Second Language Writing and Assessment: Voices from Within the Saudi EFL Context
Bibliographic record
Abstract
This small scale, quantitatively based, research study aimed at exploring one of the most debated areas in the field of Teaching English to Speakers of Other Languages (TESOL); and that is, the perceptions and attitudes of English as a Foreign Language (EFL) teachers as well as EFL learners at an English Language Institute (ELI) at a major university in the Western region of Saudi Arabia, King Abdulaziz University, towards second language writing assessment. The research study involved, randomly selected twenty-two EFL teachers and seventy-eight EFL students between the period of September 2016 and December 2016. Two, purposefully designed, twenty-item, Likert scale questionnaires were distributed amongst the teachers and students. One for the participating EFL teachers and one for the participating EFL students. Data analysis using descriptive statistical methods indicated several concerns which EFL teachers and students have with regards to the writing assessment in general and to the obstacles EFL teachers face when teaching and assessing writing. In addition, there was an indication of general resentments and strong feelings amongst the EFL students where the majority indicated that they are sometimes graded unfairly and writing assessment should take another, more holistic approach rather a narrow one. The study makes recommendations for future research.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".