CLOUD: A Computing Infrastructure on Demand

Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services. A cloud computing platform dynamically provisions, configures, reconfigures, and deprovisions servers as needed. Cloud applications are those that are extended to be accessible through the Internet. The datacenter hardware and software is what we will call a Cloud. Cloud computing is changing the way we provision hardware and software for on-demand capacity fulfillment and changing the way we develop web applications and make business decisions. Definition: Cloud computing is a computing paradigm in which tasks are assigned to a combination of connections, software and services accessed over a network. This network of servers and connections is collectively known as "the cloud." Computing at the scale of the cloud allows users to access supercomputer-level power. Users can access resources as they need them.

Cloud computing describes how computer programs are hosted and operated over the Internet. The key feature of cloud computing is that both the software and the information held in it live on centrally located servers rather than on an end-user's computer. How does cloud computing

works? The concept is fairly simple. First, consider the traditional means of running application: and application appears to run on a dumb terminal or, these days your PC; practicality, this is only front-end of the application. Your computer is connected to a server that actually runs the

program or application and returns information to your personal computer. The server constitutes the backend and it can be located in the same building as you are or not. With cloud computing application program runs somewhere within the cloud; ideally the user concern only with applications that are available and need not to be aware of the underlying technology or the physical location of the Application's computer. User desktop is connected via internet to a server farm, a collection of remote servers that runs many, many applications at once. Which server or servers an application runs on is determined by the 􀁪pplication program already running on the machines; there IS an attempt to balance the load so that all of the programs run optimally.

A carbon 2.0 framework based on cloud computing

One remarkable news in 2009 was the United Nations Climate Change Conference (COP15) held in Copenhagen. Although many were disappointed with COP15's main output, the summit did introduce a new kind of dynamics in global climate policy, in addition to the Copenhagen accord. Specifically, considering the climate change all over the planet, we must pay close attention to this severe situation and take action as soon as possible. Under this new trend, all organizations, even households and individuals, are expected to set their agendas to manage their carbon

footprint (CFP) below a certain level in the near future. This new practice needs appropriate IT systems to help tackle challenges in measuring, analyzing and managing carbon emissions in a cost-effective manner . Currently, several carbon management systems are being developed to facilitate enterprises to calculate and manage their CFP, such as ESS's GHG/Carbon management SolutionTM , U.S. Army's Enviance system ,IBM/EIM's GreenCertTM, etc. However, their IT architectures are less efficient compared to the latest IT technologies. In addition, a number of CFP tools are being developed to fulfill households and individuals’ carbon

related needs, such as UK Carbon Footprint Ltd's CFP calculator , the Nature Conservancy's CFP calculator , etc. These CFP tools are based on B/S model, and it can only calculate user's CFP over the Internet. Therefore, it is still a challenging issue on how to take advantage of the latest IT technologies to improve carbon systems.We focus on the next-generation carbon systems for both enterprises and households use and present a carbon 2.0 framework based on cloud computing. This new framework is massively scalable and can dynamically configure/deliver

carbon-related services on demand. Moreover, it is a real low-carbon framework itself!

The term "carbon 2.0" was first introduced by Qindong Liu and Jan Stallaert, who attempted to unveil the architecture and functionality of future carbon systems for enterprises. Cloud computing is a new computing paradigm and it can make good use of

economies of scale, and dynamically deliver almost any IT related resources as a service on demand, such as computing power, storage capacity, network service, integrated management, with dynamically configurable interface and the economy model of charging for capacity.

Simulation

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning.
There are two types of simulation:-
Physical simulation refers to simulation in which physical objects are substituted for the real thing (some circles use the term for computer simulations modelling selected laws of physics, but this article doesn't). These physical objects are often chosen because they are smaller or cheaper than the actual object or system.
Interactive simulation is a special kind of physical simulation, often referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.
Human in the loop simulations can include a computer simulation as a so-called synthetic environment.

Wireless communication

The term wireless is normally used to refer to any type of electrical or electronic operation which is accomplished without the use of a "hard wired" connection. Wireless communication is the transfer of information over a distance without the use of electrical conductors or "wires ".The distances involved may be short (a few meters as in television remote control) or very long (thousands or even millions of kilometers for radio communications). When the context is clear the term is often simply shortened to "wireless". Wireless communications is generally considered to be a branch of telecommunication .
Wireless operations permits services, such as long range communications, that are impossible or impractical to implement with the use of wires. The term is commonly used in the telecommunications industry to refer to telecommunications systems (e.g., radio transmitters and receivers, remote controls, computer networks, network terminals, etc.) which use some form of energy to transfer information without the use of wires. Information is transferred in this manner over both short and long distances.
The term "wireless" has become a generic and all-encompassing word used to describe communications in which electromagnetic waves or RF (rather than some form of wire) carry a signal over part or the entire communication path.
Wireless networking (i.e. the various flavors of unlicensed 2.4 GHz WiFi devices) is used to meet a variety of needs. Perhaps the most common use is to connect laptop users who travel from location to location. Another common use is for mobile networks that connect via satellite. A wireless transmission method is a logical choice to network a LAN segment that must frequently change locations.

Neural Networks

In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses , are usually formed from axons to dendrite , though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex. Whilst a detailed description of neural systems is nebulous, progress is being charted towards a better understanding of basic mechanisms.

Simplified view of an artificial neural network

Artificial intelligence and cognitive modelling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems.

In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition,image analysis and adaptive control , in order to construct software agents(in computer and video games) or autonomous robots . Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimization and control theory.

The cognitive modelling field involves the physical or mathematical modeling of the behaviour of neural systems; ranging from the individual neural level (e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism (e.g. behavioural modelling of the organism's response to stimuli).

