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Information literacy

Information literacy
The United States National Forum on Information Literacy defines information literacy as " ... the ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively use that information for the issue or problem at hand."[1][2] Other definitions incorporate aspects of "skepticism, judgement, free thinking, questioning, and understanding.. A number of efforts have been made to better define the concept and its relationship to other skills and forms of literacy. History of the concept[edit] The phrase information literacy first appeared in print in a 1974 report by Paul G. The Presidential Committee on Information Literacy released a report on January 10, 1989, outlining the importance of information literacy, opportunities to develop information literacy, and an Information Age School. The Alexandria Proclamation linked Information literacy with lifelong learning. On May 28, 2009, U.S. Presidential Committee on Information Literacy[edit] Related:  knowledge management

The Momentum of Knowledge Management The Momentum of Knowledge Management Debra M. Amidon Founder and Chief Strategist, ENTOVATION® International The following paper summarises recent developments in the field of Knowledge Management. There are accompanying timeline images of Wellsprings Hindsight (44K) and Insight (33K), or an updated comprehensive timeline (305K). This article appeared in the May/June 1996 edition of Research-Technology Management, the journal for Industrial Research Institute (IRI). The Momentum of Knowledge Management What began almost 10 years ago - Knowledge Innovation® - has now reached the stage of a critical mass of insight. Although there has been a plethora of articles and books on the topic, the seminal cook-book (if there ever be such a thing) is only 'work-in-process.' Today, there is an emerging 'community of practice' which transcends any function, sector, industry or geography. transformation of the enterprise - profit or not-for-profit - through knowledge management. 1. 2. 3. . 4. 5. 6. 7.

Wired West vol. 5 no. 4 - Competitive Intelligence: What to Do with the Data Roger Hough, Lowell Professional Services Introduction This article is a summary of a presentation given to a joint meeting of the Special Libraries' Association and the Society of Competitive Intelligence Professionals (SCIP) in Calgary on 16th May 2002. Competitive intelligence increasingly requires access to and the processing of large quantities of data. Various analytical techniques are available to help create actionable intelligence. What is Competitive Intelligence? The Society of Competitive Intelligence Professionals (SCIP) defines CI as "a systematic and ethical programme for gathering and analysing information about your competitors' activities and general business trends to further your own company's goals". CI is not market research, benchmarking, the corporate library, nor is it "neat" information, although all these components can contribute to actionable CI. Converting data into intelligence Analytical Techniques Trend Analysis / Maturity Analysis Toolbox Analysis Bibliometrics

Dealing with the problems The Daily Motivator - Friday, September 13, 2013 The more you learn from your problems, the more effective you become at dealing with them. The more you learn from a problem, the less likely it is to trouble you again. When a difficult problem comes along it can be easy to feel sorry for yourself. What will help is a positive, informed response. At first, go ahead and feel bad about the problem. Problems can get your attention and motivate you, so let them. Choose to be positively motivated, to learn, and to respond with action. — Ralph Marston Copyright ©2013 Ralph S. Copyright ©2013 Ralph S.

Manage Your Data: Data Management: Subject Guides The MIT Libraries supports the MIT community in the management and curation of research data by providing the following services: Data Management Guide This Data Management and Publishing Guide is a practical self-help guide to the management and curation of research data throughout its life cycle. Assistance with Creating Data Management Plans Many funders, such as the National Science Foundation, have requirements for data sharing and data management plans. Workshops Our workshops teach you how to manage data more efficiently for your own use and help you to effectively share your data with others. Individual Consultation and Collaboration with Researchers We are available for individual consultation on data management issues, and can provide expertise in areas such as data organization and preservation, connect you to a network of data management services, and advocate for your needs. Referrals to Related Services Contact Us

Why Socrates hated explicit knowledge, and what to do about it. Socrates, as reported by Plato in The Phaedrus, was not a fan of explicit knowledge. Explicit knowledge, in those days, meant Writing, and Socrates never wrote anything down - he had a scribe (Plato) to do that for him. He mistrusted writing - he felt it made people stupid and lazy by giving them the impression that they were recording (and reading) real knowledge. Here's Socrates "He would be a very simple person...who should leave in writing or receive in writing any art under the idea that the written word would be intelligible or certain; or who deemed that writing was at all better than knowledge and recollection of the same matters..... In the form of a fable, he says this about writing as a means of transmitting knowledge In Summary, Explicit Knowledge, for Socrates, is poor because it cannot be questioned, gives always the same answer, and is the "semblance of truth". Socrates (as befits one of the world's leading philosophers) had a good point.

1. The DIKW Model of Innovation Data simply exists. It gains context to become Information by human interaction, which itself becomes Knowledge by interconversion of different forms of information. Wisdom comes from repetition of the DIK cycle. Data by itself has no meaning. Information arises when humans examine the data. Knowledge is the ability to take an action. Wisdom encompasses the best, most appropriate action. Knowledge and wisdom can only be created by an efficient network of humans. The rate limiting step for most organizations is the creation of knowledge. The faster information flows to individuals, the faster the process of knowledge creation and the easier it is to make appropriate decisions.

