5.21阅读周测

Q1:姓名

:text1 Next month 11-year-olds will sit a series of short tests (SATs) in maths and English—a fact that causes much unhappiness among England’s teachers. At the National Education Union’s (NEU) recent conference, Jeremy Corbyn, Labour’s leader, announced to hearty applause that he would scrap these tests and that he would review other primary-school assessments. The attention serves as a reminder of the strength of feeling generated by testing young children. Unlike GCSE (taken at 16) and A-levels (at 18), SATs hold little sway over a pupil’s future. At most, they will help determine which academic stream the child enters in their first year at secondary school. Their chief purpose is to measure teachers and schools. If children are making good progress in their sums but not their reading, a school can devote more resources to English lessons. Nevertheless, teachers complain that they are under too much pressure to squeeze high marks out of their pupils. League tables are based on the percentage of children reaching certain standards, the schools inspectorate uses their results to inform its judgments and some teachers are on performance-related pay. Not all respond well. One head teacher in Leeds dragged a high-performing pupil from their sick bed to take a test. Another worry is that the emphasis on results has led to a narrowing of the curriculum as schools focus on maths and English, the only subjects tested. Two-thirds of primary schools spend less than two hours a week teaching science, which was dropped from the tests in 2009. Both problems arise from the way in which schools respond to the tests, rather than from the tests themselves. Transmitting pressure to pupils “can be a symptom of bad teaching”, says Natalie Perera of the Education Policy Institute, a think-tank. One remedy to the problem of narrow curriculums might be to dictate the time spent on each subject, as is the case in Finland. Instead, the government is planning tweaks that will ease the pressure on schools. Plans under consultation would mean that poor exam results no longer triggered intervention, which can lead to management changes. The Office for Standards in Education (Ofsted), meanwhile, is placing more emphasis in its inspections on ensuring that a “broad and balanced” curriculum is taught, as the law requires. Both problems arise from the way in which schools respond to the tests, rather than from the tests themselves. Transmitting pressure to pupils “can be a symptom of bad teaching”, says Natalie Perera of the Education Policy Institute, a think-tank. One remedy to the problem of narrow curriculums might be to dictate the time spent on each subject, as is the case in Finland. Instead, the government is planning tweaks that will ease the pressure on schools. Plans under consultation would mean that poor exam results no longer triggered intervention, which can lead to management changes. The Office for Standards in Education (Ofsted), meanwhile, is placing more emphasis in its inspections on ensuring that a “broad and balanced” curriculum is taught, as the law requires.

Q2:1. From the first paragraph, we know that SATs ______.

Q3:2. One purpose of SATs is to ______.

Q4:3. Teachers feel pressure because of ______.

Q5:4. Which of the following statements is true according to Paragraph 5?

Q6:5. The author’s attitude towards Mr Corbyn’s promise is one of ______.

:Text 2 The news that the Home Office is sorting applications for visas with secret algorithms (computing) applied to online applications is a reminder of one of Theresa May’s more toxic and long-lasting legacies: her immigration policies as home secretary. Yet even if the government’s aims in immigration policy were fair and balanced, there would still be serious issues of principle involved in digitizing the process. Handing over life-changing decisions to machine-learning algorithms is always risky. Small biases in the data become large biases in the outcome, but these are difficult to challenge because the use of software covers them in clouds of confusion and supposed objectivity, especially when its workings are described as “artificial intelligence”. This is not to say they are always harmful, or never any use: with careful training and well-understood, clearly defined problems, and when they are operating on good data, software systems can perform much better than humans ever could. This isn’t just a problem of software. There is a sense in which the whole of the civil service, like any other bureaucracy, is an algorithmic machine: it deals with problems according to a set of determinate rules. The good civil servant, like a computer program, executes their instructions faithfully and does exactly what they are told. The civil servant is supposed to have a clearer grasp of what their instructing human means and really wants than any computer could. But when the instructions are clear, the machinery of government—a telling metaphor—is meant to put them into action. Digital algorithms make it easy to make bigger mistakes, faster, and with less accountability. One key difference between the analogue bureaucracy of the traditional civil service and the digitized bureaucracy of artificial intelligence is that it is very much easier to hold a human organization to account and to retrace the process by which it reached a decision. The workings of neural networks are usually vague even to their programmers. Yet the technology promises so much to governments that they will certainly set up it. This does not mean we are powerless. There are simple, non-technical tests that can be applied to government technology: we can ask easily whether digitizing any particular service makes life better for the public who must use it or merely more convenient for the civil service. In some cases these aims are directly opposed. Forcing applicants for universal credit to apply online directly harms anyone without an internet connection, and such people are likely to be most in need of it. Similarly, the digitization of immigration services means it is more difficult and more expensive to get a face-to-face interview; again, something that is likely to damage those who need it most.

Q7:6. According to Paragraph 1, secret algorithms ______.

Q8:7. It’s hard to find small data biases in that .

Q9:8. In part, the civil service is similar to algorithmic machine because ______.

Q10:9. Which one is the feature of digital algorithms?

Q11:10. The author’s attitude toward digital algorithms in immigration services can be described as ______.