The Effect of Pre-teaching Vocabulary and Collocations on the Writing Development of Advanced Students
Bibliographic record
Abstract
This study homes in on the lexical aspects of the language shedding light on the effect of pre-teaching vocabulary and collocations on writing development. It was believed that pre-teaching relevant vocabulary and collocations could be used to improve the learners` writing ability, and the researchers have assumed that learners` L2 writing can be developed over time, in response to instruction, feedback, and practice. Forty advanced students in Goldis Institute participated in the study. Two groups were randomly determined as the control and the experimental groups, each with twenty subjects. A pretest was administered to both groups to ensure the initial equivalence of the groups. After twenty sessions of instruction and five composition tests at regular intervals, the researchers found out that the students in experimental group were more successful than those in the control group who used traditional methods. As statistical analysis of the post-test compositions shows, there is significant difference between the two groups indicating that pre-teaching vocabulary and collocations can be a useful means of helping students to improve their writing quality. Results also show that the L2 writing of these students does indeed develop over time, with notable improvements in a number of features.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".