Stephen Downes and George Siemens are giving a MOOC (Massive Open Online Course) on Connectivism & Connective Knowledge between September and November 2008. Exploring emerging topics in knowledge, learning and technology, It’s going to be held online and will utilize latest Web 2.0 technologies and distributed approaches. What is more important, is that if someone is going to pull this off, it’s George and Stephen, both the most visionary and knowledgeable teachers I’ve ever had online.

If you are interested in this stuff – perhaps think of yourself as an expert, an enthusiastic pro-amateur or a newbie – this is the course you want to take this year, if you are going to participate in any. The course already has at least 1200 registered participants from all over the world and it’s free. If you want to get credits, you can enroll at the University of Manitoba, but this is not a requirement for participation.

I will be joining, hope you will too.

The conference blog
The conference wiki (and enrolling)

• Week 1: (September 7-13) What is Connectivism?
• Week 2: (September 14-20) Rethinking epistemology: Connective knowledge
• Week 3: (September 21-27) Properties of Networks
• Week 4: (September 28-October 4) History of networked learning
• Week 5: (October 5-11) Connectives and Collectives: Distinctions between networks and groups
• Week 6: (October 12-18) Complexity, Chaos and Research
• Week 7: (October 18-25) Instructional design and connectivism
• Week 8: (October 26-November 1) Power, control, validity, and authority in distributed environments
• Week 9: (November 2-8) What becomes of the teacher? New roles for educators
• Week 10: (November 9-15) Openness: social change and future directions
• Week 11: (November 16-22) Systemic change: How do institutions respond?
• Week 12: (November 23-29) The Future of Connectivism

Recently I’ve been quite busy with various business ideas and as we know, time, focus and energy are scarce resources and I would never have the ability to implement all of them successfully. therefore, I just share with you one of them that might have some potential and value to turn into a viable business.

The internet search is pretty much about searching text. We know that many of the algorithms we have created are mainly successful for content that is processed in the linguistic domain. Therefore, many applications responsible for searching images or sound for example, usually rely on textual interpretations of the media resource in question: what textual content is present on the same page, what textual content is used in linking to such a resource, what textual content is extracted from the visual images or sound by humans or computers (like geolocation, where a certain picture was taken, tags etc). This is the easy approach.

Now, I know there are a few projects that try to outsource the task of describing images and audio in the textual form to their users. BBC for example, has a project where they put their radio programs online and let people annotate the audio with descriptions of various segments in the audio, to enable much more accurate navigation and search among the radio programs online. A similar navigational experience can be seen on the TED conference site for videos: in the video player, much of the talks are split into seekable chunks with certain textual ques on what a certain part of the speech might include. This trendemously improves the viewing experience.

There are similar projects for images, where people use tags or other ways to describe what they see. Flickr in a sense guides people to do this for themselves in a less structured way to gain insight in the relationship among vast ammounts of images they have or other people might have on a similar context.

If you think of the Flickr or BBC example, much of the analysis is left for the viewer who perceives the content. I believe there is a cognitive threshold for participating in such annotation activity, unless you get some machine guidance that steers your attention to certain manageable chunks. I also imagine that having computers doing such annotation completely automatically is a really hard computational problem and still out of our reach. Therefore, we have to join machines and people in a symbiosis that can solve the problem much more quicker and with lesser threshold than what one or another can do simply alone.

Let’s take an audio podcast, where you have let’s say 3 people discussing a certain topic. It’s 90 minutes long. You listen through it. It’s incredibly hard to find the exact spot where someone was saying something interesting, that you would like to refer to afterwards. You might remember who said it and in what context, but you have no idea of time, where it actually happened. As a result, seeking to the exact spot is hard and linking to it is even harder (as far as I know, there is only a few services, where you can link to an exact spot in music or video streams).

Now, imagine a computer program that can identify from the audio levels, patterns and phase where certain boundaries might reside. The result would be to split the stream into blocks. You would end up with blocks of different people speaking, but the computer doesn’t have much of a chance to identify who was actually speaking at what parts. Such algorithms already exists, see for example here or here. You would combine this data with audience response on who is speaking at certain points of time.

