Analysing The Political Effect Of The Olympics Politics Essay

Analysing The Political Effect Of The Olympics Politics Essay The Olympic games are supposed to unite the world; they were created as a tool to help connect individual countries; they are a way to put politics behind us and compete athletically; however the summer Olympic games in 1980 and 1984, held in Moscow and Los Angeles respectively, showed that they could be used as leverage when one country disagreed with another. The 1979 invasion of Afghanistan, the Cold War, and disagreements among leaders of different countries played a role in the boycott of these Olympic Games. The tensions between the United States and the Soviet Union as well as their boycott of the Olympic Games in the 1980s will be analyzed in this report. After World War II had come and gone a conflict arose between the United States and the Soviet Union. This conflict is believed to have lasted all the way into the early 1990s when the Soviet Union collapsed. The war was passed down from leader to leader in each country. It was not a conventional war but instead was mostly fought with threats. Each country would use the media to condemn the other. It is unknown how long the war lasted. This is a huge debate among many historians (Cold War). The only post World War II super powers were the Soviet Union and the United States. Each country began investing time and money in the development of nuclear weapons. This led to what is known as a nuclear arms race. Each side tried to develop more nuclear weapons than the other. This dangerous situation led to the each country using words to fight instead of weapons. They were afraid of the outcome if nuclear weapons were used (Cold War). As a result, tensions were high between the United States and the Soviet Union. The Soviet Union tried to make peace with the rest of Eastern Europe even though they had been invaded by some of the Eastern Europe nations in both World War I and World War II. They used their communist influence in liberating nations of Eastern Europe after World War I. As a result, the world began to see other communist nations in Eastern Europe. They hoped that this would protect their borders from future invasion (Cold War). This also helped unite Eastern Europe and led to more nations supporting the Soviet Union. Afghanistan is a country in the Middle East just west of Pakistan. A country with more than 28 million people, as of a July 2009 estimate by the U.S. Department of State, its size is just smaller than the state of Texas (Afghanistan). In 1979, after the Afghan Prime Minister Hafizullah Amin refused to cooperate with the Soviet Union on how to stabilize the government, the Soviets invaded Kabul. Once they landed in the countrys capital they killed Hafizullah Amin and replaced him by with a new man chosen by them as the new Prime Minister (Afghanistan). The new leader needed the Russian army to maintain control of the government because the mostly Muslim Mujahedeen was fighting back. As a result of the Mujahedeens resistance the Soviets were only able to keep control of the major cities while 75% of the country was controlled by the Mujahedeen (Soviet Invasion of Afghanistan). The United States, in the midst of the Cold War with the Soviet Union, denounced the Soviet invasion of Afghanistan. They did not want the Soviet Union to spread communism any further than they already had in the rest of Eastern Europe. The U.S. supported the Mujahedeen in their efforts against the Soviet Union. They supplied them with money and various weapons, and also began to use the term freedom fighters to describe them. This was done in spite of the Soviets claiming that they did not invade Afghanistan but they were invited by the Prime Minister. They also said that they were there to support a legitimate government and the Mujahedeen were just terrorists (Soviet Invasion of Afghanistan). The invasion took place just months before the 1980 Olympic Games in Moscow. President Jimmy Carter informed the American people of the boycott in his January 23, 1980 State of the Union Address. Saudi Arabia was the first to boycott the 1980 Olympic Games due to the invasion taking place on Islamic Land. They were backed up by many other countries including Canada, West Germany, Japan, the United States, and Israel. In total 60 other countries joined Saudi Arabia including the United States. Many cited the Soviet invasion of Afghanistan as the main cause for their boycott. However, some did say that they did to not participate due to economic reasons (Tristham). Four years later the 1984 Olympic Games were held in Los Angeles. The Soviet Union issued a statement, chauvinistic sentiments and an anti-Soviet hysteria being whipped up in the United States. They decided to boycott the Olympics due to those reasons. Thirteen other Soviet Allies also choose to boycott the Los Angeles Olympics. Iran was the only country to boycott both the 1980 and 1984 Olympics (Olympic Boycott History). That same year the Soviet Union organized the Druzhba Games. Other countries, which boycotted the 1984 Olympics, participated in the event. The motto of these games was Sport, Friendship, Peace (Olympic Boycott History). The Soviet Union pulled out of Afghanistan in 1989. That same year the Berlin Wall collapsed, which marked an end to a communist Germany. Two years later the Soviet Union also collapsed and the United States was able to begin to establish good relations with Russia. Although much has changed between the United States and Russia it is not hard to see how the tension of a Cold War and a Soviet invasion of Afghanistan led to a boycott of the Olympics. The Olympics are a time to put the politics behind us and unite in sport. As shown in the 1980 and 1984 Olympic Games, this is not always the case.