Telecommunication

Telecommunication is the assisted transmission of signals a distance for the purpose of communication. In earlier times, this may have involved the use of smoke signals, drums, semaphores,flags, or heliograph . In modern times, telecommunication typically involves the use of electronic transmitters such as the telephone, television, radio,computer. Early inventors in the field of telecommunication include antonio meucci ,Alexander Graham Bell,Guglielmo marconi and john logie baird . Telecommunication is an important part of the world economy and the telecommunication industry's revenue has been placed at just under 3 percent of the gross world product .

Artificial Learning

Much effort has been devoted to understanding learning and reasoning in artificial intelligence, giving rise to a wide collection of models. For the most part, these models focus on some observed characteristic of human learning, such as induction or analogy, in an effort to emulate (and possibly exceed) human abilities. We propose seven desirable properties for artificial learning systems: incrementality, non-monotonicity, inconsistency and conflicting defaults handling, abstraction, self-organization, generalization, and computational tractability.We examine each of these properties in turn and show how their (combined) use can improve learning and reasoning, as well as potentially widen the range of applications of artificial learning systems.
Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.

If features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoreical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.

All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology , a new mathematical theory of information,an understanding of control and stability called cybernetics , and above all, by the invention of the digital computer , a machine based on the abstract essence of mathematical reasoning

Sylvan learning

Sylvan Learning is a chain of franchised tutoring centers which provide personalized tutoring in readnig,writing, mathematics, skills and test-prep for college entrance and state exams. Since June of 2007, Sylvan has been owned by Edge Acquisition, LLC, which operates a variety of for-profit educational businesses. Sylvan Learning is headquartered in Baltimore,Maryland.

By 1987 Sylvan had slightly over 500 franchises nationwide and went public on the NASDAQ exchange. By the summer of 1987 most of the stock was acquired by KinderCare, Inc. (Montgomery, AL). The company moved its headquarters from suburban Seattle to Alabama at that time.

KinderCare executives by late 1988 had replaced the original Sylvan founding staff.

Sylvan Learning began in Portland, OR in the early 1980's and was founded by former school teacher, W. Berry Fowler. By late 1983, Sylvan was successfully managing multiple franchises in the western USA and moved to suburban Seattle. Julie Davis was responsible for the educational programs started by Fowler. She added more reading programs and math, college prep plus pre-K programs. Claude Rorabaugh (who later was responsible for the development and growth of LaserGrade Computer Testing in the 2000's) was responsible for marketing and franchise center development. Besides profitable center development, he and his team offered tuition financing and actual guarantees of student growth. ("In 3 and a half months your child can gain a full grade level...")Those marketing tools plus endorsements from former Reagan cabinet member, Education Secretary William Bennett, fueled the growth of company in the mid '80's.

Its Services:-

Sylvan Learning offers instruction in the form of remedial help or enrichment for high-achieving students. For students who are experiencing difficulty in school, Sylvan's programs include Beginning Reading, Academic Reading, Academic Writing, Math Essentials, Advanced Math, Study Skills, and Homework Support. Students who are on track in school may enroll in Sylvan's SAT and ACT prep courses, as well as Advanced Reading, which is a form of speed reading . Starting in 2008, Sylvan Learning Center now offers a College Writing Prep class, matching that with rival Huntington Learning Center.

learning by analogy

Reasoning by analogy generally involves abstracting details from a a particular set of problems and resolving structural similarities between previously distinct problems. Analogical reasoning refers to this process of recognition and then applying the solution from the known problem to the new problem. Such a technique is often identified as case-based reasoning. Analogical learning generally involves developing a set of mappings between features of two instances. Paul Thagard and Keith Holyoak have developed a computational theory of analogical reasoning that is consistent with the outline above, provided that abstraction rules are provided to the model.
Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. The history of work on analogy in modern cognitive science is sketched, focusing on contributions from cognitive psychology, artificial intelligence, and philosophy of science. This review sets the stage for the 3 articles that follow in this Science Watch section.
Many professors rely on analogy, metaphor and over-generalization to maximize complex learning, and it's a skill that is taught in graduate schools across the world. I've taught over 80 classes in graduate school, and the only way to teach complex topics like statistics and database are by using analogies, building on the prerequisite conceptual framework classes such as "Algorithms" and "Data Structures" courses.
Teaching by analogy and over-generalization is an integral part of the learning process, especially for complex concepts, and academic research confirms the centuries-old belief that children learn complex concepts best by analogy and over-simplification.
Ontology Recapitulates Phylogny is the analogy (untrue, BTW), that a developing zygote/fetus reproduces it's own evolutionary stages

a quotes given by william wordsworth

Science appears as what in truth she is,
Not as our glory and our absolute boast,
But as a succedaneum, and a prop
To our infirmity.

learning by induction

Inductive learning is essentially learning by example. The process itself ideally implies some method for drawing conclusions about previously unseen examples once learning is complete. More formally, one might state: Given a set of training examples, develop a hypothesis that is as consistent as possible with the provided data. It is worthy of note that this is an imperfect technique. As Chalmers points out, "an inductive inference with true premises [can] lead to false conclusions". The example set may be an incomplete representation of the true population, or correct but inappropriate rules may be derived which apply only to the example set.

A simple demonstration of this type of learning is to consider the following set of bit-strings (each digit can only take on the value 0 or 1), each noted as either a positive or negative example of some concept. The task is to infer from this data (or "induce") a rule to account for the given classification:

A rule one could induce from this data is that strings with an even number of 1's are "+", those with an odd number of 1's are "-". Note that this rule would indeed allow us to classify previously unseen strings (i.e. 1001 is "+").

