Developing a Framework for Classroom Lesson Delivery to Improve English Teachers’ Performance in the Foundation Year Programme at a Saudi University
Bibliographic record
Abstract
The current English programme provided to foundation year students at King Abdulaziz University is failing to equip learners with the desired level of English language. This paper assesses the teaching materials and proposes a teaching framework to improve teachers’ lesson delivery. The framework was designed to overcome some of the shortfalls in the mandatory course book series (Headway Plus). The framework adopted Kumaravadivelu’s (2010) principles for lesson delivery: diagnosis, treatment and assessment, and advocates the development of cognitive ability, encourages collaborative learning, problem solving, emphasizes process rather than product, communication skills, and self-inquiry. Fifteen English teachers participated in a pilot study by implementing the new lesson delivery framework over a 14-week course. A peer observation strategy was used to help the teachers reflect on their teaching methods and improve the quality of their teaching. Also, during the last two weeks of the course the head of the English unit and an external observer conducted observations to monitor and assess teachers’ development. The findings of the study revealed the new framework helped teachers improve their lesson delivery and encouraged them to use new teaching techniques such as problem solving and a student-centred approach. However, the results also indicated there was a need for teacher training courses to help teachers understand the principles behind the different teaching methodology. In addition, a cultural factor was found to be influential, as some teachers were reserved about criticizing other teachers’ performance.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".