The Old Man And Sea :: Essays Papers

The Old Man And Sea Manzanares, March 21th of 1999. The Old Man And The Sea What is the title of the story? A= The Old Man and the Sea. Who is the main character? A= . Santiago (The Old Man) is the main character of The Old Man and the Sea. His occupation is a fisherman. Unlike the rest of the fishing community, Santiago continues to fish using traditional methods. These methods, however, do not allow Santiago to catch many fish. Thus, he is forced to live a semi-impoverished life Who is the secondary character? A= Manolin (the young boy) is a young man and good friend of Santiago. Santiago has spent several years teaching and instructing Manolin in the traditional methods of fishing. Where and when the story takes place? A= In Cuba and out in the Gulf Stream, in the 50’. What is the climax of the story? A= During the last few moments of the Marlin's life. Santiago battles furiously with the huge fish as it thrashes about in the water. The danger to Santiago is immense because the size of the mar lin is much greater than the Santiago's boat. Did you like the story? Why? A= Yes because is about the hard existence of the man fighting against his destiny, conditioned by the social and cultural structures that mark his life. Do a summary of the story A= The story is about Santiago a Cuban fisherman who goes through many conflicts with nature and himself. He experiences poor luck in the latter part of his life which leaves him poor and destitute, relying on a boy to feed him and to be his only true friend. In spite of his skill as a fisherman, only his diligent perseverance ended his eighty-five day drought of fish. In this time of need, Santiago's pride prevailed over his hunger and need of supplies. While fishing in solitude, Santiago's eighty-five day ordeal ended with the snaring of a marlin. During the contest between himself and the fish, Santiago had to endure many physical and emotional conflicts. Santiago's physical conflicts include his hunger, fatigue, and the cramping of his hand. His body required nutrition and became tired and thirsty, inflicting great pain and demanding his attention. The obtaining of nourishment was a task which required all his skills and physical strength while at the same time holding a line with a marlin larger than any he had ever seen.

Public Grazing on Bureau of Land Management Land :: Agriculture Farming Environment Essays Papers

Public Grazing on Bureau of Land Management Land The Bureau of land Management is an agency of the department of the Interior. It manages 264 million acres in the western lands and over 700 acres of mineral estate nationwide. The purposes of these lands are mineral development, recreation, timber, and grazing. The on that we are going to talk about is grazing on the BLM lands and how they are improving them. In the 1930's, overgrazing was damaging the Western rangelands to a dust bowl. In Wyoming during 1909 the sheep numbers reached six million. Most of these sheep operations were nomadic, with that meant that some of these operations were keeping their sheep on public land all year round. The range land became deteriorated bye this way of grazing. By the 1920's and 1930's the ranchers and the conservationists wanted something to be done before the land got any worse. Congress knew that they had to do something before they lost their country's biggest asset. The Taylor Grazing Act (TGA) of 1934 was passed. What the TGA did was regulated grazing on public lands through using permits. With regulation of public lands they could control numbers of occupancy and uses on the land. It also could preserve the land from destruction, with that it could improve the land and develop it better. In 1964 Public Land Law Review Commission was established to make recommendations on how to manage the lan d. Congress responded to that by passing the Federal Land Policy and Management Act (FLPMA) in 1976, which keeps the lands in Federal ownership. The Public Rangelands Improvement Act of 1978 was another act that improved rangelands. It realized that public rangelands were producing less than their potential. This act helps maintain and improve the conditions of the rangelands so that they become productive and usable to their highest potential again. The Executive Order 12548 of 1986, signed by President Reagan, stated that there would be annual fees for domesticated livestock grazing on public rangelands. Just in Oregon and Washington the federal government will receive over $1.8 million annually for grazing about 250,000 animals on BLM land. The BLM has improved the rangeland in Oregon by one hundred percent. With the Oregon Trail having immigrants and their cattle coming through, it destroyed the land with no grass left to graze. The BLM scattered cattle throughout the land and the grazing has improved, so has the water development.

