Effectiveness of CIRC to Enhance Chinese Reading and Writing Skills for Grad 3 Primary Students in Liuzhou City, China
Bibliographic record
Abstract
The objective of this study is to investigate the effects of implementing the Collaborative Strategic Reading (CIRC) approach in reading instruction on the development of reading and writing abilities among third-grade children in primary schools. Utilizing a unified pre-test and post-test experimental design. The study’s sample consists of 43 students that are enrolled in Class 1 of Grade 3. The study involved the implementation of Collaborative Integrated Reading and Composition (CIRC) methodology in Chinese reading instruction. Additionally, self-designed assessments were administered to measure the students’ reading and writing proficiency levels. These assessments were conducted both before to and after the experimental intervention, allowing for the examination of any variations in the students’ scores. The data underwent analysis utilizing the Statistical Package for the Social Sciences (SPSS), which involved calculating the mean, standard deviation, and conducting a T-test. The findings of the study suggest that the use of CIRC (Cognitive Instructional Reading Comprehension) in Chinese reading instruction yields a substantial enhancement in the reading and writing proficiencies of elementary school children. Moreover, there is a notable increase in the scores across several dimensions of reading and writing skills. Based on the findings of this study, the researchers propose the implementation of CIRC technology in Chinese reading instruction across several grade levels as a means to augment students’ proficiency in reading and writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".