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.

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New page: Recognizing

The new page is a remix of all my blog posts about McGilchrist’s (@divided_brain) and @Downes’ ideas on recognizing:

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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, and

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.


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Wish list

There was the question of what @downes and @gsiemens can do working together, and I don’t want to miss this opportunity for an early Christmas wish list 🙂

Over the years, both thinkers have emphasized different important things but without explicitly disagreeing with each other:

  • Siemens still points to the conceptual level of Connectivism, creating coherence, and sensemaking;
  • Downes has deep thoughts about how human recognition actually works;
  • Siemens has ideas of how technology can support knowledge as an “outboard brain”, not just as logistics (storage and communication of information), and also about a learning analytics that is not patronizing the learner towards prescribed outcomes but keeps a human face;
  • Downes emphasizes assessment as human recognition, and independent/ autonomous navigation within the subject matter rather than memorizing it.

What is needed is a learning solution that leverages all of the above aspects.

  • A demonstration of how human recognition works differently than the AI competitor who is catching up rapidly.
  • An illustration of how learning works when there is no predefined true or false outcome but a real understanding of complex conceptual networks is needed.
  • A demonstration of how connectivist principles apply to concrete subject matter from sample knowledge domains,
  • … to drill down to which structures lend themselves to non-linear, networked coverage,
  • … and to study which kinds of learner preferences influence their interaction with the sample subject matter.

Personally, I am particularly curious about how the “outboard brain” can help to offload parts of the emerging conceptual network, to free precious working memory (of course because I work on a think tool). Or what non-patronizing analytics will find out about different learner and teacher bias towards speed and (a)synchronicity. But there is much more in the field of machine-supported human recognition.

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#NRC01PL Choice and Agency

In today’s hangout, both fathers of Connectivism were unanimous.

“There is automation that enables choice & human agency & that which doesnt” (@gsiemens, see @Autumm’s tweet)

It is encouraging that they are on the same side when it comes to the important issues of educational technology.

It sounds simple but it is really very important, and it is easy to forget it because it is easy to be too much enthused about technology. For me, the issue of empowerment vs. patronization has been a central one, see my blog post “Between Empowerment and Patronization: 40 Years IT“.

Of course, choice in IT may also be intimidating, when the user interface is stupid and the help texts mindlessly reiterate nonsense like “select the desired option” — because the programmers were too lazy to think enough about the options themselves.


And some users are easily contented with perceived control. A dashboard — doesn’t this sound great? Indeed I picture my ideal PLE as a dashboard, and start ramp into my PLN.


But openedX’s dashboard means the point where I could navigate to my various edX classes. For me, it is only always an annoying obstacle between the start page and my only one course, NRC01PL. (Besides this, it is annoying during the startup how quickly the “remember me” expires, and that they don’t tolerate a space after my email address, as it is copied after doubleclicking.)

So I think it is important to distinguish between true control and disguised patronization. To learn this distinction, may be even more important than crap detection (if I dare to say this on an April 1st ?). Skills like such distinctions are not acquired through memorization but often through navigating diverse spaces. So Stephen’s formula of March 18 is spot on:

“It’s not that there is nothing to learn, it’s that it’s complex and needs to be navigated… not memorized”

Navigating through choices, not via pre-programmed walkthroughs.

But I should add that I understand the choice to select edX at this stage: because it speaks some LTI language which is needed for LPSS’ interfaces. At the threshold between an old type system and a new type paradigm, I have often met such solutions of gateways, proxies and encapsulations that mitigate the legaccy system.



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#NRC01PL Minimal PLE ingredients

This week’s topic in #NRC01PL is PLEs. A cMOOC like NRC01PL is always inspiring to think about something (even if it is seemingly starting slow, which I attribute to the terrible edX discussions platform). This time, the question for me is:

What does my PLE need as a minumum?


I quickly found the answer that, for me, it is not sufficient to have discussions with posts and comments, but I need to see people behind the comments, and this means that I like to look at their profiles and click the URLs leading to their blogs or other homepages. If there is no permanent address like a blog but only a stream like Twitter, Facebook or Google plus, it is less attractive for me since my preference is not the rapid flow but the more aynchronous affordance of slower conversations.

But on the edX platform, I could not simply click on a name to see the links leading to more about the person. This is a big disappointment for me, as I know since we thought about the relationship between personal and conceptual connections in a previous cMOOC.

It is often emphasized that cMOOCs thrive through content artefacts created by the participants. Should I dig out my old PLE picture (which I find great, of course) and the related blog post of some previous cMOOC? If I were a fellow participant, I would not like old content to be thrown at me and pushed to me. Rather, I like to pull and pick it myself. And this needs the link to the persons of the course.

The discussions in the forum alone don’t suffice, let alone discussions of the stale edX format. Even on Moodle, the forums are much richer and more flexible — the first thread of CCK08 is a real firework compared to an edX forum.

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