Freaky Friday Essay examples -- essays research papers

â€Å"Freaky Friday† The movie that I chose to review was titled â€Å"Freaky Friday.† It stars Jamie Lee Curtis and Lindsay Lohan as a mother and daughter who switch bodies for a day. In this film, Tess Coleman (played by Jamie Lee Curtis) is a widowed psychiatrist juggling her job and family while planning her second marriage. Anna Coleman (played by Lindsay Lohan), who disapproves of her mother’s second marriage plans, is of no help to her mother at all during her stressful situations. Anna is a rebellious rocker who plays guitar in a garage band and would rather flirt with older boys than listen to her uptight mother. One night, while the warring mother and daughter are at a Chinese restaurant, their fighting is overheard by an elderly Chinese grandmother who curses a fortune cookie, so that the angry mother and daughter will wake up the next morning in each other's bodies. Due to accepting and ingesting the fortune cookie, both Tess and Anna are there by forced to live in each other’s bodies for the day, in which it just so happens to be the day of Tess’s rehearsal dinner and Anna’s band audition at the House Of Blues. Of course, once Tess and Anna change places, they discover that the opposite person really does not have an easier life. For instance, Anna must listen to a litany of patient woes and panic at appointments while in the body of her mother and Tess gets bullied at school and must take a school placement exam while in the body of her teenage daughter. This Disney m...

From the Farm, Inc Marketing Plan Essay

The purpose of this paper is to determine how to drive more sales to From the Farm’s website as well as increase overall brand awareness through the use of targeted marketing and advertising programs and also to gain a deeper insight of prospective customers which can assist in forming target market segments and creating targeted marketing and advertising programs that cater to those segments. Furthermore, we need to determine how to remain cost-effective with these proposed improvements to the marketing programs. Being a small e-commerce startup with limited funds and personnel, FTF has struggled for over four years to create effective marketing programs and have seen very little positive impact of their past marketing efforts. The lack of an effective marketing strategy which includes targeted advertising has led to increased and somewhat unnecessary marketing costs because several of the marketing campaigns are built on the premise of â€Å"testing it out† and seeing what kind of response it gets. The implementation of targeted marketing and advertising programs can not only increase From the Farm’s sales revenue, but also be more cost-effective since the campaigns are targeted and relevant to their audience. In addition, an improved digital marketing strategy can help FromTheFarm. com improve their overall market positioning due to the fact that they will gain a better understanding of their customers through the data that is collected and thus enable them to build a more effect online marketing strategy. Furthermore, this enhanced insight of their customers can also enable them to improve their product development and product offerings on their website. All of these things combined can definitely contribute to growing their sales revenue and more importantly, by having targeted marketing and advertising programs, they can ensure that every dollar spent is not a dollar wasted. Company Background From The Farm, Inc. (FTF) is a privately-owned e-commerce company headquartered in Stockton, California which specializes in the sales and home delivery of gourmet and organic foods and produce. FTF was founded in 2008 with the purpose and intent of becoming â€Å"America’s Online Farmer’s Market†. According to a report done by the United States Department of Agriculture (USDA), farmers only earned an average of $0. 16 for every dollar spent on the food they produced (Canning, 2011). FTF’s mission is to provide an online marketplace which connects customers with American Family farms by offering farm-fresh products and other specialty food items delivered straight to their door. By allowing customer to purchase directly from the farmer though the website, they cut out the middle man, and therefore give customers the opportunity to taste and experience what truly fresh food and produce tastes like while also supporting the success and livelihood of hard-working farmers all over the county. FromTheFarm. com is currently funded by its parent company, Onions, Etc. , one of the largest onion distributors in the United States. Currently, FTF only has two full-time employees – the Chief Marketing Officer (CMO)/General Manager and a Marketing Specialist – in addition to the Founder/CEO, one marketing intern, and one contract-based Marketing/PR Consultant. Any other tasks with regards to finance and accounting are handled by Onions, Etc. personnel and all IT related tasks are outsourced to an outside IT firm and web design firm. FTF has undergone major organizational changes in the past twelve months, in an effort to restructure the marketing team with the intention of implementing an improved marketing strategy in order to grow sales and increase brand awareness. Due to limited funds and personnel, FTF has struggled to create an effective targeted marketing and advertising strategy and as a result have not seen much growth since being founded in 2008. 1. 0 Market Summary and Target Audience Being that From the Farm is a small, family-owned company they do not have the financial strength that other larger food retailers have; however they still have a tremendous opportunity to capture the market being that the food e-commerce market has yet to be penetrated. According to a recent report by eMarketer, as of 2012, U. S. -commerce sales have grown to $224. 2 billion and are expected to grow to $361. 9 billion by 2016. Currently, online food and beverage sales is the smallest U. S. e-commerce category, however, this segment reached sales of $5. 