Techniques for modeling the inductive learning process include: Quinlan's decision trees (results from information theory are used to partition data based on maximizing "information content" of a given sub-classification) , connection decision list techniques , among others. (most neural network models rely on training techniques that seek to infer a relationship from examples) and

This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods both connectionist and symbolic

The intrinsic accuracy of an inductive problem is the accuracy achieved by exhaustive table look-up. Intrinsic accuracy is the upper bound for any inductive method. Hard concepts are concepts that have high intrinsic accuracy, but which cannot be learned effectively with traditional inductive methods. To learn hard concepts, we must use constructive induction - methods that create new features. We use measures of concept dispersion to explore (conceptually and empirically) the inherent weaknesses of traditional inductive approaches. These structural defects are buried in the design of the algorithms and prevent the learning of hard concepts. After studying some examples of successful and unsuccessful feature construction ("success" being defined here in terms of accuracy), we introduce a single measure of inductive difficulty that we call variation. We argue for a specific approach to constructive induction that reduces variation by incorporating various kinds of domain knowledge. All of these kinds of domain knowledge boil down to utility invariants, i.e., transformations that group together non-contiguous portions of feature space having similar class-membership values. Utility invariants manifest themselves in various ways: in some cases they exist in the user's stock of domain knowledge, in other cases they may be discovered via methods we describe

LEARNING CENTERS

Online Schools ensures that all schools have effective access to the communications and information resources of the Internet. Online Schools has developed guidelines for cost-effective Internet Learning Centers with reliable access to the Internet. In creating these guidelines, Online Schools considered various elements such as Internet connectivity, computer availability, acquisition costs, scalability, technical support, and educational tools.

An internet learning centers are made up of a network of ten high-speed, Pentium-class computers with color monitors, two printers, a scanner and a digital still camera. Software included word processing, presentation, spreadsheet, antivirus, web publishing, and image editing applications.

Online Schools suggests a variety of ways to access the Internet, including dial-up, leased-line, and wireless.
The Project Coordinator should create partnerships with a local Internet Service Provider (ISP) and local
technology distributors to support reliable, and sustainable Internet access.

In locations where high bandwidth is available, leased-line access would be appropriate. Regions that are more
rural, but maintain telephone lines should use dial-up access. Remote regions and areas without reliable
telephone access should investigate the feasibility of wireless Internet access. Internet speed should be in the
range from 28K to 128K. The Internet should be available for use during the school day for a minimum of eight
hours.

Machine learning

As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics , but also theoretical computer science.

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:

• Supervised learning — in which the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate) the behavior of a function which maps a vector into one of several classes by looking at several input-output examples of the function.
• Unsupervised learning — An Agent which models a set of inputs: labeled examples are not available.
• Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
• Reinforcement learning — in which the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
• Transduction — similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
• Learning to learn — in which the algorithm learns its own inductive bias based on previous experience.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge. This capacity to learn from experience, analytical observation, and other means, results in a system that can continuously self-improve and thereby offer increased efficiency and effectiveness.

If an expert system--brilliantly designed, engineered and implemented--cannot learn not to repeat its mistakes, it is not as intelligent as a worm or a sea anemone or a kitten.
-Oliver G. Selfridge, from The Gardens of Learning.

"Find a bug in a program, and fix it, and the program will work today. Show the program how to find and fix a bug, and the program will work forever."
- Oliver G. Selfridge, in AI's Greatest Trends and Controversies

Good Places to Start

The Discipline and Future of Machine Learning.video of Tom Mitchell's March 1, 2007 seminar talk at the Carnegie Mellon University School of Computer Science's Machine Learning Department: "Over the past 50 years the study of machine learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of statistics that largely ignored computational considerations, to a broad discipline that has produced fundamental statistical-computational theories of learning processes, has designed learning algorithms that are routinely used in commercial systems from speech recognition to computer vision, and has spun off an industry in data mining to discover hidden regularities in the growing volume of online data. This talk will provide a personal view of the current state of machine learning, and where I think the field might (should) be headed over the coming decade. I’ll propose several specific research areas which seem to me to have great potential, and will leave plenty of time at the end for audience discussion."

An AI Bite by Simon Colon. Sponsored by, and available from, The Society for the Study of Artificial Intelligence and Simulation of Behavior. "Given a task we want the computer to do, the idea is to repeatedly demonstrate how the task is performed, and let the computer learn by example, i.e., generalize some rules about the task and turn these into a program."

Introduction to Machine Learning - Draft of Incomplete Notes. By Nils J. Nilsson. "The notes survey many of the important topics in machine learning circa 1996. My intention was to pursue a middle ground between theory and practice. The notes concentrate on the important ideas in machine learning---it is neither a handbook of practice nor a compendium of theoretical proofs. My goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible."

• Chapter 1, Introduction: What is Machine Learning? "Machine learning usually refers to the changes in systems that perform tasks associated with artificial intelligence (AI). Such tasks involve recognition, diagnosis, planning, robot control, prediction, etc. ... To be slightly more specific, we show the architecture of a typical AI 'agent' in Fig. 1.1. ... One might ask 'Why should machines have to learn? Why not design machines to perform as desired in the first place?' There are several reasons why machine learning is important. ..."

Software That Learns by Doing. Machine-learning techniques have been used to create self-improving software for decades, but recent advances are bringing these tools into the mainstream. By Gary H. Anthes. Computerworld (February 6, 2006). "Attempts to create self-improving software date to the 1960s. But 'machine learning,' as it's often called, has remained mostly the province of academic researchers, with only a few niche applications in the commercial world, such as speech recognition and credit card fraud detection. Now, researchers say, better algorithms, more powerful computers and a few clever tricks will move it further into the mainstream. ... Computer scientist Tom Mitchell, director of the Center for Automated Learning and Discovery at Carnegie Mellon University, says machine learning is useful for the kinds of tasks that humans do easily -- speech and image recognition, for example -- but that they have trouble explaining explicitly in software rules. In machine-learning applications, software is 'trained' on test cases devised and labeled by humans, scored so it knows what it got right and wrong, and then sent out to solve real-world cases. Mitchell is testing the concept of having two classes of learning algorithms in essence train each other...."

