雅思阅读如何短时内获得更高分

如何在有限的时间内，让雅思阅读获得更多的分数?今天小编给大家带来了雅思阅读如何短时内获得更高分，希望可以帮助到大家，下面小编就和大家分享，来欣赏一下吧。

雅思阅读如何短时内获得更高分

具体方法如下：

雅思阅读解题方法一、首先决定要以什么样的顺序做题。真正的雅思考试并不是想象中的由易到难，很有可能一开始的文章就很难。设想，如果用30分钟先解决一道难题，再用剩下的30分钟去完成两道简单的题目，效果注定不好!试举剑桥4中TEST 2 为例，三篇文章分别 “lost for words”, “alternative medicine in Australia”, “play is a serious business”.乍一看第三篇文章题目是最简单的，实际上它反而是最难的。

雅思阅读解题二、题型决定做题顺序，而不是题目。“lost for words”题型分别是：summary, 人名理论matching, yes/no/not given. “alternative medicine in Australia”题型分别是：multiple choices, yes/no/not given, 填空题. “play is a serious business”题型分别是：信息段落配对，多选多，人名理论配对。初步分析题型后还可以细化，理清做题思路。第一篇文章难度适中，summary 属主旨类型题建议先做，同时可以把人名全部找到以节省时间。matching题中出现五对五配对还有NB。第二篇文章题目虽难但是引言部分交待很清楚，属简单的题目。填空题实属数字游戏，整篇文章完全按顺序出题。第三篇文章难度系数较大，信息段落配对难把握，之后的人名理论配对干扰选项过多，从一开始相当于八选一。这样分析下来，我们的做题顺序应该是“Passage 2/1/3”。

雅思阅读解题三、掌握技巧，灵活运用。题型没有绝对的难易之分，对其他考生难也许对你反而容易，要结合自己的实际情况。可以先做送分题“表格填空，图形题，完成句子”。在五大主流题型中，配对题比较费时，其中信息和段落配对最花时间，建议放在最后。而作为主旨的heading 和summary 可考虑先做，因为完成主旨题型后文章大致的内容和结构都可以掌握，对于细节题的定位会方便很多。

雅思考试像是一场战斗，应该灵活运用作战的策略和方法，后期更需要成套的阅读训练。如果能够运用这些提高效率节省时间的方法，有助于学生信心的塑造和雅思分数的提高。

雅思阅读文章先做哪一篇?

雅思阅读考试主要有三篇文章，每篇文章所涉及到的背景知识，题型，难度等方面会有所不同。那么，考生就有所疑问了，到底雅思阅读文章先做哪一篇才能有效节省时间与提高效率呢?在备考雅思阅读考试时，多数考生存在这样一个误区，即三篇文章的难易程度是递增的，因而在考试时，如果时间不够的话就不怎么管第三篇文章了，把前两篇文章做完并做对就已经不错了。其实，这是一种非常错误的观念。因为阅读文章的难易程度不是由易到难的，而是随机的。具体先做哪一篇，考生还是要理性地分析。

实际上，既然雅思阅读文章有题材考察与题型考察之分，那么考生其实就可以从这两个角度进行分析，看看自己究竟适合先做哪一篇。

从题材角度来看：

雅思阅读文章通常分为生物，地理和社会科学三大类，不同类别包含的内容繁多，考生要熟悉了解。比如生物类题材是常考题材之一，常常涉及到动物的生活习性，基因研究，器官研究等。地理类题材有一定难度，所涉及的专业知识较多，范围也很广泛，如气象预测，沙漠化之类的。社会类题材可以说是一个比较大的范畴，包含众多小的分支，如教育，历史，考古等等。点击查看详情

当考生拿过试卷后，要将三篇文章的主标题，副标题，插图以及第一句话全部浏览后，选择一篇从题材上自己比较熟悉和了解的文章先做，毕竟这样的文章背景知识和专有名词对于考生来说是相对熟悉的，做起题来就会容易一些。