09 billion and experienced a 17% growth in 2012 (eMarketer, 2012). Another report by Nielsen indicates that the sales rate for consumer packaged goods online is expected to reach $25 billion by 2014. This t rending growth can be attributed to the fact that more and more consumers are beginning to do their grocery shopping online (Nielsen, 2011). FTF can definitely capitalize on this emerging trend since the online grocery shopping experience is primarily fueled by a needs-driven experience since there are a greater variety of options available online. In addition, e-commerce allows for smaller companies such as FTF to compete against â€Å"Big Brand† companies since the big brand physical advantages become nonexistent and opens up the opportunity to create a niche brand for customers who prefer to buy their food and groceries online. With a creative and effectively targeted market program, FTF can reach a significant amount of customers online and leverage unique and exclusive products such as tropical fruits, figs, Piedmontese beef, and fresh cherries to capture these customers. There is a huge opportunity to capitalize on the available internet marketing technologies being that there is so much data available through these marketing channels and the fact that many consumers are turning to e-commerce to purchase goods. According to a recent Digital Marketing Report by eMarketer, â€Å"88. % of US internet users ages 14 and up will browse or research products online in 2012, an 83. 9% of that group will make at least one purchase via the web during that year† (Peart, Utreras, & Wang, 2011). Target Market Since From the Farm is a food e-commerce company, it is easy to assume that this website and its products can appeal to the masses. There is a large assortment of foods from fresh fruit and produce includi ng exotic tropical fruits to certified organic meats to an array of desserts which means that FTF has something to offer every kind of customer. FTF’s customers will consist of individuals who are 25 years old and up and have a wide range of preferences when it comes to food, whether it may be parents looking for healthy foods for their kids or health conscious individuals who prefer organic and gluten-free products or chefs and restaurant owners that need to order in bulk, From the Farm can accommodate a very diverse set of needs. Nonetheless, From the Farm’s products aren’t just for those who want to purchase these items for personal consumption because From the Farm also offers products that can be sent as gifts. As stated previously, From the Farm’s customer base is diverse since it consists of individuals with varying needs and preferences when it comes to food selections. The primary market that FTF will target is the online grocery shopper market. The profile of typical online shoppers is as follows: single or dual-income households with no children and are technically savvy, affluent, and time poor. This group consists of early adopters of new technology and is heavy internet users who regularly purchase goods online. Convenience is a main factor for this group and they have little to no concern about product price or delivery charges. The other major category within this market is families with young children. Similar to the previously discussed category, this category includes single parents, dual-income households, middle-income and above average-earning households. The key differentiator is that this category has one or more children, typically with at least one child under the age of five years old. This group consists of adults in their late 20s to 40s. The individuals in this category turn to online grocery shopping because it saves them time, is less hectic, and overall more convenient in nature. Other categories include college students and military families who are not located close to a standard-size store or who wish to purchase products found only in their home regions. In addition, the elderly, disabled and those individuals who find it difficult to get out of the house make up a significant share of online grocery shoppers. As such, the share of senior citizens and disabled individuals has grown over the past five years and is expected to continue growing in the future. Furthermore, online grocery shoppers are more than twice as likely as the average internet user to go online to read and post product reviews, download coupons and search for recipes, according to a study by the Nielsen Company from September 2009 (Panteva, 2012).

Week 9’s Final

Part One †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Write an essay of at least 700 words. Comprehensive writing skills must be used. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  The First Amendment to the Constitution bars Congress from infringing on the freedom of speech of the citizenry of the United States. It does not prohibit private restrictions on speech. With this in mind, many universities have over the years instituted speech codes or have banned hate-speech. If you were in charge of a university what rules would you make for student conduct online?Explain your reasoning and support your answer with examples and other evidence. If our legal reality truly reflected our political rhetoric about liberty, Americans and especially American college and university students would be enjoying a truly remarkable freedom to speak and express controversial ideas at the dawn of the twenty-first century. Virtually every public official declares a belief in â€Å"freedom of speech. † Politicians extol the virtues of freedom and boast of America’s unique status as a nation of unfettered expression.Judges pay homage to free speech in court opinions. Even some fringe parties’ communists and fascists who would create a totalitarian state if they were in power have praised the virtues of the freedom they need for their survival. Few individuals speak more emphatically on behalf of freedom of speech and expression, however, than university administrators, and few institutions more clearly advertise their loyalty to this freedom than universities themselves.During the college application process, there is a very high probability that you received pamphlets, brochures, booklets, and catalogs that loudly proclaimed the university’s commitment to â€Å"free inquiry,† â€Å"academic freedom,† â€Å"diversity,† â€Å"dialogue,† and â€Å"tolerance. â€Å"You may have believed these declarations, trusting th at both public and private colleges and universities welcome all views, no matter how far outside the mainstream, because they want honest difference and debate.Perhaps your own ideas were â€Å"unusual† or â€Å"creative. † You could be a liberal student in a conservative community, a religious student at a secular institution, or even an anarchist suffering under institutional regulations. Regardless of your background, you most likely saw college as the one place where you could go and hear almost anything—the one place where speech truly was free, where ideas were tried and tested under the keen and critical eyes of peers and scholars, where reason and values, not coercion, decided debate.Freedom and moral responsibility for the exercise of one’s freedom are ways of being human, not means adopted to achieve this or that particular