#EL30 Temporary: Copy of the Week 8 Synopsis

There seem to be errors from the central site, but in our decentralized environment this should not mean that we had to wait for the problem to be fixed; here is my older copy of Stephen Downes’s text.

“It is a truism that we learn from experience, and yet creating a role for experience in learning has been one of the most difficult problems in education. And so much of education continues to rely on indirect methods depending on knowledge transfer – reading, lectures, videos – rather than hands-on practice and knowledge creation.

The emergence of the web, YouTube, Web 2.0 and social media was a great step forward, assigning a role for creativity in the learning experience. But experience, ultimately, requires an openness that media platforms were unable to provide.

New technology is beginning to combine the ability of teachers and role models to model and demonstrate successful practice and the need for learners to practice and reflect on their learning in that environment. Content distribution networks and live streaming are transforming real-world events into hands-on learning experiences.

A good example of this is the live-streaming platform Twitch and especially games like Fortnight, in which players become spectators, and back again, over and over. And using applications like xSplit or Open Broadcaster Software individuals can make their experiences part of the learning experience shared by others.

It is a model in which the creation of the content becomes a part of the content itself. We see this with the recent self-shredding art by Banksy or the inside look at how the single-scene time-lapse sequence in Kidding was filmed. Some artists have made working openly part of the act – Deadmau5, for example, showing how electronic music is produced. Being able to see and experience how something is created is a key step on the way to becoming a creator oneself, and becoming a creator, in turn, becomes a key part of the learning experience.

The difference between previous iterations of learning technology and that which we are experiencing with E-Learning 3.0 is that these creative activities become distributed and democratized. Just as multiple authors can edit Wikipedia articles or work on code in GitHub, participatory learning media enables learners to interact creatively without management or direction; the outcome is a consensus determined not by voting but by participation. Experience in learning changes the relation between teacher and student from one of persuasion (and even coercion) to one of creativity, co-work, and construction.

Workplaces, and especially distributed workplaces, are beginning to create self-organizing consensus-based co-production networks. Early awkward and exploitative platform-based efforts such as Uber and Airbnb are giving way to more sophisticated and equitable network alternatives such as Steam, Koumbit and Medium.”

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#EL30 Week 8: The invisible

This week is about experience, and the synopsis argues for ‘practice’ rather than ‘indirect methods’. Critics might object that the ‘indirectness’ is nobler than jumping at stuff that is immediately palpable, the abstract is more ‘noble’ than the concrete, theory more than practice, and ‘Bildung’ (Humboldt’s ideal) more than ‘Ausbildung’ (training), because these detours foster the capability known as transfer of learning from one problem to another one that may arise in an unknown future.

Rather than dismissing these criticisms altogether, I was musing as follows. What is so valuable in the indirect and abstract? It can’t be just that it seems more difficult? Why is it more difficult? Often the indirect and abstract is powerful but difficult to understand because it involves something invisible.

In IT, for example, the power of indirection is obvious: instead of writing a program for directly adding 2 + 3, I write a program for the variables x + y and then fill in whatever values I want. However, my values become invisible. Difficult in a similar way, is also the concept of the Clipboard on a desktop computer, or the cookies, or ‘modes’ such as Overtype or Insert. In programming, it is particularly difficult to imagine all the abstract data structures hidden somewhere down there. One needs some imagination to cope with the invisible.

The clipboard icon as it appears on the button for 'paste', and next to it the icons for 'cut' (scissors) and 'copy' (two sheets).

It becomes much easier if some part of the program is already running with real data (e.g. in a Jupyter notebook), or when we seem to manipulate palpable objects, when have come to grips with them. It resonated very much with me when Stephen, after taming the badge API, wrote “now we have the mechanism and the vocabulary”. Similarly, it is easier to watch pictures about a culture that is geographically or temporally very distant, than to imagine it via historical or travel reports.

Is it too easy for developing our imaginative skills? The trick is when “the creation of the content becomes a part of the content itself” (from the synopsis).

Because, before the creation, things were invisible, too, and had to be imagined.

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#EL30 Week 7: This class

This week’s assignment was to reach consensus on a task, and here is an attempt to do the resultant task. (I am aware that the consensus is still pending and I am prepared to do another attempt if the consensus will finally be different.) The task, as I understand it now, is to write about my experience of this course, and to take into account the aspect of community.

1. After some less satisfying MOOCs, this is once again a cMOOC as I love it — with much more asynchronous, reflective components than rapid-fire synchronous elements. In particular, with more blogging than chatting in Facebook-style or in live sessions. And the input is very high quality and very well introduced.