从题型角度来看：

如果考生对于三篇文章背景知识的掌握程度差不多的话，就可以在浏览完三篇文章所涉及到的所有题型之后，就可以选择一篇从题型上自己比较拿手的文章先做。

雅思阅读题型主要有八种，即list of headings，complete the sentences，answer short questions，picture filling，summary，multiple choices，matching，true or false or not given。不同题型的难易也会不同，比如list of headings也就是标题对应题，这类题型无需定位，但需要对文章大体的内容有一定理解，难度系数两颗星; matching因果配对无出题规律，定位也比较复杂，难度系数三颗星;true or false or not given定位相对简单，但是往往在判断时容易失误，难度系数两颗星。点击查看详情

总而言之，考生要根据自己对不同题材和题型的掌握程度来决定雅思阅读文章先做哪一篇，也就是要合理安排做题顺序，这样才会高效完成任务。另外，考生在打好基础之后在平时最好能够按照实际的阅读考试时间来做题，检测一下自己是否能够在规定时间内做完所有题目，并找出不足之处加以改进。希望考生们取得满意的雅思成绩，更多雅思备考经验我们将会陆续为大家更新。

雅思阅读练习题：Can a robot replace us?

的雅思备考资料，无忧考网将第一时间为大家发布。

Robots. They can already clean our floors, build our cars, review legal documents, check us in at hotels and serve us drinks, but will we see them sitting around the boardroom table in place of people? Are my fellow board directors about to be made obsolete, replaced by robots overseeing companies? Will the discussion of diversity around the board table begin to include talk of non-humans?

It might sound farfetched, but 45% of 800 executives surveyed by the World Economic Forum’s Global Agenda Council on the Future of Software and Society said they expected an artificial intelligence machine will sit on a company’s board of directors by the year 2025.

This isn't the first time this has come up. Last year an investment group drummedup(招徕) headlines by announcing that it had appointed an AI to its board of directors. The firm said it would analyse data to help them make decisions about biotech investments. But, since that isn’t the full extent of the role of a genuine board member, the algorithm clearly was not really going to function as a full voting member of the firm’s board.

So do we need to set a place for robots at the boardroom table? There are a couple of reasons why I’m in the 55% who don't believe that are our jobs are at risk, or at least not from robots.

I believe data in the boardroom is growing in importance. Data can help us make decisions of all kinds. When developing strategy, it informs our thinking on marketplace trends, on what people are buying, selling, saying and doing.

On the remunerations and compensation committee, we use data to see what people are earning in the company and in the industry more broadly, and to examine overall compensation trends. In the audit committee, and also when doing any sort of financial analysis, we use data to see where spending and saving is and isn’t happening. And risk committees use data to analyse the myriad factors associated with risk of any kind, be it financial, infrastructure, strategy, legal and more.

In fact, there are few, if any areas, where we don't depend on data in some shape or form.

But it’s how we use the data to inform our decisions that differentiates us from the robots.We weigh the information to make decisions that are specific to the company, its employees and its competitiveness. It is the data mixed with creativity and intuitive, non-linear thinking that makes a company successful.

Indeed, if everyone is using the same sort of data and simply making automatic, calculated decisions from it, then differentiation is lost. The competitive advantage,in many ways, comes from the ingenuity and creativity of people.

I've often said that the boardroom is the most “human” place I've ever been. People come with their own knowledge, feelings, emotions and agendas. And, yes, that can colour the conversation on the day and sometimes hinder efficiency or easy decision making. But there is also a healthy friction that comes into play as well, which results in more robust results.

It is true that humans are fallible, and board directors are no exception. For example, pay packets(工资待遇) for CEOs can be too high, perhaps because they’ve been driven by human greed or judgement of real value. But would it be any different with a robot on the board? They would analyse the pay of other CEOs, and come up with calculations, just as is done now. But would they be able to bring about change to the compensation structure? Would they force a right sizing or apply a moral or ethical dimension to whether the compensation was correct? I think it would still take a human, or at least a human programming it, to calculate that.

