Teaching and Learning Vocabulary through Reading as a Social Practice in Saudi Universities
Bibliographic record
Abstract
The study explores the social practice of vocabulary learning by examining vocabulary teaching techniques employed by teachers, the vocabulary learning strategies (VLSs) identified by students as most useful and the ones they felt most competent in using when reading and teachers’ and students’ attitudes towards learning vocabulary through reading. While most vocabulary research is quantitative, this study used a mixed methods approach of quantitative and qualitative data collected from a range of sources. One hundred and fifty students majoring in English from four different universities completed a semi-structured questionnaire and twenty-two of them were interviewed. In addition, nine teachers of vocabulary and reading subjects were interviewed and their classes observed. A systematic analysis for the prescribed textbooks was also conducted. The findings revealed that both teachers and students were negotiating their autonomy on an ongoing basis, which means that the social context of learning has a powerful influence on what students learn. The study concludes that vocabulary learning is a social practice influenced by a range of factors, such as teaching techniques, VLSs, the prescribed textbook, participants’ beliefs and attitudes, learners’ interests, cultural values and learners’ level of competence in English.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".