21st Century Learning and Teaching

More than 60% of the human population is connected to the Internet, most sectors of activity have switched to digital and software drives innovation. Yet Internet standards and protocols were invented at a time when less than one percent of the population was connected. It is time to use the data flows, the available computing power and the possibilities of interactive communication for human development… and to solve the serious problems we are facing. That is why I will launch soon a major international project – comparable to the construction of a cyclotron or a voyage to Mars – aiming at an augmentation of the Internet in the service of collective intelligence.

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What's the secret of success while innovating? I hear you screaming already: I would like to be innovative, I even have some ideas BUT I am fearing about WHAT OTHERS would say and think about!! To be honest, NEVER look about WHAT OTHERS would say, innovation is something NEW and the new will…

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Pierre Lévy Professeur à l’Université d’Ottawa. Ce travail est la pré-impression d’un article dans le numéro 58 de RED. Il sera publié en tant que contribution d’invité, dans le genre «histoire personnelle comme recherche éducative» (Personal History as Educational Research).   Cet article n’a d’autre but que de raconter mon expérience...

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https://globaleducationandsocialmedia.wordpress.com/2014/01/19/pkm-personal-professional-knowledge-management/

Résumé : Le medium algorithmique est une infrastructure de communication qui augmente les pouvoirs des médias antérieurs en y ajoutant la mécanisation des opérations symboliques. Son émergence au milieu du vingtième siècle résulte d’une longue histoire scientifique et technique que je résume au début de l’article. Je rappelle ensuite les grandes étapes de son développement (ordinateurs centraux, internet et PC, Web social, Cloud augmenté par l’intelligence artificielle et la chaîne de blocs) ainsi que leurs conséquences sociocognitives.

J’évoque pour finir les développements futurs de ce médium dans la perspective d’une intelligence collective réflexive basée sur une nouvelle forme de calcul sémantique.

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Contrary to what its name might suggest, digital transformation is primarily a problem of culture and leadership, a “human” phenomenon. It implies working in a transdisciplinary way, collaborating beyond the usual borders and above all – like in any organizational transformation process – creating trust: trust between engineers and humanists, between government departments, between citizens and government, etc. And since there are no miracles, trust is built over time and experience and it can only come from listening, honesty, competence, and transparency.

Technically, the data produced and collected must be designed from the outset to lend themselves optimally to automatic analysis and the systems must be built so that they can evolve easily. As for the famous ethical dimension of algorithms, we will not forget that the data feeding automatic learning must be examined from a critical point of view by competent humans and not blindly believed. In my understanding, a good part of the “ethical” problems related to AI comes from two essential points being forgotten:

– The tools must be designed from the user’s point of view (design thinking).
– The quality of training data determines the quality of program performance (garbage in garbage out).

Many topics were treated in this event, with numerous concrete examples of the use of new technologies: security, the blockchain, cloud, micro-services, Internet of things, etc. The theme that caught my attention the most was chatbots animated by natural language processing and deep learning. In the coming years, more and more services (governmental or commercial) will use chatbots.

The public already knows Siri by Apple or Alexa by Amazon. Rather than thinking of these programs as autonomous “artificial intelligence”, it would be more useful to conceive them as media for distributing expertise, user-friendly interfaces for exploring huge databases, knowledge management toolsand cognitive augmentation. Chatbots are able to understand questions in natural languages (not just recognize keywords), they can learn from their interactions, they are able to suggest relevant ideas according to context and provide a “pleasant” experience to their users.

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FIGURE 1 J'ai montré dans un post précédent, l'importance contemporaine de la curation collaborative de données.  Les compétences dans ce domaine sont au coeur de la nouvelle litéracie algorithmique.

La figure 1 présente ces compétences de manière systématique et, ce faisant, elle met en ordre les savoir-faire intellectuels et pratiques tout comme les « savoir-être » éthiques…

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