Teaching Vocabulary: The Relationship between Techniques of Teaching and Strategies of Learning New Vocabulary Items
Bibliographic record
Abstract
This study aims to investigate the techniques of teaching new lexis which are adopted by non-native teachers of English language. It also aims to investigate the strategies of learning new lexis which are adopted by learners in relation to their level. The work is based on two hypotheses: It is hypothesized that there is a relationship between the techniques and strategies which are used for teaching and learning new English lexis.It is hypothesized that the level of learner, might not affect his or her choice for a particular strategy. To test these hypotheses, the researcher has chosen a purposive sample: the pupils of the seventh class, the eighth class and teachers of English language at the Basic level schools, in the River Nile State, Sudan, in the school year 2014. The instruments which were used to collect data, were two questionnaires (a teachers’ version and a pupils’ version). To analyze and interpret the data percentages and Chi Square were used. The results showed that there is a relationship between the techniques of teaching and the strategies of learning new lexis.The chi-square test showed that, the results were statistically significant at level 0.05 and this supports the second hypothesis that the learner’s stage of learning does not affect his or her choice of a particular strategy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".