We naturally lead manicured lives, so that our favorite blogs and writers and friends all look and think and sound a lot like us. (While waiting in line for my cappuccino this weekend, I was ready to punch myself in the face, as I realized that everyone in line was wearing the exact same uniform: artfully frayed jeans, quirky printed t-shirts, flannel shirts, messy hair, etc. And we were all staring at the same gadget, and probably reading the same damn website. In other words, our pose of idiosyncratic uniqueness was a big charade. Self-loathing alert!) While this strategy might make life a bit more comfortable – strangers can say such strange things – it also means that our cliches of free-association get reinforced. We start thinking in ever more constricted ways. And this is why following someone unexpected on Twitter can be a small step towards a more open mind. Because not everybody reacts to the same thing in the same way. Sometimes, it takes a confederate in an experiment to remind us of that.
- Jonah Lehrer, http://scienceblogs.com/cortex/2010/07/twitter_strangers.php
Tagged: AofA RSS
-
Aaron Silvers
-
Aaron Silvers
#GLS2010 – Recommended by Meghan Gardner in a discussion of abundance vs. scarcity models in Game Theory. The book can be found on Amazon here: Nonzero: The Logic of Human Destiny
.
-
Aaron Silvers
“Does the Internet Make You Dumber?” by Nicholas Carr
This article in the Wall Street Journal is an example of criticism on the lengths to which we count on (for the moment) brittle information sources on the Internet.
http://online.wsj.com/article/SB10001424052748704025304575284981644790098.html
-
Aaron Silvers
The Architecture of Actualization Presen…
The Architecture of Actualization
-
Aaron Silvers
Near-peers are the people who know a little bit more than me in a domain of knowledge and can guide me to improve; they might also know a little bit less than me in other areas, and it provides me the opportunity to cement what I know by sharing what I know. Thus we develop together.
-
Aaron Silvers
Knowing who the sources of feedback are has a lot of uses, but there are other uses of Pervasive Feedback where we all might be better off if we didn’t know who was providing feedback.
Consider, for a moment, how suggestion engines currently work.
Suggestion engines, which are at their core collaborative filtering systems, are the closest thing we have to the kind of system I’m talking about. You create an account on @Amazon or @Facebook and once you get past your name and contact information, you’re immediately asked a bunch of questions.
If there was a network for the purposes of learning, the initial profile you complete might address these questions:
- What are your interests?
- Who do you know?
- What do you know?
- What do you want to know?
Assume that you could keep all your personal information (name, phone numbers, email addresses, location, etc) separated from the information in these bigger questions, and other, more specific questions could yield the opportunities for specific connections to people you might not know already, but whom you might benefit from their input.
You could attend a conference, and you might be interested in knowing who else in this hypothetical learning network is also attending the conference.
You might have an idea for a serious game, and you might want to know who at this conference could help you turn your idea into a reality.
You might want information that you could trust because the brokers who (or which) are connecting you and this information are credible (even esteemed), In the same way, you could share rich and valuable knowledge with someone (or an agency) you trust, without sharing the pieces of information that are particularly sensitive and unnecessary to appreciate the knowledge that’s worth sharing.
-
Aaron Silvers
Pervasive Awareness
Pervasive Awareness refers to generated information which captures/exchanges information about/for users semi-autonomously.
An example of this is live-tweeting. The twitter stream is a form of pervasive awareness feedback that could guide speakers and lecturers in real time. One could be made pervasively aware of their performance and, if potentially adjust their lecture or presentation to improve it.
Individually authored feedback, especially constructive criticism that might come across as harsh (out of context), could be especially instructive.
That is a nudging I feel is quite necessary to improving performance and to learn.
I see a connection between Pervasive Awareness is related to General Semantics, which I mentioned earlier. This is already with us, but for the most part such pervasive awareness either happens or it’s manufactured to happen. We want authenticity.
Example: Let’s say the notion of having a GPS for Learning strikes enough of a reaction in people to tweet about it. For anyone to use that feedback constructively as Pervasive Awareness, there needs to be trust.
As a speaker, I don’t necessarily need to trust you, as a person I know or don’t know, to accept your criticism or feedback — I need to trust that what you have to share is valid.
I need to trust that even critical feedback is being shared in a context that is helpful.