Machine learns games 'like a human.' By Will Knight. New Scientist News (January 24, 2005). "A computer that learns to play a 'scissors, paper, stone' by observing and mimicking human players could lead to machines that automatically learn how to spot an intruder or perform vital maintenance work, say UK researchers. CogVis, developed by scientists at the University of Leeds in Yorkshire, UK, teaches itself how to play the children's game by searching for patterns in video and audio of human players and then building its own 'hypotheses' about the game's rules. In contrast to older artificial intelligence (AI) programs that mimic human behaviour using hard-coded rules, CogVis takes a more human approach, learning through observation and mimicry, the researchers say. ... 'A system that can observe events in an unknown scenario, learn and participate just as a child would is almost the Holy Grail of AI,' says Derek Magee from the University of Leeds." Be sure to see the sidebar with related articles & web sites.

Machine Learning Lecture Notes. From Professor Charles R. Dyer, University of Wisconsin - Madison.

"The importance of learning, however, is beyond question, particularly as this ability is one of the most important components of intelligent behavior. ... Although learning is a difficult area, there are several programs that suggest that it is not impossible. One striking program is AM, the Automated Mathematician, designed to discover mathematical laws (Lenat 1977, 1982). Initially given the concepts and axioms of set theory, AM was able to induce such important mathematical concepts as cardinality, integer arithmetic, and many of the results of number theory. AM conjectured new theorems by modifying its current knowledge base and used heuristics to pursue the 'best' of a number of possible alternative theorems. ... Early influential work includes Winston's research on the induction of structural concepts such as 'arch' from a set of examples in the blocks world (Winston 1975 a)."

Machine Learning. Preprint of Thomas G. Dietterich's article in Nature Encyclopedia of Cognitive Science, London: Macmillan, 2003. Available from the author's collection of introductory information. "Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. ... Second, there are problems where human experts exist, but where they are unable to explain their expertise. ... Third, there are problems where phenomena are changing rapidly. ... Fourth, there are applications that need to be customized for each computer user separately."

• Also see Professor Dietterich's home page for links to ML resources and more information about his research at Oregon State University: "The focus of my research is machine learning: How can we make computer systems that adapt and learn from their experience? How can we combine human knowledge with massive data sets to expand scientific knowledge and build more useful computer applications? My laboratory combines research on machine learning fundamentals with applications to problems in science and engineering."

Two courses from MIT's OpenCourseWare "a free and open educational resource for faculty, students, and self-learners around the world."

• Machine Learning; Fall 2002. Professor Tommi Jaakkola. "6.867 is offered under the department's 'Artificial Intelligence and Applications' concentration. The site offers a full set of lecture notes, homework assignments, in addition to other materials used by students in the course. 6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work."

Videos of lectures & interviews from the 2006 Machine Learning Autumn School at CMU: Machine Learning over Text & Images (available from VideoLectures). "Machine learning approaches to natural language processing problems such as information retrieval, document classification, and information extraction have developed rapidly over recent years. Even more recently, the joint analysis of text and images has become a significant focus for machine learning. This autumn school will summarize the state of the art in machine learning for text analysis and for joint text/image analysis, as presented by researchers active in these fields. It is intended for students who already have a familiarity with machine learning, and is designed for software developers, graduate students, and advanced researchers with an interest in learning more about this area."

Glossary of Terms. Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Ron Kohavi and Foster Provost, eds. Machine Learning, 30: 271-274 (1998). "To help readers understand common terms in machine learning, statistics, and data mining, we provide a glossary of common terms."

Readings Online

AI in the news: Machine Learning

Applying Metrics to Machine-Learning Tools: A Knowledge Engineering Approach. Fernando Alonso, Luis Mate, Natalia Juristo, Pedro L. Munoz, and Juan Pazos. AI Magazine 15(3): Fall 1994, 63-75. "The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. Machine-learning tools have contributed positively to the process of trying to eliminate or open up this bottleneck, but how do we know whether the field is progressing? How can we determine the progress made in any of its branches? How can we be sure of an advance and take advantage of it? This article proposes a benchmark as a classificatory, comparative, and metric criterion for machine-learning tools. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool."

Machine Learning: A Historical and Methodological Analysis. By Jaime G. Carbonell, Ryszard S. Michalski, and Tom M. Mitchell. AI Magazine 4(3): Fall 1983, 69-79. Abstract: "Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques."

Brain learns like a robot- Scan shows how we form opinions. By Tanguy Chouard. Nature Science Update (June 10, 2004). "Researchers may have pinpointed the brain regions that help us work out good from bad. And their results suggest that humans and robots are more alike than we may care to admit, as both use similar strategies to make value judgements. ... The team also plotted brain activity on a graph to give a mathematical description of processes that underlie the formation of value judgements. The patterns they saw resembled those made by robots as they learn from experience. 'The results were astounding,' says study co-author Peter Dayan. 'There was an almost perfect match between the brain signals and the numerical functions used in machine learning,' he says. This suggests that our brains are following the laws of artificial intelligence."

Machine Learning Research: Four Current Directions. By Tom Dietterich. AI Magazine 18(4): Winter 1997, 97-136. Abstract: "Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models."

Learning. An overview by Patrick Doyle. Very informative, though there are some spots that are quite technical.

Journal of Machine Learning Research. "The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning."

