Reflections by Some Jordanian EFL Lecturers on Salmon’s Five-Stage E-Learning Model and Its Possible Application to Teaching English in Jordan
Bibliographic record
Abstract
This study attempted to evaluate Gilly Salmon’s Five-stage e-learning Model and its possible contribution to learning English language skills by surveying the related literature and obtaining perspectives of some EFL lecturers in Jordan during the 1st semester, 2018–2019. A convenient sample of twenty EFL lecturers participated in a semi-structured interview to reflect on the contribution of the five-stage model to English language instruction. The study revealed some strengths and drawbacks of the above model. While acknowledging the existence of several positive attributes of this model such as exhibiting coherence and being structural and developmental and featuring the engagement of learners via collaborative language learning, this model, according to some EFL specialists, demands further improvement to highlight, for instance, face-to-face mode of language instruction and to be more spiral and bi-directional. The study called for integrating assessment into the model to monitor learner’s learning progress. It also called for achieving independent language learning and enabling learners to transfer their learning beyond the model’s final stage of development. It was suggested that the above model should be modified to account more adequately for online English language 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".