-
Aaron Silvers
GPS for Learning
How many of us use some form of GPS to get around town, or to travel?
GPS systems guide us to where we want to go, physically. While we use a GPS, we are all working very diligently to manage the growing amount of information around us already with pulling tools, like your phone; your GPS is largely a pushing tool.
I instruct my GPS I want to get from point A to point B, but I don’t want to compute the path myself. I want the tool to instruct me on where to go. So, too, is it with the notion of a GPS for Learning.
More and more of us use GPS to navigate, spatially, from one location to another location. In our cognitive development, at any one point in our learning, we have opportunities to grow, but more and more of us face challenges in figuring out what to learn next.
If we know what we want to be when we grow up, we could use directions on how to get there.
If we don’t know what we want to be when we grow up, we need to at least get suggestions we can trust on what to learn next.
We’ve bought into the notion, in learning professions, that we are all lifelong learners. I argue that without any direction, we learn without purpose.
Hence the idea of a GPS for Learning; a system of people and tools that can help us individually and collectively surmise where we’re at, where we’ve been, and provide directions on the different paths we can go in and, if we can identify waypoints in our growth, take us on the paths that travel through there.
-
Steve Howard
-
Aaron Silvers
This is a really good point, Steve. The GPS metaphor might be more appropriate in terms of performance outcomes… Hmmm. It’s a really good point.
I’m not quite giving up on it, but this is an area that needs more discussion.
-
Aaron Silvers
Vocabulary
The differences between metadata, paradata and metaparadata, as explicitly and simply as I can make them:
- Metadata is a capture of what a piece of content is about, like a summary or overview with keywords.
- Paradata describes one aspect of how a piece of content is used.
- Context is an aggregation of different pieces of paradata, fully describing how content was used.
- Metaparadata is a capture of the link between you, what you’re looking for and your evaluation of the content.
-
StephanieDaul
Paradata is a little confusing we you go to the slide on Paradate. When you said how content is defined I was think that is determined by the owner of the content. May want to make that a little clearer.
-
Aaron
Content is definitely defined by the owner/creator of content. But it’s also defined by anyone who used the content.
Think of a book you’re reading. It’s defined/designed by the author in the hopes of striking certain chords with you as a reader. That doesn’t always strike you as a reader as the author intended, though.
Your overarching experience with such a book is in a certain context. Each element of that context is a piece of paradata: how did you like the book, what mood were you in when you read the book, what room did you sit in, was it hot outside, did you like the font and the paper, etc.
-
Aaron Silvers
There are links that can be made between what you’re looking for, and what you finally decide is what you’ve been looking for. Those links are very important because it matches up a piece of content with the context of how you want to use it.
In order for the context to make sense to search engines, they need to know a bit about you and how you define things and search for things to make their search engines better.
These linkages are a second-order of paradata — they’re metaparadata.
Metaparadata is incredibly important in semantic web use because in order to guide you or direct you to what you’re looking for (or, in a learning sense, what you might need), these links between who you are, what you’re looking for and what turns out to be the right content have to be understood and abstracted to help other people find what they need, which may not necessarily be what they’re looking for (see General Semantics).
-
Steve Howard
Amazon does this in a number of ways – “if you like this, you’ll probably like this too”. Amazon also helps us along by telling us
- what people ultimately bought when they looked at this
- what else they boughtSo not only is Amazon learning *my* likes, Amazon also tries to ptrdict what I might like/want/need based on the history of other like-minded people. Very smart.
-
Aaron
EXACTLY. Amazon is a consumer model. I personally believe a learning model should be more engaging than just consumption, but what Amazon has done over the past several years is lead in this space.
-
Steve Howard
I’d like to see this sort of thing in an LMS. I see it fledgeling in a couple rigfht now, but I think they are about here Amazon was in 2004 …
-
-
I wrote a long response to the effect that I thought GPS was a bad metaphor, because a learning goal is not finite in the way a physical location is.
But it I never argued my way into a better metaphor. So, given that a learning goal is often not finite (I want to learn how to make great curry) because the possible starting position can be so variable (“I’ve never cooked.” “I’m a professional pastry chef.” “I’ve cooked at home for years, but never cooked with spices before”), what is your picture of how this learning GPS might present itself to us?