Bookish Math - Statistical tests are unraveling knotty literary mysteries. By Erica Klarreich. Science News (December 20, 2003; Vol. 164, No. 25). "Stylometry ['the science of measuring literary style'] is now entering a golden era. In the past 15 years, researchers have developed an arsenal of mathematical tools, from statistical tests to artificial intelligence techniques, for use in determining authorship. ... For decades, computers have supported the work of experts in stylometry. Now, computers are becoming experts in their own right, as some researchers apply artificial intelligence techniques to the question of authorship. ... In 1993, Robert Matthews of Aston University in England and Thomas Merriam, an independent Shakespearean scholar in England, created a neural network that could distinguish between the plays of Shakespeare and of his contemporary Christopher Marlowe. A neural network is a computer architecture modeled on the human brain, consisting of nodes connected to each other by links of differing strengths. ... A couple of years later, Holmes and Richard Forsyth of the University of Luton in England used the Federalist Papers to test another artificial intelligence technique. They applied genetic algorithms, which use Darwinian principles of natural selection. The idea is to create a set of rules for determining authorship and then let the most useful, or fit, rules survive. ... Yet another analysis of the Federalist Papers was presented at a computer science conference in October. Glenn Fung of Siemens Medical Solutions in Malvern, Pa., used one of artificial intelligence's newest tools, a pattern-recognition technique called support-vector machines."

Machine Learning, Neural and Statistical Classification. Donald Michie, D. J. Spiegelhalter, and C. C. Taylor, editors. "[This] book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web."

AI and the Impending Revolution in Brain Sciences Powerpoint slides of Tom Mitchell's AAAI Presidential Address, August 2002. [An associated video file is also available from his home page"Thesis of This Talk: The synergy between AI and Brain Sciences will yield profound advances in our understanding of intelligence over the coming decade, fundamentally changing the nature of our field."

Statistical Data Mining Tutorials - Tutorial Slides by Andrew Moore, professor of Robotics and Computer Science at the School of Computer Science, Carnegie Mellon University. "The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms."

Machine learning on physical robots [slide show with audio]. By Peter Stone, The University of Texas at Austin. Presented at the University of Pennsylvania's GRASP Lab's GRASP Seminar Series (March 31, 2006). Abstract: "As robot technology advances, we are approaching the day when robots will be deployed prevalently in uncontrolled, unpredictable environments: the proverbial 'real world.' As this happens, it will be essential for these robots to be able to adapt autonomously to their changing environment. For a robot to learn to improve its performance based entirely on real-world environmental feedback, the robot's behavior specification and learning algorithm must be constructed so as to enable data-efficient learning. This talk presents 3 examples of machine learning on physical robots. ..."

Automated Learning and Discovery State-Of-The-Art and Research Topics in a Rapidly Growing Field. By Sebastian Thrun, Christos Faloutsos, Tom Mitchell, and Larry Wasserman. AI Magazine 20(3): Fall 1999, 78-82. "This article summarizes the Conference on Automated Learning and Discovery (CONALD), which took place in June 1998 at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists concerned with decision making based on data. One of the meeting's focal points was the identification of promising research topics, which are discussed toward the end of this article."

AI on the Web: Machine Learning A resource companion to Stuart Russell and Peter Norvig's "Artificial Intelligence: A Modern Approach" with links to reference material, people, research groups, books, companies and much more.

"The Adaptive Systems Group at the Navy Center for Applied Research in Artificial Intelligence is performing state-of-the-art research in machine learning and robotics, with emphasis on techniques that will allow the creation of systems that are more adaptive to changes in their environment and to changes in their own capabilities." Check out their research projects, such as:

• Continuous and Embedded Learning (Anytime Learning): "Continuous and embedded learning is a general approach to continuous learning in a changing environment. ... The basic idea is to integrate two continuously running modules: an execution module and a learning module. This work is part of an ongoing investigation of machine learning techniques for solving sequential decision problems."

Applications of Machine Learning collection from the Alberta Ingenuity Centre for Machine Learning.

"Grammatical Inference, variously refered to as automata induction, grammar induction, and automatic language acquisition, refers to the process of learning of grammars and languages from data. Machine learning of grammars finds a variety of applications in syntactic pattern recognition, adaptive intelligent agents, diagnosis, computational biology, systems modelling, prediction, natural language acquisition, data mining and knowledge discovery. ... This homepage is designed to be a centralized resource information on Grammatical Inference and its applications. We hope that this information will be useful to both newcomers to the field as well as seasoned campaigners"

Index of Machine Learning Courses. Maintained by Vasant Honavar, Artificial Intelligence Research Group, Department of Computer Science, Iowa State University. When you visit the page for any given course, be sure to check out sections such as 'course readings' and 'additional resources' for you're sure to find plenty of gems there.

MLnet OiS. "Welcome to the MLnet Online Information Service (the successor of the ML-Archive at GMD). This site is dedicated to the field of machine learning, knowledge discovery, case-based reasoning, knowledge acquisition, and data mining. Get information about research groups and persons within the community. Browse through the list of software and data sets, and check out our events page for the latest calls for papers. Alternatively have a look at our list of job offerings if you are looking for a new opportunity within the field." This web site is funded by the European Commission. Here are some links to just a few of their collections:

• Applications of Machine Learning Methods
• Courses for Machine Learning, Knowledge Discovery, Data Mining
• Learning Methods, including: First Order Regression, Incremental decision tree learning, Naive Bayes, Neural Nets, Regression Rules, Star, Support Vector Machine (SVM) ... and many more.

Machine Learning at IBM. "The Machine Learning Group [Haifa] specializes in developing algorithms for automatic pattern recognition, prediction, analysis, classification, and learning of structures."

Machine Learning and Applied Statistics at Microsoft. "The Machine Learning and Applied Statistics (MLAS) group is focused on learning from data and data mining. By building software that automatically learns from data, we enable applications that (1) do intelligent tasks such as handwriting recognition and natural-language processing, and (2) help human data analysts more easily explore and better understand their data."

Machine Learning and Data Mining Group at the Austrian Research Institute for Artificial Intelligence (ÖFAI). Projects, publications, and more.

The Machine Learning Department, an academic department within Carnegie Mellon University's School of Computer Science and successor to CALD, the Center for Automated Learning and Discovery. "We focus on research and education in all areas of statistical machine learning."