point of view. Unfortunately, ironically, and sadly, America’s colleges and universities are all too often dedicate d more to censorship and indoctrination than to freedom and individual self-government. In order to protect â€Å"diversity† and to ensure â€Å"tolerance,† university officials proclaim, views deemed hostile or offensive to some students and some persuasions and, indeed, some administrators are properly subjected to censorship under campus codes.In the pages that follow, you will read of colleges that enact â€Å"speech codes† that punish students for voicing opinions that simply offend other students, that attempt to force religious organizations to accept leaders who are hostile to the message of the group, that restrict free speech to minuscule â€Å"zones† on enormous campuses, and that teach students sometimes from their very first day on campus that dissent, argument, parody, and even critical thinking can be risky business. Simply put, at most of America’s colleges and universities, speech is far from free.College officials, in betraying th e standards that they endorse publicly and that their institutions had, to the benefit of liberty, embraced historically, have failed to be trustees and keepers of something precious in American life. This  Guide  is an answer and, we hope, an antidote to the censorship and coercive indoctrination besetting our campuses. In these pages, you will obtain the tools you need to combat campus censors, and you will discover the true extent of your considerable free speech rights, rights that are useful only if you insist upon them.You will learn that others have faced and overcome the censorship you confront, and you will discover that you have allies in the fight to have your voice heard. The  Guide  is divided into four primary sections. This introduction provides a brief historical context for understanding the present climate of censorship. The second section provides a basic introduction to free speech doctrines. The third provides a series of real-world scenarios that demons trate how the doctrines discussed in this  Guide  have been applied on college campuses.Finally, a brief conclusion provides five practical steps for fighting back against attempts to enforce coercion, censorship, and indoctrination. Part Two †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Write an essay of at least 700 words. Comprehensive writing skills must be used. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Between 1949 and 1987, the Fairness Doctrine was an FCC rule designed to provide â€Å"reasonable, although not necessarily equal† opportunities in presenting opposing viewpoints in radio broadcasting in order to avoid one-sided presentations.The practice was repealed under President Reagan as part of a wider deregulation effort. Do you think the Fairness Doctrine should be revived, revised, or left dead? Why? The  Fairness Doctrine  was a policy of the United States  Federal Communications Commission  (FCC), introduced in 1949, that required the holders of  broadcast licenses  to both present controversial issues of public importance and to do so in a manner that was, in the Commission's view, honest, equitable and balanced.The FCC decided to eliminate the Doctrine in 1987, and in August 2011 the FCC formally removed the language that implemented the Doctrine The Fairness Doctrine had two basic elements: It required broadcasters to devote some of their airtime to discussing controversial matters of  public interest, and to air contrasting views regarding those matters. Stations were given wide latitude as to how to provide contrasting views: It could be done through news segments, public affairs shows, or editorials.The doctrine did not require equal time for opposing views but required that contrasting viewpoints be presented. The main agenda for the doctrine was to ensure that viewers were exposed to a diversity of viewpoints. In 1969 the  United States Supreme Court  upheld the FCC's general  right  to enf orce the Fairness Doctrine where channels were limited. But the courts did not rule that the FCC was  obliged  to do so. 3]  The courts reasoned that the scarcity of the broadcast spectrum, which limited the opportunity for access to the airwaves, created a need for the Doctrine. However, the proliferation of cable television, multiple channels within cable, public-access channels, and the Internet have eroded this argument, since there are plenty of places for ordinary individuals to make public comments on controversial issues at low or no cost. The Fairness Doctrine should not be confused with the  Equal Time  rule.The Fairness Doctrine deals with discussion of controversial issues, while the Equal Time rule deals only with political candidates. The Fairness Doctrine has been both defended and opposed on First Amendment grounds. Backers of the doctrine claim that listeners have the right to hear all sides of controversial issues. They believe that broad-casters, if left alone, would resort to partisan coverage of such issues. They base this claim upon the early history of radio.Opponents of the doctrine claim the doctrine's â€Å"chilling effect† dissuaded broadcasters from examining anything but â€Å"safe† issues. Enforcement was so subjective, opponents argued, there was never a reliable way to determine before the fact what broadcasters could and could not do on the air without running afoul of the FCC. Moreover, they complain, print media enjoy full First Amendment protection while electronic media were granted only second-class status. I'll be honest, I'd never even heard of the Fairness Doctrine until I read this question.After looking it up on a few different sites, I'd have to say I’m still not entirely sure whether or not I think it should be reinstated. I see both pro's and cons to requiring licensed broadcast stations to present controversial public issues (which tends to apply mainly to political situations) in a fair, equal and honest way. I think this would create a more balanced source of rational discourse and  information  for the public on such issues and in this way serves the public interest.That being said, I think this is getting uncomfortably close to infringing upon freedom of the press and speech. I understand that the Fairness doctrine has the best of intentions and has even served us well in the past, But often, even good legislation leads to increased powers and control for government. No matter how many checks and balances our government has, It only takes one government official's loose interpretation of a law in order to justify abusing his office and encroaching up the basic rights our constitution grants us.

