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We create economic value out of information when we figure out an effective strategy that includes aggregating, filtering and connecting.

So, the real question is, how do we design filters that let us find our way through this particular abundance of information? And, you know, my answer to that question has been: the only group that can catalog everything is everybody. One of the reasons you see this enormous move towards social filters, as with Digg, as with del.icio.us, as with Google Reader, in a way, is simply that the scale of the problem has exceeded what professional catalogers can do. But, you know, you never hear twenty-year-olds talking about information overload because they understand the filters they’re given. You only hear, you know, forty- and fifty-year-olds taking about it, sixty-year-olds talking about because we grew up in the world of card catalogs and TV Guide. And now, all the filters we’re used to are broken and we’d like to blame it on the environment instead of admitting that we’re just, you know, we just don’t understand what’s going on.

The five forms of filtering break into two categories: judgement-based, or mechanical.

In general, where we are now in the online world is where we were before the beginning of e-learning [1]. Traditional theories of distance learning, of (for example) transactional distance, as described by Michael G. Moore, have been adapted for the online world. Content is organized according to this traditional model and delivered either completely online or in conjunction with more traditional seminars, to cohorts of students, led by an instructor, following a specified curriculum to be completed at a predetermined pace.

networked markets

In learning, these trends are manifest in what is sometimes called "learner-centered" or "student-centered" design. This is more than just adapting for different learning styles or allowing the user to change the font size and background color; it is the placing of the control of learning itself into the hands of the learner

Venn Diagram's can be used to illustrate the amount of overlap between various sets of items. In the projects section of Neoformix I have just published an application I call Twitter Venn. It supports investigation into the relationship between how words are used within the messages of all the people using Twitter.