Cutting the "cost of not knowing" It's not always easy to put a value on knowledge, or on knowledge management. However it is easier to put a cost to the lack of knowledge, through asking the question "How much would you have saved, if you had known what you know now, in advance"? The answer to this question represents the "cost of no knowledge" We recently asked this question of a project manager, at the end of his project, once he had identified the problem areas with the benefit of hindsight. Savings of $30 m by avoiding sanction delays "The 8 months hiatus may have cost $30 million, that is just off the top of my head, no science” Savings of $.5m in better involvement of the operations staff “The documentation issue may have cost the project about $0.5 million”. $.5m in commissioning + $2-3m lost revenue “The cost to the project would be an extension of the PM team, say $0.5 million, plus 4/6 weeks lost revenue, equivalent to $0.5 million/week.”.

Community of practice and trust building - ... a beginner at something A few days ago I shared my crude model how we go from words to trust. I strung it along: word, definition, context, grammar, meaning, concept, understanding, salience, insight, trust, reputation. I believe each prior step must be present and perceived by both partners in an interaction before the next step gets good traction. Being in the people business of establishing technical trust - as I am - is an interesting combination of challenges: engineering, salesmanship, diplomacy, organization and administration, combined with awareness for the needs of future users of what we test and certify, and the needs and expectations of society. Seeking a competitive edge in this usually means working without a model, or just making one up and test it, see what sticks and build on that. Trust is a non-negotiable essential in business. To me, competitive edge is all about faster, yet secure trust building, towards more intense knowledge flows and learning from each other.

10 Destructive KM Myths These 10 Destructive KM Myths seem to permeate conversations around the digital KM-sphere. They are not ranked and I am sure you could add to them, but, from my perspective, they need to be put out to pasture. 1. KM is technology: I can’t believe that this is still being discussed, but there you go. Look at the number of KM programmes run from an IT-Centric focus and you should start to feel concerned. Why are you interested in managing organisational knowledge resources? 2. 3. 4. 5. 6. 7. 8. 9. 10. Please, take the time to share... virtuallythere - Cognitive Domain dikw We have already made a distinction between learning as a process and learning as a product or learning understood in terms of the outcome the learning process. In this section we are concerned with learning as a product. More specifically, we're concerned with the various components of the cognitive domain. 2.1 Do We Really Want to Produce Wise Students? The inclusion of wisdom in the process of learning might strike some educators as a little odd, but as Bruner pointed out in "The Process of Education", education is about more than learning. 6.1 Reasons for Revising the Taxonomy There are two reasons why the original taxonomy was revised.The first reason is that teachers criticized the original domain because the categories did not correspond to the way in which they framed their learning objectives or their learning outcomes. 6.2 The Knowledge Domain Knowledge is not represented in the revised framework. 6.3 The Cognitive Processes 6.4 The Knowledge Dimension and the Cognitive Processes

The best course I ever did, and 11 Top Tips for creative teaching | Transition Network Over the next few days we will be sharing the winning three stories in our Transition Training competition of courses people did that changed their lives. I thought it might be a good idea to start with my story of the course that impacted me the most in my life so far. In June 2001, I got off the bus in a small village in Lancashire, with a rather heavy bag and in somewhat inclement weather, to walk up the hill to Middlewood, a permaculture project set atop a hill in beautiful woodland. The reason for my trek was to do a course called Teaching Permaculture Creatively, led by Rod Everett. Middlewood was a stunningly beautiful place. The Middlewood Study Centre, with the yurt we studied in to the right. The course itself took place in a large yurt, in the round. What most impressed me was how much of the course, how much of the learning, happened without your being aware that it was happening. Sure enough, it turned out we had learnt an astonishing amount of stuff.

Systema: CI-DIKW Hierarchy Definitions « I have been wanting to clearly define each of the terms Data, Information, Knowledge and Wisdom for some time. I have thought about Artificial Intelligence, Knowledge Bases, Knowledge Management, Data Management and other disciplines and have decided on the following simple definitions: Wisdom is the ability to model entities in a system. This is extrapolative.Knowledge is the ability to model relationships in a system. This is interpolative.Information is the ability to model attributes in a system. This is intrapolative.Data is the ability to model constraints in a system. I have been forced to come up with the root “polite” to describe a single input value as opposed to “polar” which is a collection of input values. Motivation ModelingNetwork ModelingData ModelingProcess ModelingPerson ModelingTime Modeling The perspectives CIDIKW and focuses MNDPPT make a thirty-six cell framework I call the Six Hats, Six Coats Framework. Like this: Like Loading...

Technical Writing and Storytelling — about work Most instructions on technical writing focus on style, structure, clarity and the study of how people read and process information. That’s important — we learn how to organize technical writing logically, define and use terms effectively, and describe complex systems in ways that readers of various experiences levels can understand. In our community, a ton of technical information is shared outside of documentation — where many of the best practices for technical writing were developed. We make blog posts, podcasts, illustrations, tweets, conference presentations, papers and more. In these mediums, we see that the most successful technical writing tells a story that offers a compelling plot and a connection to larger or deeper themes. Tragedies speaks to our human interest in drama and conflict, our voyeurism, our schadenfreude… even our morbidity.