Web Mining Homework

A Recommender System Based On Web Data Mining for Personalized E-learning Jinhua Sun Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [email  protected] edu. cn Yanqi Xie Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [email  protected] edu. cn Abstract—In this paper, we introduce a web data mining olution to e-learning system to discover hidden patterns strategies from their learners and web data, describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable e-learning system, propose a new framework based on data mining technology for building a Web-page recommender system, and demonstrate how data mining technology can be effectively applied in an e-learning environment.Keywords–Data mining; web log,;e-learning; recommender readily interpreted by the analyst. A virtual e-learnin g framework is proposed, and how to enhance e-learning through web data mining is discussed. II. RELATED WORK I. INTRODUCTION With the rapid development of the World Wide Web, Web data mining has been extensively used in the past for analyzing huge collections of data, and is currently being applied to a variety of domains [1]. In the recent years, e-learning is becoming common practice and widespread in China.With the development of e-Learning, massive amounts of learning courses are available on the e-Learning system. When entering e-Learning System, the learners are unable to know where to begin to learn with various courses. Therefore, learners waste a lot of time on e-Learning system, but don’t get the effective learning result. It is very difficult and time consuming for educators to thoroughly track and assess all the activities performed by all learners.In order to overcome such a problem, the recommender learning system is required. Recommender systems are used on ma ny web sites to help users find interesting items [2], them predict a user's preference and suggest items by analyzing the past preference information of users, e-learning system is applied on the basis of the method. The user’s learning route is given and then provides the relevant learners useful messages through dynamically searching for the appropriate learning profile.This paper recommends learners the studying activities or learning profile through the technology of Web Mining with the purpose of helping they adopt a proper learning profile, we describe a framework that aims at solution to e-learning to discover the hidden insight of learning profile and web data. We demonstrate how data mining technology can be effectively applied in an e-learning environment. The framework we propose takes the results of the data mining process as input, and converts these results into actionable knowledge, by enriching them with information that can beThe route where the learner brow ses through the web pages will be noted down in Web log, carries on the technology of Web mining through Learning Profile and Web log, and analyzes from the materials related to association rule. It can be found the best learning profile from this information. These learning profiles combine with the Agent and put them on the learning website. Furthermore, the Agent recommends the function of learning profiles on learning website. Therefore, the learner will acquire a better learning profile.This chapter briefly illustrates the relevant contents including: e-Learning, Learning Profile, Agent, Web Data mining and Association rule. A. E-learning E-learning is the online delivery of information for purposes of education, training, or knowledge management. In the Information age skills and knowledge need to be continually updated and refreshed to keep up with today’s fastpaced study environment. E-learning is also growing as a delivery method for information in the education fiel d and is becoming a major learning activity. It is a Web-enabled system that makes knowledge accessible to those who need it.They can learn anytime and anywhere. E-learning can be useful both as an environment for facilitating learning at schools and as an environment for efficient and effective corporate training [3]. B. A Glance at Web Data Web usage mining performs mining on web data, particularly data stored in logs managed by the web servers. All accesses to a web site or a web-based application are tracked by the web server in a log containing chronologically ordered transactions indicating that a given URL was requested at a given time from a given machine using a given web client (i. e. browser).As shown in table 1, Web log contains the website â€Å"hit† information, such as visitor’s IP address, date and time, required pages, and status code indicating. The web log raw 978-1-4244-4994-1/09/$25. 00  ©2009 IEEE data is required to be converted into database f ormat, so that data mining algorithms can be applied to it. TABLE I. WEB LOG EXAMPLES Web logs 172. 158. 133. 121 – – [01/Nov/2006:23:46:00 -0800] â€Å"GET /work /assignmnts/midterm-solutions. pdf HTTP/1. 1†³206 29803 2006-12-14 00:23:56 209. 247. 40. 108 – 168. 144. 44. 231 GET /robots. txt – 200 600 119 125 HTTP/1. 0 www. a0598. com ia_archiver – – sefulness and certainty of a rule respectively [5]. Support, as usefulness of a rule, describes the proportion of transactions that contain both items A and B, and confidence, as validity of a rule, describes the proportion of transactions containing item B among the transactions containing item A. The association rules that satisfy user specified minimum support threshold (minSup) and minimum confidence threshold (minCon) are called strong association rules. D. Web Mining for E-learning Learning profile help learner to keep a record of their current knowledge and understanding of e-learn ing and elearning activities.Web mining is the application of data mining techniques to discover meaningful patterns, profiles, and trends from both the content and usage of Web sites. Web usage mining performs mining on web data, particularly data stored in logs managed by the web servers. The web log