• "What is Machine Learning? Machine Learning is a scientific field addressing the question 'How can we program systems to automatically learn and to improve with experience?' We study learning from many kinds of experience, such as learning to predict which medical patients will respond to which treatments, by analyzing experience captured in databases of online medical records. We also study mobile robots that learn how to successfully navigate based on experience they gather from sensors as they roam their environment, and computer aids for scientific discovery that combine initial scientific hypotheses with new experimental data to automatically produce refined scientific hypotheses that better fit observed data. To tackle these problems we develop algorithms that discover general conjectures and knowledge from specific data and experience, based on sound statistical and computational principles. We also develop theories of learning processes that characterize the fundamental nature of the computations and experience sufficient for successful learning in machines and in humans."
• Be sure to check out their collections of current research projects and past research projects.

Machine Learning Dictionary. Compiled by Bill Wilson, Associate Professor in the Artificial Intelligence Group, School of Computer Science and Engineering, University of NSW. "You should use The Machine Learning Dictionary to clarify or revise concepts that you have already met. The Machine Learning Dictionary is not a suitable way to begin to learn about Machine Learning."

Machine Learning in Games. Maintained by Jay Scott. "How computers can learn to get better at playing games. This site is for artificial intelligence researchers and intrepid game programmers. I describe game programs and their workings; they rely on heuristic search algorithms, neural networks, genetic algorithms, temporal differences, and other methods. I keep big list of online research papers. And there's more."

Machine Learning and Inference (MLI) Laboratory at George Mason University (GMU) "conducts fundamental and experimental research on the development of intelligent systems capable of advanced forms of learning, inference, and knowledge generation, and applies them to real-world problems."

Machine Learning Resources. Maintained by David Aha. Links to a wealth of information await you at this site.

The Machine Learning Systems (MLS) Group at the Jet Propulsion Laboratory, California Institute of Technology. Read about projects such as OASIS, the Onboard Autonomous Science Investigation System: "Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. This circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions."

"Sodarace [a joint venture between: soda and queen mary, university of london] is the online olympics pitting human creativity against machine learning in a competition to construct virtual racing robots. ... Sodarace is not just for fun. It is a shared competition for Artificial Intelligence researchers to test their learning algorithms while also being a play space in which to communicate the benefits of Artificial Intelligence research with a wide audience and promote a creative exploration of physics and engineering."

• ML Programs. You'll find FOCL, Hydra, and others.
• Repository. "This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
• Research. "Machine learning investigates the mechanisms by which knowledge is acquired through experience. ... Our research involves the development and analysis of algorithms that identify patterns in observed data in order to make predictions about unseen data. New learning algorithms often result from research into the effect of problem properties on the accuracy and run-time of existing algorithms."

Machine Learning is an international forum for research on
computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to:

Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.
Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning.

logical learning

logical

learning style

If you use the logical style, you like using your brain for logical and mathematical reasoning. You can recognize patterns easily, as well as connections between seemingly meaningless content. This also leads you to classify and group information to help you learn or understand it.

You work well with numbers and you can perform complex calculations. You remember the basics of trigonometry and algebra, and you can do moderately complex calculations in your head.

You typically work through problems and issues in a systematic way, and you like to create procedures for future use. You are happy setting numerical targets and budgets, and you track your progress towards these. You like creating agendas, itineraries, and to-do lists, and you typically number and rank them before putting them into action.

Your scientific approach to thinking means you often support your points with logical examples or statistics. You pick up logic flaws in other peoples words, writing or actions, and you may point these out to people (not always to everyone’s amusement).

You like working out strategies and using simulation. You may like games such as brainteasers, backgammon, and chess. You may also like PC games such as Dune II, Star craft, Age of Empires, Sid Meier games and others.

Common pursuits

People with a strong logical style are likely to follow such pursuits as the sciences, mathematics, accounting, detective work, law and computer programming.

Common phrases

You are more likely to use phrases that reflect your most dominant style out of the visual, aural or physical styles, however you may also use phrase like these:

• That’s logical.
• Follow the process, procedure, or rules.
• There’s no pattern to this.
• Let’s make a list.
• We can work it out.
• Quantify it, or prove it!

Learning and techniques

If you are a logical learner, aim to understand the reasons behind your content and skills. Don’t just rote learn. Understanding more detail behind your compulsory content helps you memorize and learn the material that you need to know. Explore the links between various systems, and note them down.

While you study, create and use lists by extracting key points from your material. You may also want to use statistics and other analysis to help you identify areas you may want to concentrate on.

Pay attention to your physical state, for example your breathing and stress level. It’s possible that you isolate your own body from your rational thought. Remember that you are just as much a part of the “system” as any equipment you may be using.

Also remember that association often works well when it is illogical and irrational. It doesn’t matter how logical two items are together. You have a better chance of recalling them later if you have make the association illogical. Your brain may protest at first!

In your scripting though, highlight logical thoughts and behaviors. Highlight your ability to pick up systems and procedures easily, and that you can detect when you need to change a set procedure.

Make use of “systems thinking” to help understand the links between various parts of a system. An important point here is that systems thinking helps you understand the bigger picture. Often the whole is greater than the sum of the parts. For example, you may understand the individual aircraft systems and flight surfaces, but you may not have a view of how all those systems support flight in equilibrium. Systems diagrams can help you gain that understanding.

You may find it challenging to change existing behaviors or habits. You can rationalize all you want to about why you should change a behavior, but you may find it persists. Try the shunt technique to understand what behavior you currently have and what behavior you want to have. When you understand those behaviors, use the technique to divert from the old behavior to the new.

You may sometimes overanalyze certain parts of your learning or training. This can lead to analysis paralysis. You may be busy, but not moving towards your goal. If you find you are overanalyzing which school to start with, or you are over-planning your course maps, stop and refocus on activities that move you forward. Consider how much “bang for buck” you get from spending more time than necessary. Measure your activities by your speed towards your goal. Planning exactly how much time to spend on each chapter of theory doesn’t help learn it anywhere near as fast as starting on the theory!

