The Use of Humourous Texts in Improving ESL Learners’ Vocabulary Comprehension and Retention
Bibliographic record
Abstract
Successful language acquisition requires extensive word knowledge. However, learners are reportedly unable to increase their word knowledge due to insufficient meaningful input in the language classrooms. This paper intended to present another tool to encourage learners’ vocabulary development. It examined the effect(s) of using short narrative jokes on ESL learners’ word comprehension and retention. The study involved an experiment in which two intact groups of tertiary students attended four reading sessions. In each reading session, before they began reading, the participants were given a vocabulary test (pre-test) to measure vocabulary recognition of the target words. The experimental group was then exposed to short humourous text while the control group was exposed to comparable non-humourous text. After each reading session, the participants were given immediate vocabulary test (post-test 1) to measure vocabulary comprehension. After a week, the participants were given delayed vocabulary test (post-test 2) to measure vocabulary retention. The participants’ gain scores were assessed by comparing their post-test 1 to pre-test score. Lastly, the gain scores and scores in delayed vocabulary test of the two groups were compared using t test. The findings of this study indicated that humour could relatively influence word comprehension and retention. One of its implications is for language teachers to include humour in vocabulary teaching and learning.
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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".