Investigating Strategies for Enhancing Appropriate Nominalisation by Selected Grade 7 English First Additional Language Learners: Morphological Perspective
Bibliographic record
Abstract
Nominalisation is an indispensable process in written and verbal communication. However, nominalising words is a daunting exercise for learners with limited exposure to English. This study investigated strategies for enhancing appropriate nominalisation by selected Grade 7 English First Additional Language (EFAL) learners. This topic has been chosen because the nominalisation process challenges these learners. The paper was underpinned by Halliday’s (1985) grammatical metaphor and Jackendoff’s parallel architecture theories relevant to the study. The objectives were to identify, describe and evaluate suitable strategies for dealing with the nominalisation process by selected Grade 7 EFAL learners at primary schools. This study employed a quantitative research approach coupled with a descriptive research design. Using a simple random sampling technique, 47 learners participated in this study. Data were collected using structured questionnaires. A pilot study was conducted on 10 learners, not from the target group. The Statistical Package for Social Sciences version 29 was employed to analyse the findings due to its new achievements towards data interpretation. The preliminary results showed that some learners could not nominalise English words appropriately. However, the main findings revealed that learners performed preternaturally after utilising ‘written and spoken instruction’, ‘vocabulary learning’, ‘comprehensible input’ and ‘language output’ strategies. The study implies that the identified strategies are indispensable during nominalisation. Future researchers can conduct further studies on the current topic. This study recommends frequently using the identified strategies to enhance appropriate nominalisation by selected Grade 7 EFAL 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".