ning 2.0 and the
Stephen Downes - 0 views
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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
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2.0 and the impact of web 2
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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
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Reflections on open courses « Connectivism - 4 views
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In education, content can easily be produced (it’s important but has limited economic value). Lectures also have limited value (easy to record and to duplicate). Teaching – as done in most universities – can be duplicated. Learning, on the other hand, can’t be duplicated. Learning is personal, it has to occur one learner at a time. The support needed for learners to learn is a critical value point.
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Excellent insight!
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Here's the key: if what we are typically doing in our classrooms can be easily duplicated, then it has lost its value in both the wider economy and in the educational ecosystem. We university professors must redefine the way we add value to our students' personal learning networks.
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Learning, however, requires a human, social element: both peer-based and through interaction with subject area experts
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Content is readily duplicated, reducing its value economically. It is still critical for learning – all fields have core elements that learners must master before they can advance (research in expertise supports this notion). - Teaching can be duplicated (lectures can be recorded, Elluminate or similar webconferencing system can bring people from around the world into a class). Assisting learners in the learning process, correcting misconceptions (see Private Universe), and providing social support and brokering introductions to other people and ideas in the discipline is critical. - Accreditation is a value statement – it is required when people don’t know each other. Content was the first area of focus in open education. Teaching (i.e. MOOCs) are the second. Accreditation will be next, but, before progress can be made, profile, identity, and peer-rating systems will need to improve dramatically. The underlying trust mechanism on which accreditation is based cannot yet be duplicated in open spaces (at least, it can’t be duplicated to such a degree that people who do not know each other will trust the mediating agent of open accreditation)
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Social Media is Killing the LMS Star - A Bootleg of Bryan Alexander's Lost Presentation... - 0 views
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Note that this isn’t just a technological alternate history. It also describes a different set of social and cultural practices.
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CMSes lumber along like radio, still playing into the air as they continue to gradually shift ever farther away on the margins. In comparison, Web 2.0 is like movies and tv combined, plus printed books and magazines. That’s where the sheer scale, creative ferment, and wife-ranging influence reside. This is the necessary background for discussing how to integrate learning and the digital world.
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These virtual classes are like musical practice rooms, small chambers where one may try out the instrument in silent isolation. It is not connectivism but disconnectivism.
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Here we are…there we are going « Connectivism - 0 views
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Learning consists of weaving together coherent (personal) narratives of fragmented information. The narrative can be now created through social sensemaking systems (such as blogs and social networks), instead of centrally organized courses. Courses can be global, with many educators and participants (i.e. CCK08). Courses, unlike universities, are not directly integrated into the power system of a society. Can decentralized networks of autonomous agents serve the same function as organized institutions? But who loses, and what is lost, if the teaching role of universities decline?
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So learning is developing a story from one's schema of a thing!
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"But who loses, and what is lost, if the teaching role of universities decline?" My concern surrounds the word teaching. Who said that is their primary role? Isn't it licensing, formally sanctioning persons so they can enter the world of work with the "proper" credentials? Did you learn anything in your college days?
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So what really needs to change is not the university, but the culture it serves...
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The virtues that a society finds desirable are systematized in its institutions. However futile this activity, it helps society, and media, to hold people accountable, to devise strategies, and create laws so people feel safe. Similarly, results that are desirable (financial, educationally, etc) are systematized to ensure the ability to manage and duplicate results. I shared some thoughts on this systematization last year as a reason for the currently limited impact of personal learning environments (PLEs). Quite simply, even revolutionaries conserve.
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Teaching is what is most at risk. Can a social network - loosely connected, driven by humanistic ideals - serve a similar role to what university classrooms serve today? I hope so, but I don’t think so. At least not with our current mindsets and skillsets. We associate with those who are similar. We do not pursue diversity. In fact, we shy away from it. We surround ourselves with people and ideas that resonate with our own, not with those that cause us stress or internal conflict. Secondly, until all of society becomes fully networked (not technologically networked, but networked on the principles of flows, connections, feedback), a networked entity always risks being subverted by hierarchy. Today, rightly or wrongly, hierarchy holds power in society.
The Dirty Little Secret About the "Wisdom of the Crowds" - There is No Crowd - 0 views
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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."
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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.
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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.
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Filtering Reality - The Atlantic (November 2009) - 1 views
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Nearly every communication method we invent eventually conveys unwanted commercial messages. AR systems will be used for spam too, whether via graffiti-like tags, ads that pop up when you look too long at a shop, or even abstract symbols stuck to a wall or worn on a shirt that, when viewed through an AR system, turn into 3-D animations. Fortunately, just as Web browsers have pop-up blockers, AR systems will filter spam. Moreover, they’ll likely be able to filter out physical ads, too, such as billboards—a capability that many opponents of visual clutter will find deliriously attractive.
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Conceivably, users could set AR spam filters to block any kind of unpalatable visual information, from political campaign signs to book covers. Parents might want to block sexual or violent images from their kids’ AR systems, and political activists and religious leaders might provide ideologically correct filters for their communities. The bad images get replaced by a red STOP, or perhaps by signs and pictures that reinforce the desired worldview.
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It won’t take a majority of people using these filters to poison public discourse; imagine this summer’s town-hall screamers on constant alert, wherever they go. Yet this world will be the unintended consequence of otherwise desirable developments—spam filters, facial recognition, augmented reality—that many of us will find useful.
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