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Record W1535068680 · doi:10.5539/elt.v8n9p104

The Use of Humourous Texts in Improving ESL Learners’ Vocabulary Comprehension and Retention

2015· article· en· W1535068680 on OpenAlexvenueno aff
Nursyafiqah Zabidin

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyTest (biology)Reading comprehensionPsychologyVocabulary developmentSession (web analytics)Reading (process)ComprehensionWord recognitionLinguisticsMathematics educationTeaching methodComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.313
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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