The Effect of Meta-cognitive Vocabulary Strategy Training on the Breadth of Vocabulary Knowledge
Bibliographic record
Abstract
A significant amount of research in Second Language Acquisition (SLA) manifests that not all StrategyBased Instruction (SBI) studies have been beneficial in driving interlanguage development forward. The purpose of the current study was to investigatethe effect of metacognitive vocabulary strategy training on the breadth of vocabulary knowledge among a group of intermediate Iranian EFL students. In so doing, two intact classes at Islamic Azad University of Hamedan in Iran were selected as the participants of the study. The total number of students were sixty students with thity students in each of the experimental and control group. The participants were randomly assigned to control group and experimental group. Vocabulary Levels Test (VLT) as an instrument for measuring the breadth of vocabulary knowledge was administered before (as pretest) and after (as post-test) the treatment of the study. Both groups worked on the same reading passages and textbook. In addition, the students in experimental group were also taught in meta-cognitive vocabulary learning strategies while the students in control group received traditional teaching without any treatment for 12 sessions. The result of one-way ANCOVA indicated that meta-cognitive vocabulary strategy trainingwas beneficial in enhancingthe breadth of vocabulary knowledge of students.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".