Vocabulary Index as a Sustainable Resource for Teaching Extended Writing in the Post-Pandemic Era
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, Malaysian English teachers identified a pressing need to support upper primary school pupils, particularly those in the upper levels, in the effective composition of extended writing. Additionally, these educators required more innovative methodologies for teaching vocabulary in this context. Consequently, the current study aimed to develop a vocabulary index as a suggested resource for Malaysian English teachers instructing upper primary school pupils on extended writing. To achieve this, a quantitative computational research strategy and corpus-driven research design were employed. A purposive sampling technique was used to select 560 advanced upper primary school pupils from 28 schools, each with high English performance in the capital of each state and the federal territory of Malaysia, who produced a total of 152,187 words in extended writing for analysis. LancsBox, a primary computational linguistics application, was used for data processing. Given that the vocabulary index for extended writing necessitates a more comprehensive coverage of vocabulary, functional and content words were included, and keywords, raw and normalised frequencies were analysed and reported. Through the vocabulary index built in this study, the researchers found English teachers in Malaysia should utilise local issues in writing prompts, emphasise the use of both positive and negative adjectives, introduce complex sentence structures to enhance pupils’ writing abilities and also train pupils to organise the ideas in their writing. Future linguistic studies could replicate the present investigation, so that it can respond to their classroom needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".