The effects of self-reading and text layouts on seventh graders' mathematical performance about "Pythagorean Theorem"
Bibliographic record
Abstract
本研究旨在瞭解自行閱讀與閱讀不同編排順序的文本對國一學生有關勾股定理的概念、程序與解題表現之影響。本研究編擬有關勾股定理的「證明優先」與「應用優先」文本，這兩種文本的內容相同但編排順序不同；以及編製概念、程序與解題表現前後測試卷。在蒐集前後測資料後，主要以多變量與單變量變異數分析處理實驗資料。研究結果顯示，閱讀不同文本與學生程度之間存在顯著的交互作用；而且並非只要透過自行閱讀文本（測驗時也未收回文本），就一定有利於提升高、中、低三種程度的學生在概念、程序和解題等數學能力的測驗成績。 The purpose of this study aims at understanding the effects of reading texts by oneself with different layouts on 7th graders’ mathematical performance about “Pythagorean Theorem.” This study designed “proof-first” and “application-first” texts, and instruments for testing mathematical performance about “Pythagorean Theorem.” After collecting pre-test and post-test data, the statistical method of ANCOVA was mainly used to analyze experimental data. The results showed that the interaction between text layouts and students’ mathematical abilities was statistically significant, and that students, who read a mathematical text by themselves as testing in mathematics related to the text, did not necessarily perform better than students, who did not read the mathematical text by themselves as testing.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".