The Effect of Using Blended Learning in Teaching English on Direct and Deferred Achievement of Primary School Students
Bibliographic record
Abstract
The study aimed at recognizing the effect of using integrated learning methods in teaching English on immediate and later achievement for primary phase students. The study followed a quasi-experimental method by using an experimental group, which learned English through integrated learning and a controlled group, which learned the same subject by using the traditional way. To achieve the study's objectives, the researcher prepared an achievement test. The validity and reliability of the test have been checked by introducing it to a group of specialized teachers in addition to finding Cronbach's alpha value, which worked out at (0,627). After data collection has been completed, the arithmetic means of the immediate achievement test and later achievement test have been calculated. The results indicate that there are statistically significant differences in the achievement of the primary phase in favor of the experimental group, which has been taught the English subject by integrated learning method. The study recommends that it is necessary to apply an integrated learning strategy since it is effective in students' scientific achievement, and improving motivation for learning, and considering the rules of the integrated educational process to train the teaching staff to apply integrated learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".