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anonymous

Carsonified » Meet @HelloApp, Making Conferences More Fun - 0 views

carsonified.com/...pp-making-conferences-more-fun

i3tools eci831 cck09 i3cs21 twitterapp

shared by anonymous on 22 Sep 09 - Cached
  • anonymous on 22 Sep 09
     
    After four tiring-exciting-stressful-fun days, we'd like to introduce you to our new little buddy, HelloApp. The idea is simple: When you arrive at a conference, you just say where you're sitting, via Twitter. Once you do that, you can … 1. Search for people with a certain skill-set (ie PHP, jQuery, CSS3, marketing, etc) and see where they're sitting 2. View the seating diagram colored based on Twitter follower count 3. Search for a specific person in the audience and find out where they're sitting 4. View the seating diagram colored based on whether people are Designers, Developers or Businessmen 5. Earn badges and points by meeting people and completing tasks. If you earn a high enough rank, you'll be able to post public messages to the entire audience and win prizes.
gobibijou

Stephen Downes - 0 views

www.blip.tv/840097

downes elearning education web2.0

shared by gobibijou on 24 Sep 09 - Cached
  • ning 2.0 and the
    • gobibijou
      gobibijou on 24 Sep 09
       
      S. Downes: http://www.blip.tv/file/840097 2 approaches to learning - tradiotional (AI): old artifitial technology. Expert system organises. Old managnement systems. Focus on: - Goal orientated. - Competencies. - Efficency (from A to B in the most efficient). Requieres: - an expert - knowledge representation (VS. Siemens: the knowledge that we have CAN'T be represented) for expl. language -- Problem: it creates a simplification of the knowledge. - learning activities are set up by an expert. -network approach: (???IDF). Conectivism (born 40 years ago Pappert &?). Computational system is NOT set up as a representational system BUT is set up as a NETWORK (like a brain). The connectivist system: - is unnorganized - is unstructured (previously) - looks messy and unorganised - can NOT be predicted HOw Knowledge is represented in the system? DISTRIBUTED. Our concept of X is not a symbolic representation but a set up of active connections also in a neuronal level (?) Model of learning NOt based in deduction and inference BUT on ASSOCIATION based on: - concurrency. - proximity. - back propagation (economics: supply and demand market is based on that) - ???Amealing the way form networks/community in society work in THE SAME WAY that they do in a neuronal level and a personal level. Communities ARE networks that work through distributed connections. How should be the network? - DIVERSITY (wide representation of different points of views) Knowledge in a network is: EMERGENT - AUTONOMY : each individual is self-directed. Each individual works as his own guide. - CONNECTEDNESS (or interactivities). Knowledge produced by mechanism of interaction is produced by the nature/properties of the network. The way/organization of connections are formed is essential. - OPENESS (there's no inside/outside the "system"). Connection FLOWS freely. RECOGNITION of patterns (clustter). LEARNERS: Learners have different things they want to learn and the system
  • 2.0 and the impact of web 2
    • gobibijou
      gobibijou on 24 Sep 09
       
      S. Downes: http://www.blip.tv/file/840097 NOtes (need to be double checked) 2 approaches to learning 1. traditional (AI): old artifitial technology. Expert system organises. Old managnement systems. Focus on: - Goal orientated. - Competencies. - Efficency (from A to B in the most efficient). Requieres: - an expert - knowledge representation (VS. Siemens: the knowledge that we have CAN'T be represented) for expl. language -- Problem: it creates a simplification of the knowledge. - learning activities are set up by an expert. 2.-network approach: (???IDF). Conectivism (born 40 years ago Pappert &?). Computational system is NOT set up as a representational system BUT is set up as a NETWORK (like a brain). The connectivist system: - is unnorganized - is unstructured (previously) - looks messy and unorganised - can NOT be predicted HOw Knowledge is represented in the system? DISTRIBUTED. Our concept of X is not a symbolic representation but a set up of active connections also in a neuronal level (?) Model of learning NOt based in deduction and inference BUT on ASSOCIATION based on: - concurrency. - proximity. - back propagation (economics: supply and demand market is based on that) - ???Amealing the way form networks/community in society work in THE SAME WAY that they do in a neuronal level and a personal level. Communities ARE networks that work through distributed connections. How should be the network? - DIVERSITY (wide representation of different points of views) Knowledge in a network is: EMERGENT - AUTONOMY : each individual is self-directed. Each individual works as his own guide. - CONNECTEDNESS (or interactivities). Knowledge produced by mechanism of interaction is produced by the nature/properties of the network. The way/organization of connections are formed is essential. - OPENESS (there's no inside/outside the "system"). Connection FLOWS freely. RECOGNITION of patterns (clustter). LEARNERS: Learners have different thin
  • gobibijou on 24 Sep 09
     
