Teaching Multiword Expressions in a Second-Language Context
Bibliographic record
Abstract
The discussion on multiword expressions is an unavoidable aspect of any target language. Idioms, which are part of multiword expressions in the English Language, are viewed as one of the neglected areas in the second-language classroom. This study explored how teachers from the three main levels of education in two municipalities in the Bono Region of Ghana approached the teaching of idioms. This descriptive qualitative case study examined the resources available to teachers, assessing their preferences and awareness of approaches. The findings revealed that these instructors relied primarily on the core teaching materials and sometimes on other online resources for additional support. Due to changes in the curriculum, what emerged from the study is that idioms were not part of the content that was taught at the teacher-training colleges. These results also demonstrate a strong preference for traditional techniques because of familiarity and curriculum constraints. Teachers' awareness and usage of other methods, which are cognitively motivated, are limited. The implications could be linked to pedagogy, training, and resource constraints that teachers may face. It also highlights the necessity for curriculum adjustments to cater to the inadequacies. Addressing the identified concerns will improve the teaching and learning experience, to meet the approved standards, the expectations of teachers, and the needs of students. A focus on professional development programs tailored toward innovative teaching practices could address the training needs of educators and create more dynamic learning opportunities for learners.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".