Bibliographic record
Abstract
This research, using Latin Square Design experiment, aims to examine the effects of different presentation modes on EFL students’ vocabulary learning and retention. 141 Chinese freshmen of undergraduates were divided into three groups and participated in an experiment. Materials consisted of reading comprehension and Nation’s Vocabulary Levels Test for pretests and the posttests were composed of instant vocabulary test, delayed vocabulary test 1 (Word Association Test), and test 2 (Vocabulary Knowledge Levels Test designed by Wesche and Paribakht). Results indicated that there were no significant differences between the effects of the three modes (vocabulary list, picture mode and incidental context) on students’ vocabulary learning and short-term recall, and that the three modes significantly affected students’ long-term vocabulary retention with the differences between receptive vocabulary knowledge and productive one. In addition, their effects on the acquisition of words with different parts of speech were not always the same. All in all, picture mode had most significant positive effect on vocabulary recall and production/output, but there was no significant difference between vocabulary list and accidental context. Reasons for the results were explained from the perspectives of modal theory, condition limitations for L2 learning and advantage of context for picture presentation. Finally, implications for L2 vocabulary teaching were also discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".