or the drawing result, but in the making of the drawing — and the sense-making along the process.

PS Don’t miss Jenny’s great image image and Howard Rheingold’s interview with Sousanis.


Posted in Visualization | 4 Comments