The Application of RCRR (Read, Cover, Remember, Retell) Technique in Teaching Reading at Junior High School
Bibliographic record
Abstract
This research was experimental research. The population of this research was the VIII grade students of SMPN 2 Kec. Luak Payakumbuh. There were four classes of the eight grade students, total population was 88 students. The sample of this research had chosen by using cluster sampling. The experimental class was VIII.3 that had taught by using Read, Cover, Remember, Retell (RCRR) strategy and the control class was VIII.4 had taught by using conventional class in teaching reading. The instrument of this research was reading test in multiple choice test form. It was valid because the students have learnt the material. Then, the instrument was reliable because the result of split-half was 0.65 gave positive association. To get the data, the researcher used the t-test formula that suggested by Gay and Airasian. The result of the research was analyzed by t-test formula and the researcher got t-calculated was 8.38. After that, the researcher compared to t-table was 2.021. Where the level of significant was 0.05 with degree of freedom was 43. After analyzing the data, the researcher got t-count higher than t-table. So, the null hypothesis (H0) was rejected and alternative hypothesis (H1) was accepted. It means there was a significant effect of teaching reading by using Read, Cover, Remember, Retell (RCRR) strategy at the eight grade students of SMPN 2 Kec. Luak. Keywords : RCRR, Teaching Reading, The Application.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".