Vocabulary Facilitation on Technical Modules for ESL Learners: A Case Study of a Sri Lankan Higher Educational Institute
Bibliographic record
Abstract
This study focuses on the degree of facilitation of the English language module on a technical module offered for adegree program in a higher educational institute in Sri Lanka. The sample consists of 5,855 words from one technicalmodule in the stream of Accounting and Finance and 10,554 words from one English language module prescribed forthe BBA (Special) Degree program during the first year first semester of undergraduates. The level of facilitation wasmeasured in terms of vocabulary; an essential component found in the empirical literature to acquire the technicalknowledge in tertiary education. In order to achieve the main objective, “whether or not the language modulefacilitates the technical module” the researchers utilized Academic Word List (AWL) and examined the presence ofAWL items in both modules and compared the common distribution of AWL items. The results showed 12.33%presence of AWL items in the technical module and 3.95% in the language module. 65 AWL word families wereidentified as common to both modules. The facilitation of the language module on the technical module in terms ofvocabulary is 42.20%. Interestingly, the most frequently used 10 AWL items are not common to both modules.Collocation and gap making can be suggested as appropriate vocabulary activities in order to enhance the exposureof the ESL learners to vocabulary.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".