    downes talking about approaches in education. Web 2.0, elearning...
Ed Webb

The Dirty Little Secret About the "Wisdom of the Crowds" - There is No Crowd - 0 views

www.readwriteweb.com/...t_the_wisdom_of_the_crowds.php

crowdsourcing connectivism opinion

shared by Ed Webb on 22 Sep 09 - Cached
  • Wikipedia isn't written and edited by the "crowd" at all. In fact, 1% of Wikipedia users are responsible for half of the site's edits. Even Wikipedia's founder, Jimmy Wales, has been quoted as saying that the site is really written by a community, "a dedicated group of a few hundred volunteers."
  • I think your headline is misleading and Vassilis Kostakos should read the book before poking holes. Surowiecki is very clear about the conditions necessary for a wise crowd to prevail and those conditions are: 1. Diversity of opinion 2. Independence 3. Decentralization 4. Aggregation If your crowd possesses those qualities then it is wise and then it will be better at making decisions under Surowiecki's paradigm. The crowds used in the research (and the crowd in general) doesn't possess those qualities and therefore is an unfit data set. We should be trying to create the ideal crowd before we can obtain superlative results and not try to get good results from any random crowd.
  • Limitations in predictions market are well documented (and include Muhammad's points above), and constrain their practical application to a well-defined number of situation. Crowdsourcing suffers from the same limitations, which is not a problem, as long as you limit its application correspondingly. The problem occur when you stretch it outside the required constraints and yet present the results as "scientific", i.e. as a good proxy for what the crowd thinks. That's what professor Vassilis Kostakos's theory ultimately comes down to (or should - I don't know, I haven't read his report). Apps like Digg or Amazon's review are not scientific applications of crowdsourcing, and thus their results should not be seen as precise representation of our collective thinking.
  • ...3 more annotations...
  • Wisdom of Crowds is a crypto-fascist idea; there is no objective truth, there are no facts, truth is what "the crowd" decides it is. You get these unhealthy echo chambers of "activists" setting the agenda. This article said it best, over three years ago: DIGITAL MAOISM The Hazards of the New Online Collectivism By Jaron Lanier
  • What I'd like to see is non-fakeable metrics on ecommerce sites: return rates or reorder rates (as appropriate), for example. Or for apps, how many times users open the app per day/week or whatever.
  • the research is interesting if linked to ideas of unrepresentative or illiberal democracy, as posited by Fareed Zakaria that suggests small interest groups can hijack democratic systems.
Ed Webb

The Transducer » Blog Archive » Brain Behavior and Behaving like Brains - 2 views

transducer.ontoligent.com/...599

transducer cognition education learning

shared by Ed Webb on 15 Oct 09 - Cached
  • boundaries are inserted where the brain experiences what Zacks calls “prediction error” — when things break a pattern of repetition and thus signal to the brain a boundary that is used to construct the temporal model for the event — its typical sequence. 
  • the response of the audience — comprised mainly of educational experts — and of Zacks himself is that one practical lesson from his research is that creators of narrative content, such as film, should make an effort to provide more obvious segmentation in their products.  Clearly, if this is how the brain works, we should work this way too. I think this is a major fallacy that pervades the reception of brain science research.  People tend to assume that if the brain works a certain way, then so should we.
  • the lesson is to get good at perceiving and creating event boundaries, which requires not pre-segmented media, but the opposite — hard to grasp art, stuff that violates expectations and rewards the perciever with a different perspective.   In fact, giving students media with well defined boundaries may cause their capacity to construct boundaries to atrophy, much as caffeine causes our adrenal glands to shrink. (I know, it’s a good reason to stop drinking coffee.)
  • ...1 more annotation...
  • So, what is the pedagogical and media design lesson here?  Learning Teaching is not about making content easy to ingest, it’s about creating environments where students can play this game of meaning formation, which isn’t always stress-free.  Marketers may disagree, but they are in the business of indoctrination, not teaching.
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