Semantic Mapping or Rote Memorisation: Which Strategy Is More Effective for Students’ Acquisition and Memorization of L2 Vocabulary?
Bibliographic record
Abstract
The present paper aimed to compare the influence of two vocabulary teaching strategies on students’ vocabulary retention—roughly used in this paper to refer to the process of acquisition and memorisation. In particular, the strategies of semantic mapping and rote memorisation were compared and contrasted within a trail of evidence-based data gathered systematically from two ESL classes in an international school in the Emirate of Sharjah in the United Arab Emirates (UAE). The participants of the study were 30 male students who were in grade 12, the last stage of high school in the UAE educational system. The participants were randomly divided into two groups: a control group and an experimental group. In order to measure the impact of the two strategies under investigation on the students’ vocabulary retention, the two groups sat for a pre-test and a posttest. The intervention that took place between the two tests lasted for three weeks. The results showed that the students’ retrieval of the target vocabulary words improved as a result of implementing both strategies, but that the improvement which resulted from the use of semantic mapping overrode that which ensued from rote memorisation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".