Another important part to the human boardroom is the ability to judge people. Not what is on paper, but rather the people who are actually in front of you. Anyone can come up with an idea and even present it well. But as any investor, and particularly venture capitalists, will tell you, quantitative calculations can only take you so far when judging whether the idea is likely to be a success. Bringing those ideas to fruition, be it a new venture or a new direction for a company, requires the right people to execute on it. Judging whether things are going well or poorly, whether a strategy will work or not comes down to the ability to size someone up. The board must decide they are going to trust a CEO or an executive, and that has a lot to do with whether they are credible leaders.That can't be a judgement solely based on numbers.

Some of the fallibility in boardrooms comes from the fact that as a diverse group of board members we are,individually, often called to make decisions on matters where we don’t have expertise. In those cases, we depend on data and analysis and good briefings to help us to make those decisions, but that is still not a substitute for good judgement.

Also, data is not necessarily completely objective, nor is it always correct. Polling has proven recently that the data we have is only as good as the questions asked, and the veracity of the answers given.

Using data as a tool is great, and there is definitely a place for all kinds of AI as an enabling tool in the boardroom. But successful boardrooms are characterized by nuance and judgement. The boardroom, like most of life, is not a 0-or-1 situation. We must depend on human intuition and human understanding and combine that with data.

As board directors, we must draw on as much information and analysis as possible, and big data has a real role to play in enabling the boardroom, but I don't think I'll be sitting next to a robot in the boardroom any time soon.

Vocabulary

Obsolete 过时的

Oversee 监管

Farfetched 牵强附会的

Algorithm 运算法则

Remuneration 回报

Compensation 补偿

Audit 审计

Myriad 很多的;大量的

Differentiate 区分;区别

Intuitive 直觉的

Ingenuity 精巧;独创

Hinder 阻碍

Friction 摩擦

Robust 坚固的;有力的

Fallible 可能犯错的

Come down to… 归结于

Fallibility 可错性

Briefing 简报

Veracity 真实性

Nuance 细微之处

Draw on 利用

本文长难句

It might sound farfetched,but 45% of 800 executives surveyed by the World Economic Forum’s Global Agenda Council on the Future of Software and Society said they expected an artificial intelligence machine will sit on a company’s board of directors by the year 2025.

这听起来似乎牵强，但是在“软件与社会的未来”这一调查中，接受世界经济论坛全球事务协会调查的800名高管里，有45%的人表示期待在2025年前将看到人工智能机器参与公司董事会。

But, since that isn’t the full extent of the role of a genuine board member, the algorithm clearly was not really going to function as a full voting member of the firm’s board.

但是，由于这并不是真正的董事成员的全部职能，所以显而易见，(机器人的)运算法则并不能在公司董事会上像有投票权的成员那样履行职责。

And risk committees use data to analyse the myriad factors associated with risk of any kind, be it financial, infrastructure, strategy, legal and more.

风险委员会用数据来分析与各类风险相关的诸多因素，不管是财务、基础设施、策略、法律还是其他。

Indeed, if everyone is using the same sort of data and simply making automatic,calculated decisions from it, then differentiation is lost.

的确，如果每个人都利用同样的数据，并由此得出自动的、计算出来的结论，那么差别就消失了。

Bringing those ideas to fruition, be it a new venture or a new direction for a company, requires the right people to execute on it.

要让这些想法开花结果，无论是开一家新公司，或是为公司找一个新的方向，需要恰当的人来执行才可以。

Some of the fallibility in boardrooms comes from the fact that as a diverse group of board members we are, individually, often called to make decisions on matters where we don’t have expertise.

董事会可能犯错，是因为如下事实：董事会成员虽然多种多样，但我们经常被单个叫去，就一些我们没有专业知识的事情做决定。

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