provides a raw trace of the learners’ navigation and activities on the site. In order to process these log entries and extract valuable patterns that could be used to enhance the learning system or help in the learning evaluation, a significant cleaning and transformation phase needs to take place so as to prepare the information for data mining algorithms [6].Web server log files of current common web servers contain insufficient data upon which to base thorough analysis. The data we use to construct our recommended system is based on association rules. E. Recommendation Using Association Rules One of the best-known examples of data mining in recommender systems is the discove ry of association rules, or item-to-item correlations [7]. Association rules have been used for many years in merchandising, both to analyze patterns of preference across products, and to recommend products to consumers based on other products they have selected.Recommendation using association rules is to predict preference for item k when the user preferred item i and j, by adding confidence of the association rules that have k in the result part and i or j in the condition part [4]. An association rule expresses the relationship that one product is often purchased along with other products. The number of possible association rules grows exponentially with the number of products in a rule, but constraints on confidence and support, combined with algorithms that build association rules with item sets of n items from rules with n-1 item sets, reduce the effective search space.Association rules can form a very compact representation of preference data that may improve efficiency of s torage as well as performance. In its simplest implementation, item-to-item correlation can be used to identify â€Å"matching items† for a single item, such as other clothing items that are commonly purchased with a pair of pants. More powerful systems match an entire set of items, such as those in a customer's shopping cart, to identify appropriate items to recommend. The web data is massive since the visitor’s every click in the website will leave several records in the tables.This also allows the website owner to track visitors’ behavior details and discover valuable patterns. C. Data Mining Techniques The term data mining refers to a broad spectrum of mathematical modeling techniques and software tools that are used to find patterns in data and user these to build models. In this context of recommender applications, the term data mining is used to describe the collection of analysis techniques used to infer recommendation rules or build recommendation model s from large data sets.Recommender systems that incorporate data mining techniques make their recommendations using knowledge learned from the actions and attributes of users. Classical data mining techniques include classification of users, finding associations between different product items or customer behavior, and clustering of users [4]. 1) Clustering Clustering techniques work by identifying groups of consumers who appear to have similar preferences. Once the clusters are created, averaging the opinions of the other consumers in her cluster can be used to make predictions for an individual.Some clustering techniques represent each user with partial participation in several clusters. The prediction is then an average across the clusters, weighted by degree of participation. 2) Classification Classifiers are general computational models for assigning a category to an input. The inputs may be vectors of features for the items being classified or data about relationships among th e items. The category is a domain-specific classification such as malignant/benign for tumor classification, approve/reject for credit requests, or intruder/authorized for security checks.One way to build a recommender system using a classifier is to use information about a product and a customer as the input, and to have the output category represent how strongly to recommend the product to the customer. 3) Association Rules Mining Association rule mining is to search for interesting relationships between items by finding items frequently appeared together in the transaction database. If item B appeared frequently when item A appeared, then an association rule is denoted as A B (if A, then B).The support and confidence are two measures of rule interestingness that reflect III. WEB DATA MINING FRAMEWORK FOR E-COMMERCE RECOMMENDER SYSTEMS A. A Visual Web Log Mining Architecture for Personalized E-learning Recommender System In this section, we present A Visual Web Log Mining Architec ture for e-learning recommender to enable personalized, named V-WebLogMiner, which relies on mining and on visualization of Web Services log data captured in elearning environment. The V-WebLogMiner is such a odel: with the mining technology and analysis of web logs or other records, the system could find learners’ interests and habits. While an old learner is visiting the website, the system will automatically match with the active session and recommend the most relevant hyperlinks what the learner interests. As shown in Figure1, V-WebLogMiner is a multi-layered architecture capable to deal with both Web learner profiles and traditional Web server logs as input data. It maintains three main components: data preprocessing module, Web mining module and recommendation module. ) Web Mining Module The Web mining module discovers valuable knowledge assets from the data repository containing learners' personal data by executes the mining algorithms, tracked data of learners' perfor mance and behavior, automatically identify each learner’s frequently sequential pages and store them to recommend database. When the learner visit the site next time, hyperlinks of those pages will be added so that the learner could directly link to his individual pages being remembered.The major component of Web mining module is Web data mining which acts as a conductor controlling and synchronizing every component within the module. The Web data mining module is also responsible for interfacing with the storage. The learning profile evaluation component provide profiling tool to collect personal data of learner and tracking tool to observe learners' actions including like and dislike information. For personalization applications, we apply rule discovery methods individually to every learner’s data.To discover rules that describe the behavior of individual learner, we use various data mining algorithms, such as Apriori [8] for association rules and CART (Classificatio n and Regression Tress) [9] for classification. 