If you often focus from analysis paralysis, write “Do It Now” in big letters on some signs or post-it notes. Place them in strategic places around your work or study area.

e-learning

Electronic learning or eLearning is a general term used to refer to computer-enhanced learning. It is used interchangeably in so many contexts that it is critical to be clear what one means when one speaks of 'eLearning'. In many respects, it is commonly associated with the field of advanced learning technology (ALT), which deals with both the technologies and associated methodologies in learning using networked and/or multimedia technologies.

Market

The worldwide e-learning industry is estimated to be worth over 38 billion euros according to conservative estimates, although in the European Union only about 20% of e-learning products are produced within the common market. Developments in internet and multimedia technologies are the basic enabler of e-learning, with content, technologies and services being identified as the three key sectors of the e-learning industry.

Growth of e-learning

By 2006, nearly 3.5 million students were participating in on-line learning at institutions of higher education in the United States. Many higher education, for-profit institutions, now offer on-line classes. By contrast, only about half of private, non-profit schools offer them. The Sloan report, based on a poll of academic leaders, says that students generally appear to be at least as satisfied with their on-line classes as they are with traditional ones. Private Institutions may become more involved with on-line presentations as the cost of instituting such a system decreases. Properly trained staff must also be hired to work with students on-line. These staff members must be able to not only understand the content area, but also be highly trained in the use of the computer and Internet. Online education is rapidly increasing, and online doctoral programs have even developed at leading research universities.

Technology

Most eLearning situations use combination of the above techniques.

An example of this is moodle which use: discussion board threading, wiki and real time textual chat. However, moodle is referred to a CMS, this is because course material if often video, mp3, text documents, scanned images or links to other web sites.

Along with the terms learning technology and Educational Technology, the term is generally used to refer to the use of technology in learning in a much broader sense than the computer-based training or Computer Aided Instruction of the 1980s. It is also broader than the terms On-line Learning or Online Education which generally refer to purely web-based learning. In cases where mobile technologies are used, the term M-learning has become more common.

E-learning is naturally suited to distance learning and flexible learning, but can also be used in conjunction with face-to-face teaching, in which case the term Blended learning is commonly used.Typical Managed Learning Environment with a navigation menu and icons giving access to automated tools and content pages.

In higher education especially, the increasing tendency is to create a Virtual Learning Environment (VLE) (which is sometimes combined with a Management Information System (MIS) to create a Managed Learning Environment) in which all aspects of a course are handled through a consistent user interface standard throughout the institution. A growing number of physical universities, as well as newer online-only colleges, have begun to offer a select set of academic degree and certificate programs via the Internet at a wide range of levels and in a wide range of disciplines. While some programs require students to attend some campus classes or orientations, many are delivered completely online. In addition, several universities offer online student support services, such as online advising and registration, e-counselling, online textbook purchase, student governments and student newspapers.

e-Learning can also refer to educational web sites such as those offering learning scenarios, worksheets and interactive exercises for children. The term is also used extensively in the business sector where it generally refers to cost-effective online training.

Services

E-learning services have evolved since computers were first used in education. There is a trend to move toward blended learning services, where computer-based activities are integrated with practical or classroom-based situations.

Goals of e-learning

E-Learning lessons are generally designed to guide students through information or to help students perform in specific tasks. Information based e-Learning content communicates information to the student. Examples include content that distributes the history or facts related to a service, company, or product. In information-based content, there is no specific skill to be learned. In performance-based content, the lessons build off of a procedural skill in which the student is expected to increase proficiency.

Computer-based learning

Computer Based Learning, sometimes abbreviated CBL, refers to the use of computers as a key component of the educational environment. While this can refer to the use of computers in a classroom, the term more broadly refers to a structured environment in which computers are used for teaching purposes. The concept is generally seen as being distinct from the use of computers in ways where learning is at least a peripheral element of the experience (e.g. computer games and web browsing).

Computer-based training

Computer-based training (CBT) services are where a student learns by executing special training programs on a computer relating to their occupation. CBT is especially effective for training people to use computer applications because the CBT program can be integrated with the applications so that students can practice using the application as they learn. Historically, CBTs growth has been hampered by the enormous resources required: human resources to create a CBT program, and hardware resources needed to run it. However, the increase in PC computing power, and especially the growing prevalence of computers equipped with CD-ROMs, is making CBT a more viable option for corporations and individuals alike. Many PC applications now come with some modest form of CBT, often called a tutorial. Web-based training (WBT) is a type of training that is similar to CBT; however, it is delivered over the Internet using a web browser. Web-based training frequently includes interactive methods, such as bulletin boards, chat rooms, instant messaging, videoconferencing, and discussion threads. WBT is usually a self-paced learning medium though some systems allow for online testing and evaluation at specific times.

Pedagogical elements

Pedagogical elements are an attempt to define structures or units of educational material. For example, this could be a lesson, an assignment, a multiple choice question, a quiz, a discussion group or a case study. These units should be format independent, so although it may be implemented in any of the following methods, pedagogical structures would not include a textbook, a web page, a video conference or an iPod video.

When beginning to create eLearning content, the pedagogical approaches need to be evaluated. Simple pedagogical approaches make it easy to create content, but lack flexibility, richness and downstream functionality. On the other hand, complex pedagogical approaches can be difficult to set up and slow to develop, though they have the potential to provide more engaging learning experiences for students. Somewhere between these extremes is an ideal pedagogy that allows a particular educator to effectively create educational materials while simultaneously providing the most engaging educational experiences for students.