Almost exactly 10 years after CCK08, it suggests itself to compare it to that mother of all MOOCs. This time the topic is more special and difficult, so I still have to pick or skip the pieces of content (thereby practicing again this critical literacy of autonomously navigating the abundance), but this time the audience is a bit smaller and so fortunately I do not have to skip among participants’ contributions but can read them all.

2. My rough estimate is that approximately one third of the visible participants (i.e. those posting under the course tag) have written not more than 2 posts yet, so this is IMHO a healthy indication of diverse people dipping in and out as they want. This large variance, to me, also effects that I don’t have the impression of a community in the sense of a group with a common goal, like an activism group or one of enthusiasts, or anything like companionship, comradeship, confraternity, communion, parish/ fold/ congregation, or club. At most, I would compare it to the residence or municipality community which is defined by something like a common zip code (here, by using the hashcode el30), and whose residents have, in a certain limited sense, a common ‘fate‘ (again limited, to the 9 weeks).

Of course, the more frequent exchanges and deeper discussions among some who blog or comment more often, may create a feeling of resonance, of stronger ties, and perhaps the onset of some trust. But there is no border that could be drawn between active and inactive ‘members’ — it is a network, with liquid boundaries, where it is hard to guess if a person would denote themselves as ‘belonging to’ the ‘community’.

So, the total constituency/ population of those eligible for this week’s consensus, feels rather loose to me, and it is an intriguing game to artificially simulate a common ‘fate’ for us, for just a few days, by this brief to achieve a consensus. And it is interesting to think about what if this was really a matter of fate that would require trust among strangers.

3. But back to the course itself. What was the unique outstanding feature for me, was a certain duality in almost all weeks:

  • There was a technical sense of the weekly concept,
  • and there were far-reaching aspects far above that technical stuff.

For example, ‘identity’: just as owner of a private key, and as a whole person. Or ‘recognition’: as plaudits such as mundane little badges, and as central idea of the knowledge creation. This combination of two seemingly distinct layers, has been an extremely inspiring food for thought. And in a private backchannel exchange, a friend pointed out that this duality is similar to the big difficulty of understanding connectivism: that there is a lower layer of neurons, and an upper layer of people, which are seemingly very distinct things.

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#EL30 Week 7: Response to proposals

Thanks to Laura for the proposal which makes the consensus task stretching again, whereas Roland’s good proposal might have been just nodded through.

I think that a shared definition of such a complex term as ‘community’ is unnecessary and therefore impossible to settle on:

In a previous blog post I depicted many possible meanings of the term, see this image

(including some shared German synonyms). Much clearer than ‘community’ — at least in the context of people familiar with Downes’s writings — are the terms ‘group’ and ‘network’, where a group has a shared goal, and a network is not delimited by some borders. ‘Community’, IMHO, is a concept whose scope is rather fuzzily distributed between several such other terms (one might say, its ‘betweenness’ between the neighboring concepts is key, or its meaning is literally in the connections between them).

In particular, like a network, it does not have a predefined, fixed boundary (except in the special word sense of a geographical municipality) but its ‘membership’ is voluntary, as the blonde boy in the middle of Kevin’s bottom cartoon emphasises. And so, unlike the shared goal of a group, a possible shared goal of a community depends on a consensus. That’s why consensus building is a fairly typical task for a community — while of course I agree with Jenny that community can be much more than consensus, let alone consensus about some truth, or even about a technical status.

In an informal community, ‘membership’ may just be defined by each individual themselves, feeling as members or not, or maybe just as participants, for example in a MOOC with variable activity, with dropping in an out and perhaps lurking. If the community is not formally used for anything else, the consensus may even delimit the community: while it is desirable to achieve a broad consensus (of as many participants as possible), it needs to constrain to a minimally necessary level, or a least common denominator, to avoid that some participants may stop to feel, and self-declare, as members. And I was very reluctant to act as a community member, and my tolerance level is rather low.

I think Laura’s task is less ‘minimal’ than Roland’s, if it aims at a definition of a community in general. Maybe this community is easier to define, or the ad-hoc community whose purpose is just to complete the task.

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#EL30 Week 6: Automated Assessments

In the center of this week’s topic is the conjecture that the rich data tapestry of student learning records, might get at a more accurate picture of whether the student’s abilities will meet the requirements of a certain job profile. The many data points might lead to a sort of ‘recognition’ of connecting patterns that is more appropriate to mental competencies than a few quantitative ‘measures’ and scores. Because knowledge, too, is such a recognition.

In principle, I find this conjecture plausible. And especially the corollary which links it to the distributed web:

“In the world of centralized platforms, such data collection would be risky and intrusive” (from this week’s synopsis).

But is the conjecture true for all types of assessments, and will it lead to more justice, and should we embrace machine decisions here?


