Teaching Writing within the Common European Framework of Reference (CEFR): A Supplement Asynchronous Blended Learning Approach in an EFL Undergraduate Course in Egypt
Bibliographic record
Abstract
Based on the Common European Framework of Reference (CEFR) and following a blended learning approach (a supplement model), this article reports on a quasi-experiment where writing was taught evenly with other language skills in everyday language contexts and where asynchronous online activities were required from students to extend learning beyond classroom hours. The experiment was carried out with freshmen in one of the Egyptian private universities. Twenty one pre-intermediate level students represented the experimental group that was taught the new CEFR course, and twenty six other students of the same level represented the control group that was taught the traditional face to face academically contextualized course. A pre and post writing tests were used to reveal students’ writing proficiency before and after the experiment, and a t test was also used to measure the development of each group to find out whether their writing has developed after tutoring or not. Results indicated that the experimental group transcended the control group in 70% of the rubrics used to grade students’ writing, and when measuring the results of the experimental group in the pretest and the posttest, there was a significant development in their writing proficiency level. This experiment is considered a step towards developing students’ learning techniques in the institution; henceforth in the country. The experiment is one of the leading initiatives to teach English as a Foreign Language according to CEFR following a blended learning approach to undergraduate students in Egypt.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".