Communication technologies used in e-learning

Communication technologies are generally categorized as asynchronous or synchronous. Asynchronous activities use technologies such as blogs, wikis, and discussion boards. The idea here is that participants may engage in the exchange of ideas or information without the dependency of other participants involvement at the same time. Electronic mail (Email) is also asynchronous in that mail can be sent or received without having both the participants’ involvement at the same time.

Synchronous activities involve the exchange of ideas and information with one or more participants during the same period of time. A face to face discussion is an example of synchronous communications. Synchronous activities occur with all participants joining in at once, as with an online chat session or a virtual classroom or meeting.

Virtual classrooms and meetings can often use a mix of communication technologies.

physical education

In most educational systems, physical education (P.E.) class, also called physical training (PT), though each with a very different connotation, is a course in the curriculum which utilizes learning in the cognitive, affective and psychomotor domains in a play or movement exploration setting. The term physical education is most commonly used in this way; however, this denotes rather that "they have participated in the subject area, not studied it."

The primary aims of physical education vary historically, based on the needs of the time and place. Often, many different types of physical education occur simultaneously, some intentionally and others not. Most modern school systems claim their intent is to equip students with the knowledge, skills, capacities, and values along with the enthusiasm to maintain a healthy lifestyle into adulthood. Some schools also require physical education as a way to promote weight loss in students. Activities included in the program are designed to promote physical fitness, to develop motor skills, to instill knowledge and understanding of rules, concepts, and strategies, and to teach students to work as part of a team, or as individuals, in a wide variety of competitive activities.

School curriculum

Physical education is that phase of education which through the median of motor activity guides an individual to a successful solution to everyday living

In the United States, the physical education curriculum is designed to allow pupils exposure to the following categories of activities: aquatics, conditioning activities, gymnastics, individual/dual sports, team sports, rhythms, and dance. Some martial arts classes, like wrestling in the United States, and Pencak Silat in France, are also offered to teach students self-defense and to feel good about themselves. On July 18, 1975, the United States House of Representatives voted to require school physical education classes include both genders. Some high school and some middle school PE classes are single-sex

Clothing

The majority of schools require pupils to change into a different set of trainers or go barefoot. Some schools require students to change into athletic clothes of their own choosing while others require a uniform. A common uniform consists of a white t-shirt and shorts in the school color, but this is not a universal rule. At some catholic schools the children have to wear shorts to the knee. For safety, some schools require boys / men to wear a Cup as part of the physical education uniform. Some schools allow male students to go bare chested when they are outside during a hot day. Most uniformed classes require the student to label their name on their clothes.

Occasionally, certain activities will require a special uniform. For example, some schools require swimming as part of the physical education curriculum. In this case, students typically have to wear a bathing suit in either the school color or black with a swimming cap of the same color. In the past it wasn't uncommon for schools to allow or require male or female students to swim in the nude if the class was single-sex. Also, in games with two or more teams, students usually have to wear colored jerseys or cape-like garments over the usual uniform as a way of identifying team member.

A Canadian Initiative: Daily Physical Activity (DPA)

Starting in September 2005, all Alberta students from grades one to nine are to be active for at least 30 minutes a day. Daily physical activity (DPA) aims to increase physical activity levels among students in order to curb increasing rates of obesity and chronic diseases. A physical education class counts towards the 30-minute daily minimum or physical activity can be incorporated throughout the school day and integrated into other subjects.

DPA and daily physical education (DPE) are different concepts. Physical activity is body movement that expends energy, while physical education is a school subject designed to help children and youth develop the skills, knowledge, and attitudes needed to participate in active, healthy living.

The goal of daily physical activity (DPA) is to increase students’ physical activity levels. Schools have been given a great deal of flexibility to find creative ways to provide thirty minutes of daily physical activity for all grades one to nine students. DPA is important for all schools. Healthy students are better able to learn, and supportive environments help students develop positive habits for a healthy, active lifestyle

TIME TAKEN BY A COURSE

Different types of adapting learning materials include the quantity and quality of material available; the expertise of those adapting the material; the course delivery format and length; and whether the material has been sourced from another region. According to Phil Race in the Open Learning Handbook, writing a course could take ten hours for each hour of instruction.

Recent survey says, this author conducted a Canada-based survey that found the cost of preparing an online distance course based on lecture-based material was about one-third less than preparing the course from scratch, and that it took six to nine months to produce a distance course by adapting existing materials. Using nteractive learning objects from a learning object repository (LOR) reduced course development time, yet produced an interactive and engaging course. If there was extensive media use, such as video, animations and simulations, it took up to two years to produce a quality course. The less media material involved, the shorter the adaptation time, but the resulting course material had less interactivity—a key ingredient of quality courses.

Adapt Materials for experts

Adapting traditional classroom materials for distance delivery requires the specific expertise of a course development team.

Project co-ordinator: Provides overall project management. Frequently, the course developer or the instructional designer takes on this role.

Advisory committee: Oversees project planning, validates the course and evaluates project outcomes, especially if the program in which the courses will be used is new to the institution.

Course developer: Converts existing course materials for DE, including research, sourcing, development and writing of course content, activities, evaluations and media material. Ideally, the course developer is a content expert familiar with learners and instructional methods.

Instructional designer: Assists course developers with developing the course and identifying delivery methods based on learning and instructional needs. Checks that the course adheres to a specified standard or meets required criteria.

Course reviewer: Verifies academic content and methodology. Frequently an external reviewer is appointed but the reviewer may be an in-house instructor or chairperson familiar with programme content and the intended learners.

Clerical personnel: Help the course developer with word processing and formatting course materials; frequently the course developer takes on this role.

Intellectual property officer: Obtains clearances for copyrighted material.

Editor: Checks course material for meaning, organisation, grammar, style, bias and accuracy.

Media personnel: Help produce required media course components, such as illustrations, photographs and streaming Internet video.

Course evaluator: Develops and analyses student and tutor questionnaires and recommends course material changes.