閱讀策略教學對高二學生數學學習表現的影響 Effects of Teaching Reading Strategies on Senior High School Student’s Mathematics Performance
Bibliographic record
Abstract
本研究參考交互教學法之閱讀策略設計運用閱讀來學習數學的課程，透過實驗研究檢驗此課程是否有助於提升高中二年級學生的數學學習成就、數學閱讀理解以及策略使用的表現。教學實驗共歷時6 週，研究採前後測準實驗設計，以臺北市立一所公立高中二年級兩個班級學生為研究對象；其中一班為實驗組，實施閱讀策略教學，另一班對照組則未實施閱讀策略教學，兩班皆由同一位數學教師教學，有效樣本共計74人。研究者共設計四份評量工具檢驗教學實驗的成效，內容包含數學成就測驗、數學閱讀理解測驗以及閱讀策略使用情形自我評估，分別用於實驗前的前測、6週實驗後的第一次後測、經過1 週的第二次後測，以及6週實驗後2 個月的延後測，再以廣義估計方程式分析測驗資料。研究發現運用閱讀來學習數學課程的成效包含：一、有利於提升學生讀課本時使用各項閱讀策略的頻率；二、有利於提升學生當下的數學成就與數學閱讀理解；三、有利於提升學生使用提問與澄清策略來閱讀未學過的數學文本。但是，隨著教學實驗後所經過的時間愈久，對數學成就與數學閱讀理解的效果會逐漸消失，最後只有利於提升學生使用提問策略來閱讀未學過的數學文本。本研究將針對未如預期的延後測實驗結果提出可能的原因，作為未來進一步改善數學閱讀教學與研究 的建議。 The purpose of this study was to explore the effect of teaching reading strategies involving textbooks for senior high school students. Two classes of 11th-grade students participated in this study; one was the experimental class and the other was the control class. The experiment was designed to teach cognitive reading strategies through reciprocal teaching using a mathematics textbook to improve mathematics performance. The quantitative instruments included a mathematics achievement test, reading comprehension test, and self-assessment of reading strategy usage. These instruments were used at four different times: (1) the pretest before the experiment; (2) the first posttest after the experiment; (3) the second posttest 1 week after the experiment; and (4) the delayed measurement 2 months after the experiment. The data obtained were analyzed through the generalized estimating equation method. The results revealed that the teaching experiment effectively promoted students’ use of all types of strategies when reading the mathematics textbook. However, the math achievement and reading comprehension tests only demonstrated improvement at the first posttest. After the teaching experiment, these effects gradually disappeared. Regarding the use of reading strategies, the teaching experiment effectively promoted students’ use of questioning and clarifying. This effect also gradually disappeared after the teaching experiment. Several explanations of the unexpected results are provided in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".