3) Recommendation Module The recommendation module is a recommendations engine; it is in charge of bulk loading data from course database, executing SQL commands against it and provides the list of recommended links to visualization tools.For the recommendation module, recommendations engine is responsible for the synchronizing process indexing and mapping, is a component for storing and searching recommend assets to be used in the learning process. The recommendation engine considers the active learners in conjunction with the recommended database to provide personalized recommendations, it directly related to the personalization on the website and the development of elearning system. The task of the recommendation engine is to determine the type of the learner online and compute recommendations based on the recent actions of that learner.The decision is based on the knowledge attained from the recommended database. The recommender en gine is activated each time that the learner visits a web page. First, if there are clusters in the recommended database, then the engine has to classify the current learner to determine the most likely cluster. We have to communicate with the engine to know the current number of pages visited and average knowledge of the learner. Then, we use the centroid minimum distance method [10] for assigning the learner to the cluster whose centroid is closest to that learner.Finally, we make the recommendation according to the rules in the cluster. So, only the rules of the corresponding cluster are used to match the current web page in order to obtain the current list of recommended links [11]. 4) The Visualization tools Visualization tools should be used to present implicit and useful knowledge from recommendations engine, Web services usage and composition. Data can be viewed at different levels Figure 1. A visual web mining architecture for Personalized E-learning Recommender System ) Da ta Preprocessing Module The data preprocessing module is set of programs used to prepare data for further processing. For instance: extraction, cleaning, transformation and loading. This module uses Web log files and learner profile files to feed the data repository. The data preparation component is used to parse and transform plain ASCII files produced by a Web server to a standard database format. This component is important to make the architecture independent from the Web server supplier. of granularity and abstractions as patrolled coordinate’s graphs [12, 13].This visual model easily shows the interrelationships and dependencies between different components. Interactively, the model can be used to discover sensitivities and to do approximate optimization, etc. B. The Procedure of the Data is Explained As show in figure 1, the beginning learner, that is to say the earliest one, will study in the e-Learning teaching platform. The course materials of Web studying system c ome from the course database. The data of learner’s learning profiles may be recorded in the learner profile files and Web log files.Then next step is to find out the best learning profile from the proceeded data of Web log through web mining to proceed with Association rule and others data mining algorithm. These learning profiles need to be classified—every field has relevant courses and better learning profiles. The recommender engine will offer the list of recommended links when learners study the courses. With the above information and learning profiles, when the future learners study in Web, recommender engine offers related link lists according to recommend database. However, these link lists may not be suitable for all learners.Therefore, after finishing recommendation every time, there are systems of assessing. The learner (n +1) evaluates the learning profiles that are recommended. Because the profiles analyzed by system may not be perfect, if there are adjus tments of evaluation would make the recommendation conform to learners’ asks more. These suggestions can help learners navigate better relevant resources and fast recommend the on-line materials, which help learners to select pertinent learning activities to improve their performance based on on-line behavior of successful learners.IV. CONCLUSION AND FUTURE WORK There are some possible extensions to this work. Research for analyzing learners’ past studying pattern will enable to detect an appropriate. Furthermore, it will be an interesting research area to effectively judge session boundaries and to improve the efficiency of algorithms for web data mining. ACKNOWLEDGMENT The authors gratefully acknowledge the financial subsidy provided by the Xiamen Science and Technology Bureau under 3502Z20077023, 3502Z20077021 and YKJ07013R project. REFERENCES [1] [2] D. J. H and, H. Mannila, and P. 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[3] [4] [5] [6] [7] [8] [9] [10] Recommender systems have emerged as powerful tools for helping users find and evaluate items of interest. The research work presente d in this paper makes several contributions to the recommender systems for personalized e-learning. First of all, we propose a new framework based on web data mining technology for building a Web-page recommender system. Additionally, we demonstrate how web data mining technology can be effectively applied in an e-learning environment. [11] [12] [13]