📖 Bài đọc (reading passage)

Does class size matter?
A
A. Of all the ideas for improving education, few are as simple or attractive as reducing the number of pupils per teacher. With its uncomplicated appeal, class-size reduction has lately gone from being a subject of primarily academic interest to become a public issue. In the U.S., more than 2o states have adopted policies aimed at decreasing class size.
B
B. One way investigators have attempted to analyze the effects of class size is by reviewing existing data, such as records kept by the U.S. Department of Education. These show that between 1969 and 1997, the average number of pupils per teacher in American public and private elementary schools fell from 25 to 18, a decline of greater than 27 percent. In secondary schools, the number also fell, from 19 to 14. Does these findings mean that class size makes no difference? Not necessarily. For a variety of reasons, most researchers, including us, pay little attention to those figures. For instance, schools strive for more than just high test scores; they also usually try to keep their dropout rates low. And indeed, the dropout rate for students aged 16 to 24 fell from 15 to 11 percent over that period. Because dropouts generally come from the low end of the achievement distribution, a reduction in the dropout rate could, be expected to pull down average test scores in the upper grades. Ideally, U.S. students would all come from families that are financially well off, with two highly educated, English-speaking parents who are involved in their children's schooling. Teachers would all be creative and have complete mastery of their subject matter. Schools would be nicely outfitted with libraries, computers and other resources.
C
C. Over the past 35 years, hundreds of studies and analyses of existing data (such as the Department of Education records) have focused on class size. Unfortunately, most of these studies were poorly designed. The notable exception was the STAR project. Students entering kindergarten were randomly assigned to one of three kinds of classes: a small class of 13 to 17 students, a regular-size class of 22 to 26. The students remained in whatever category they had been assigned to through the third grade, after which they joined a regular classroom in the fourth. To ensure that teaching quality did not differ, teachers were randomly assigned to small and regular-size classrooms. Few teachers received any special training for working with small classes, and there were no new curricular materials.
D
D. Charles M. Achilles of Eastern Michigan University found "an array of benefits of small classes" in their review. They also found that the effect was stronger for minority students. Black and Hispanic children improved their scores slightly more than did other students - a significant finding from a policy standpoint. He argues, the STAR data cannot be used to prove that the gains persist for years after a student has returned to regular-size classes. He and others have also shown that during the study, too many children migrated from the regular to the small classes, probably because school personnel caved in to parent demands. Criticism does not undermine the findings of a statistically significant benefit of being in a small class.
E
E. California’s multi-billion-dollar effort, begun in 1996, stands more as a model of what not to do than as an initiative worthy of emulation. That state is trying to reduce classes in kindergarten through grade three from a maximum of 33 to a maximum of 2o in rich and poor districts alike - despite a shortage of qualified teachers, especially in low-income areas. This across-the-board approach may be politically expedient, but it seems to have actually exacerbated the disparity in resources available to rich and poor schools in California. The better-paying, more affluent districts got the best teachers - including a fair number that good teachers. The evaluators found a small but statistically significant achievement advantage in reading, writing and mathematics for students in classes that had been reduced to 20 or fewer pupils, as compared with the classes of more than 20. The second program, Wisconsin's Student Achievement Guarantee in Education (SAGE), also begun in 1996, was a five-year study. It was small - class size was reduced in just 14 schools - but noteworthy because it targeted schools in which at least 30 percent of the students were below the poverty level.
F
F. Studies such as STAR and SAGE have made it hard to argue that reducing class sizes makes no difference. On the other hand, the California initiative has shown that the strategy, applied with too little fore thought and insight, can consume billions of dollars and, at least in the short run, produce only minuscule gains and even some losses. Legislators and administrators need more solid information on the relative costs of the other options before they can make sensible policy decisions.

❓ Câu hỏi (questions)

Question 1 - 5
Which paragraph include the following information?
1
Criticism about STAR program due to some factors that are not reliable.
2
A comparison of one program’s failure and other programs’ successes.
3
The change of class-size reduction from an academic topic to a public one.
4
Actions were taken to ensure the reliability of the data.
5
Reasons why analyzing existing data on class size is complex.
Question 6 - 13
Classification
List of Findings
A
STAR
B
California
C
SAGE
6
Class's composition was left by chance.
7
It is not certain that small class size results in better performance even they went to the fourth grade.
8
The special groups got advantages from the program.
9
It was a very small-scale project.
10
The students remained in whatever category they had been assigned to through the third grade.
11
It targeted schools in which at least 30 percent of the students were below the poverty level.
12
The program aggravated the situation of the poorer districts, which were already having trouble recruiting and retaining good teachers.
13
Students' background also affect their performance.

🔥 Answer key (đáp án và giải thích)

1
D

Giải thích chi tiết

Ứng dụng Linearthinking để giải quyết dạng bài Matching Information

📌 Dạng Matching Information chúng ta nên làm cuối cùng, sau khi đã làm các dạng câu hỏi khác nhé DOLBIES, bởi vì lúc này mình đã đọc và hiểu nội dung của bài rồi → sẽ nhanh chóng tìm được vị trí đáp án hơn!!

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smiley23 Step 1: Phân tích câu hỏi (Matching Information) Criticism about STAR program due to some factors that are not reliable .

Dự đoán thông tin cần tìm (SPECIFY): đoạn đúng phải có đủ 2 mảnh:

  • lời phê bình/điểm yếu của chương trình STAR (ví dụ: “cannot be used to prove…”, “criticised…”, “problem…”).

  • Và phải nêu một yếu tố làm cho kết quả/thiết kế không đáng tin (ví dụ: dữ liệu bị “nhiễu” do người tham gia không theo đúng phân nhóm, can thiệp của nhà trường/phụ huynh, học sinh chuyển lớp, v.v.).

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smiley34 Step 2: Locate bằng chứng Đoạn D nói trực tiếp về các criticism nhắm vào STAR và nêu rõ một yếu tố làm dữ liệu kém đáng tin.

Câu bằng chứng: “He argues, the STAR data cannot be used to prove that the gains persist for years… too many children migrated from the regular to the small classes, probably because school personnel caved in to parent demands.”

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smiley26 Step 3: Đối chiếu paraphrasing

  • Criticism about STAR program the STAR data cannot be used to prove (tức dữ liệu STAR không đủ mạnh để chứng minh tác động kéo dài nhiều năm).

  • some factors that are not reliable too many children migrated from the regular to the small classes (quá nhiều trẻ tự chuyển từ lớp thường sang lớp nhỏ, làm phá vỡ thiết kế ban đầu) ⇔ và nguyên nhân: school personnel caved in to parent demands (= nhà trường “nhượng bộ” đòi hỏi của phụ huynh).

=> Vậy đoạn D đúng vì vừa có phê bình STAR, vừa có yếu tố khiến kết quả/thiết kế không còn đáng tin (học sinh chuyển nhóm do tác động phụ huynh/nhà trường).

Bẫy hay dính:

  • Đoạn C nói rất nhiều về STAR (randomly assigned, small vs regular class…) nên đọc lướt thấy keyword “STAR project” dễ chọn C. Nhưng C chủ yếu là mô tả thiết kế nghiên cứu, không tập trung vào criticism vì dữ liệu bị nhiễu/không đáng tin như đoạn D (migrated classes, caved in to parent demands, cannot prove long-term gains).

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