Then, you would use speech-to-text analysis to get the transcript. This would of course include a lot of errors and be inaccurate in its ability to crystallize what is going on at certain parts, therefore you would algorithmically extract most salient themes and topics for each part as keywords that might be able to describe the content. Afterwards you would ask people to use those keywords to recall what were the main topics there. I would suppose this would give one enough cognitive support to recall what was going on at certain parts.

In conclusion, you would use a computer to set the boundaries for podcasts and extract the keywords (all tedious work for humans), and use humans to finally correctly describe the content (all tedious work for computers). This could be done for video and images too, with alternative strategies.

So, what’s the business idea? You take this approach and you turn it into a service that other service providers can tap into to annotate their content with increased precision and reduced threshold for contribution. It could be network-based and include APIs, so that other service providers can use it to make the most out of their content in cooperation with their users. I would guess this approach would make it so simple for the viewer/listener to annotate the content that it would double the participation and quality of results for such activity in the bottom line. The service would provide open standards to describe content in such a way, that is usable in other contexts (let’s say, your music player). It would behave like CDDB does for music track listings based on checksums, but this time for their exact contents.

Eventually, you would make video and audio increasingly searchable and useful. It combines computers with humans to make the job easier for both.

Steal this startup idea, or let me know if it already exists. I believe it would be really useful.

«Who the hell wants to hear actors talk?»
H. M. Warner, co-founder of Warner Brothers, 1927.

I like to state, that the future of education is the history of education. Bruce Sterling writes in his visionary book entitled Shaping Things, that “the future composts today“. When looking at the models of the current, you will see them in a mutated form in the future. Therefore, much of change is incremental in the core. What is hard, is to articulate what the direction would be. Studying the future is the study of multiple futures. What we can do for a certain, is to shape the future through our actions today.

Researching the history of the future is the same as researching the history of the past: you can only interpret it by looking at and sensing the signs you find in the current. Researcher of the future extrapolates weak or strong signals of today. Researcher of the past goes through archives and ruins today. In other words, both are studied by researching the now.

When formulating any meaningful paths of the future of education, we have to research the now, understand the past and see what patterns might recur in the future. George Siemens does a very good job with this in his talk about a World Without Courses. Got the link from Eric Davidove, check it out.

etete

• Drop the bullet points to the notes section. If you know your stuff, you don’t need them. If you really must, split the slide so that you have a slide for each bullet point


• Think of a metaphor that could illustrate your point to draw the picture in the mind of your audience just as it is (think Jesus, much of his success is based on inspiring metaphors)


• If you cannot come up with a metaphor, use a simple photo to illustrate your point (see istockphoto.com for examples)


• If you need to add words, use a 2-5 words or use a short quote, if possible


• Never position text over a detailed part of your image, because it interferes with the background and reduces readability. If you need, edit the photo by reducing detail on certain parts with fades or blur, or cut it into pieces


• Use high quality photos. If the photo is a bit dull, use the built-in features to cheer it up by adding contrast or fiddling with gamma and brightness settings


• Remove all slide numbers, corporate branding, visually unnecessary elements and links. You can have those on their individual slides (e.g. on the beginning and end), but not on every slide


• If you really need motion, add slide animation that makes sense and supports the image (say, if you have a picture of a book, use a page flip transition)


• Make sure all elements are lined up symmetrically to slide borders or other considerable boundaries


• If your presentation has some identifiable major sections, you might want to use some slides to identify change of phase. Use similar style on each that stands out of the rest


• Use the largest font size you can afford with a readable font (arial, verdana, gill sans…)


• Use font color that sticks out of the background. With dark backgrounds use white or a very light color, with white backgrounds use slightly gray black or any almost black color


• If you have a Mac, use Apple Keynote to get really professional results with less work


• Be proud to do things differently than anyone else in the conference