By Flickr user tudedude, CC-BY-NC-SA

For existing jobs it might perfectly work. But if the decision impacts 40 years of work life, I doubt that the criteria of future needs can already be sufficiently formalized. The training stage of the AI cannot be extended to 40 years. In particular, domain-specific aspects will not suffice, and the need for domain-general literacies is even more important, to be able to abstract from today’s situation and to transfer one’s knowledge to unkonwn futures. (And it is not a good idea to just increase the abstraction level of the subject matter to be learned.) So the criteria will be rather vague here.

Will automatic assessments be more objective, and will they distribute the scarce, best-paid, positions more fairly? If the higher salary is excused with the scarcity of the necessary skills, there will always be some unspoken, or maybe even unconscious, motivation to keep that skill scarce, rather than foster its development. So, designing vague criteria for this critical selection is not straightforward. In particular, if the fitting judgement is not just a matter of ‘sufficient’ skill (like, a professional ‘recognizes’ their new peer), but ranking, often composed from scores that are totally irrelevant but are just available from several years of accumulated assessments.

Algorithmic decisions are tempting because they also work with imperfect criteria, just looking at previous decisions. But they might not have a response when we ask them how they arrived at their decision, as Stephen and Viplav observeed in their Wednesday discussion. This is a severe violation of a demand that is emerging from the political discussions, for example by algorules (which I mentioned before), namely transparency.

I think, for the final summative assessments deciding about the future life of a human, such algorithms are not acceptable. By contrast, for the formative assessments throughout the study, they might be perfect. With human teachers, both types of assessments are equally costly, therefore we have too few of the latter and too many of the former. This may hopefully change now. And that’s why distributed storage is needed.

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#EL30 Two tasks

This week’s task is to install IPFS

A screenshot of a command prompt window, showing a nostalgic style banner of IPFS. bannerand to create a content addressed resource. Here it is:


This week’s topic is resources, and this fits well to Jenny’s task for us, about Jupyter.

There are so many discussions and efforts about the logistical and legal frameworks of educational resources and the technological changes of these frameworks. So one might be frustrated that there is so little about new technological affordances of the resources themselves. Dominant theory, for example “Split Attention Effect” (from Cognitive Load Theory) is still mainly drawing upon the paper age where interactive resources were unknown. So it is refreshing that this course covered an interactive resource called Jupyter notebooks.

And Jenny’s task wants us to

“Explain your understanding of the Jupyter Notebook for four different people, none of whom have heard of Jupyter Notebooks before:

  • A 10 year old child
  • A 15 year old secondary school pupil
  • An undergraduate trainee teacher, specialising in Art
  • A University Lecturer working in the Educational Research Department”

So here is my attempt. (Disclaimer: I am not an educator!)

10 year old: Suppose you have a new cooking robot. And you have alien ingredients that you have never tasted before, and you don’t know any recipe about them. The robot understands written orders and it will quickly execute each step for you. If the result is not tasty, you can start over and modify your orders; the robot will patiently execute each step again if you tell it to do so. A Jupyter notebook contains all these orders, and a “Run” button for each step. Just too sad that the result is only on the screen and not edible.

15 year old: Same as above, but with data for a diagram instead of ingredients for a meal. If the pupil’s basic IT training has already covered databases (? I hope so), the ingredients are ‘JOINed’ rather than just mixed, and the orders to be modified will particularly be about ‘parameters’ to be varied.

The arts teacher-to-be: Same as above, but with a sculpture instead of a meal. And with additional explaining: Why does the robot have a command line interface, rather than visual user controls where I immediately see the effects (‘what you see is what you get’)? Well, some people want and prefer this linear style, and it is important for you to also understand those of your pupils who might not have chosen arts as elective subject. Furthermore, graphical interfaces don’t yet lend themselves well to such ‘scripting’. And finally, machine learning works very similarly, by such parameters that you have to tweak.

The educational researcher: Same as above, but perhaps more help is needed to overcome their traditional understanding about tweaking and fiddling: While such practical, quantitative bricolage may seem much less noble than theory, facts, and abstractions, the latter may soon fall prey to cognitive automation, and a skill of the former is also desirable, to cope with unknown futures.


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#EL30 Week 4 Identity graph, 1st attempt

This week’s task is an Identity Graph. So I tried to guess what advertisers know about me.

1. The first source is the Twitter “Interests” which can be obtained via Settings > Your Twitter data > Interests and ads data. I depicted them in blue, and added guesses why they may have been added — click the yellow icons to read more on the right hand side. I arranged and linked the items according to wild associations — feel free to rearrange, it is just a copy in your browser.


Click for interactive version

Updated version here

2. The second source is my LibraryThing tags (red), because I think my clicking behaviour will roughly match these interests, in particular at Amazon where I browse for books before I order them online with a physical bookstore. (I find this useful because the index Stephen mentioned is ‘delegated’ from the libraries and bookstores to this platform.)

