The Effect of Blended Learning on Private School Students’ Achievement in English and Their Attitudes Towards It
Bibliographic record
Abstract
This study aims at exploring the effect of using blended learning on the achievement of the sixth-grade students in English. The students are of two private primary schools in Amman/Jordan: Alrai School and Alsabelah School. It also scrutinizes their attitudes towards such a type of learning. The study attempts to answer the following questions: (i) What is the effect of using blended learning on the achievement of sixth-grade students in English? (ii) What is the effect fulfilled by blended learning on their attitudes towards such a type of a learning strategy? To reach the goals of the study, the researchers apply quasi-experimental method in which an achievement test is constructed and a questionnaire is prepared in order to measure both students’ ability concerning blended learning and their attitudes towards it as well. The study sample consists of 50 female students. It is distributed into two groups (each has 25 female students).The first group is the experimental group taught by using blended learning. The second one is the control group taught by the traditional method. Data is analyzed via adopting (SPSS) and the covariance analysis where (ANCOVA) is applied. The results show that there are statistically significant differences at the level of (a<0.05) between the means of the results of the two groups on the achievement test, and the difference is in favor of the experimental group.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".