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From Wikipedia
A learning curve is a graphical representation of the changing rate of learning (in the average person) for a given activity or tool. Typically, the increase in retention of information is sharpest after the initial attempts, and then gradually evens out, meaning that less and less new information is retained after each repetition.
The learning curve can also represent at a glance the initial difficulty of learning something and, to an extent, how much there is to learn after initial familiarity. For example, the Windows program Notepad is extremely simple to learn, but offers little after this. On the other extreme is the UNIX terminal editor vi, which is difficult to learn, but offers a wide array of features to master after the user has figured out how to work it. It is possible for something to be easy to learn, but difficult to master or hard to learn with little beyond this.
Learning curve in psychology and economics
The first person to describe the learning curve was Hermann Ebbinghaus in 1885. He found that the time required to memorize a nonsense word increased sharply as the number of syllables increased. Psychologist Arthur Bills gave a more detailed description of learning curves in 1934. He also discussed the properties of different types of learning curves, such as negative acceleration, positive acceleration, plateaus, and ogive curves. In 1936, Theodore Paul Wright described the effect of learning on labor productivity in the aircraft industry and proposed a mathematical model of the learning curve.
The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects. Efficiency and productivity improvement can be considered as whole organization or industry or economy learning processes, as well as for individuals. The general pattern is of first speeding up and then slowing down, as the practically achievable level of methodology improvement is reached. The effect of reducing local effort and resource use by learning improved methods paradoxically often has the opposite latent effect on the next larger scale system, by facilitating its expansion, or economic growth, as discussed in the Jevons paradox in the 1880s and updated in the KhazzoomBrookes Postulate in the 1980s.
Broader interpretations of the learning curve
Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as "experience curve", "improvement curve", "cost improvement curve", "progress curve", "progress function", "startup curve", and "efficiency curve" are often used interchangeably. In economics the subject is rates of "development", as development refers to a whole system learning process with varying rates of progression. Generally speaking all learning displays incremental change over time, but describes an "S" curvewhich has different appearances depending on the time scale of observation. It has now also become associated with the evolutionary theory ofpunctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields. These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.
Common terms
The familiar expression "steep learning curve" may refer to either of two aspects of a pattern in which the marginal rate of required resource investment is initially low, perhaps even decreasing at the very first stages, but eventually increases without bound.
Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning (as measured by, e.g., memory accuracy or the number of trials required to obtain a desired result) at the introductory or elementary stage. Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject.
In the former case, the "steep[ness]" metaphor is inspired by the initially high rate of increase featured by the function characterizing the overall amount learned versus total resources invested (or versus time when resource investment per unit time is held constant)â€”in mathematical terms, the initially high positive absolute value of the first derivative of that function. In the latter case, the metaphor is inspired by the pattern's eventual behavior, i.e., its behavior at high values of overall resources invested (or of overall time invested when resource investment per unit time is held constant), namely the high rate of increase in the resource investment required if the next item is to be learnedâ€”in other words, the eventually alwayshigh, alwayspositive absolute value and the eventually neverdecreasing status of the first derivative of that function. In turn, those properties of the latter function dictate that the function measuring the rate of learning per resource unit invested (or per unit time when resource investment per unit time is held constant) has a horizontal asymptote at zero, and thus that the overall amount learned, while never "plateauing" or decreasing, increases more and more slowly as more and more resources are invested.
This difference in emphasis has led to confusion and disagreements even among learned people.
The most effective solution to problems arising from a steep learning curve is to find a different method of learning that features a differently shaped (or at least less steep) curve. Such a discovery, often characterized as an aha!' moment" or "breakthrough", often results from a seemingly radical intuitive change in direction.
Learning
From Yahoo Answers
Answers:< ogive is the graph/curve of the less than cumulative frequency distribution which shows the number of observations LESS THAN the upper class boundary/class limit. > ogive, on the other hand, is the graph/curve of the greater than cumulative frequency distribution which shows the number of observations GREATER THAN the lower class boundary/class limit.
Answers:rib in Gothic vault: a diagonal rib in a Gothic vault  pointed arch: an arch that rises to a sharp point  cumulative frequency graph: a graph or curve that represents the cumulative frequencies of a set of values
Answers:cumulative frequency (cf) is the sum of all frequencies before that particular class for example u have a table like this: class f cf intervals 0010 3 3 1020 2 3+2=5 2030 7 3+2+7=12 3040 9 3+2+7+9=21 so cf of 010 is 3 cf of 1020 is 5 cf of 2030 is 12 cf of 3040 is 21 cf usually means this. if u r in the 10th grade or something then read on. the above table can also be written like this: 1. less than 10 3 less than 20 5 less than 30 12 less than 40 21 u can see that 10, 20, 30 and 40 are the 'upper limits' of the class intervals. if u draw a graph with 10, 20, 30 and 40 on the xaxis and 3, 5, 12 and 31 on the yaxis, u will get a curved or straight line called 'less than ogive' or else like this: 2. more than 0 3+2+7+9=21 more than 10 2+7+9=18 more than 20 7+9=16 more than 30 9 if u draw a graph with 0, 10 , 20, and 30 on x axis and 21, 18, 16 and 9 on yaxis u will get a line called 'more than ogive'
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