The Effects of the Online versus Face-to-face (F2F) Modes of Teaching on the Academic Achievement of EFL Learners in Writing Skills Courses
Bibliographic record
Abstract
English as a Foreign Language (EFL) practitioners regard writing as one of the most innovative discrete skills to teach. Many researchers examined the writing difficulties of EFL students and provided resolutions and guidance. Although the viability of the planned objectives was re-examined before any conclusions were made. However, this research aimed to investigate the effect of online and face-to-face (F2F) teaching methods on students’ academic performance in writing skills. The participants, N=44, were divided into two groups A and B and belonged to the English department in the second semester at Najran University, Najran, KSA. The controlled group A received online instruction, whereas the experimental group B received face-to-face instruction. A quasi-experimental study design was employed using the pre-test and the post-test research instruments. A test was administered to two groups to measure their levels of homogeneity at the beginning of the semester. Another test was then administered to the same groups after the first semester of teaching. In order to analyze the data, the SPSS program was used. According to the results, the F2F intervention improved student performance over the online mode. The F2F mode of participation was more comfortable and engaging for participants than the online mode. Additionally, F2F discussion produced better writing performances from the students than online communication does. Thus, despite certain benefits associated with F2F learning, further research is required in order to fully understand how F2F teaching approaches affect English learners' academic achievement in 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".