I did not fully understand the stipulation of not containing a root node “me” which I thought commercial personas are all about? but I’ll learn this by trying. (Updated 22:17 h: After Stephen’s explanation in the Wednesday live session, I understood it and added connections between the interests, in green.) Please comment what I missed. (If you want to arrange your own lists: I just dragged and dropped the marked text onto the canvas.)

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#EL30 Week 4: The same

Digital identity is a big opportunity to confuse users — and to lure them into friendly services who ‘take care of’ all this impenetrable stuff. In particular, the term refers to two very different meanings.

1. Etymology offers a good inspiration for thinking about one of them: the same (Latin idem). If I am a user who has never logged in into some server, its ‘cookie’ just tells the server that I am the same one who visited the site before. Nobody knows my name at this time (and not even that I am a dog, as Peter Steiner’s cartoon of July 5th, 1993, said). And this is sufficient for many useful things.

2. Only when it comes to binding this digital handle to some real-world attributes — such as my name that my father registered in the registrar’s office of my birth village — it becomes complicated. A password ties a hash value on a server to some content stored only in my brain. And a ‘public key’ is tied to the ‘private key’ (a very long password) stored on a device that only I own.

The handling of all this is still so confusing that friendly platforms and browsers invented many methods to ease and accelerate it for the users — and patronize us ever more.

The VCard icon.

So when we want to get rid of the central abusive platforms we must make sure to also get rid of the danger of confusion and new friendly patronizers, to not ‘jump from the frying pan into the fire’.

The technical W3C draft tells me that we are not there yet:

‘Zooko’s Triangle: “human-meaningful, decentralized, secure —- pick any two”.’

Of course, they picked long incomprehensible strings, but

‘mapping human-friendly identifiers to DIDs (…) is out-of-scope for this specification.’

This potential source of new confusion seems yet unsettled.


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#EL30 Technical: How I created my task

My task uses tables, views and a page on gRSShopper. If you want to try it yourself, here is my stuff: http://x28.privat.t-online.de/372/x28stuff.sql

  • go to cPanel > Databases > phpMyAdmin,
  • click your gRSShopper database
  • click Import, browse to the downloaded .sql file, scroll down and click OK. It should replace the tables x28term and x28week, and add the views x28term_html, x28week_html, and a new page. I prefixed everything with ‘x28’ to not get messed with your own stuff. Of course I tested it but still, I don’t fluently speak SQL and hope I won’t break anything. Proceed at your own risk!
  • go to the new page called “an x28map starter file” and publish it.
  • go to the new view “x28week_html” and change both occurrences of mmelcher.org to your own domain (because Javascript won’t load “cross origins”).

Unfortunately, it does not work with https (since the location of the javascript is on http). So, if you have a forced redirect to https in your .htaccess (which happened to me recently!), feel free to copy both the Javascript and CSS file to your own site. (But then don’t forget to watch my Github for updates.)

Hammer, screwdriver and a cog

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#EL30 Graph task

Task 2 in the Oct 30 newsletter said we should create a task for other participants of this course.

My task is related to week 3 about Graphs. While Frank Polster observed that for some graphs it might be a stretch to think of knowledge, the following example should make this easier: “think of knowledge as a graph”:

It involves some concepts from the synopsis texts, and you should connect and annotate them.

1. Click on a selected week from this list -1 Getting Ready, 0 E-Learning 1 and 2, 1 Data, 2 Cloud, 3 Graph, 4 Identity, 5 Resources, 6 Recognition, 7 Community, 8 Experience, 9 Agency, and you will find a list of terms.

2. Copy and paste them into a .txt file, and import them into a concept map application of your choice.

  • for Cmaptools by cmap.ihmc.us, I made a video instruction on Youtube some time ago;
  • or, for my own tool http://condensr.de/download-page/ you may just drag and drop the text into the map window that opens when you start the application.
    • or, you may just copy the text and paste it (Edit menu > Paste),
    • or, you may drop the icon of the .txt file from your file explorer into the map window;

    If you want help, don’t hesitate to call me directly!

  • For limited functionality, you may also use the demo version which should open when you click on “Preview” above the terms list.

3. Connect and annotate the terms.

The terms have been extracted from Stephen’s synopsis texts by a corpus linguistics tool called AntConc, and were loaded on my gRSShopper instance. If you want to view a sample of a map that I quickly completed for week -1, click this link: http://x28hd.de/demo/?el30sample.xml

Screenshot of a concept map


I did some annotation by inserting extra (red) items. In the full version of my tool, you can put annotations into the right pane (which is the biggest benefit of this tool).

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