The Effect of Teaching Vocabulary through Semantic Mapping on EFL Learners’ Awareness of the Affective Dimensions of Deep Vocabulary Knowledge
Bibliographic record
Abstract
This study focused on the effect of teaching vocabulary through semantic mapping on the awareness of two affective dimensions, evaluation and potency dimensions of deep vocabulary knowledge as well as the general vocabulary knowledge of EFL students. Sixty intermediate EFL female adult learners participated in this study; they were chosen among 90 students through Prelemenary English test and a general vocabulary knowledge test. They were thus randomly divided into two group, experimental and control, each consisting of 30 students. As for the treatment, modifiers describing peoples’ characteristics were taught in the text and through semantic mapping, whereas these words were taught by usual vocabulary instruction in control group. At the end, students took a vocabulary achievement test and a test of awareness of evaluation and potency dimensions of deep vocabulary knowledge. A t-test was run to analyze the data from the vocabulary achievement test. Results showed that teaching collocations has great influence on the students` general vocabulary knowledge. To see if the independent variable had significant effects on awareness of evaluation and potency dimensions of deep vocabulary knowledge, a MANOVA was run revealing that teaching vocabularies through semantic mapping significantly improved learners` awareness of the two dimensions.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".