The Effect of Humor-Integrated Pictures Using Quizlet on Vocabulary Learning of EFL Learners
Bibliographic record
Abstract
The recent improvements in technology and their integration in language learning have played a facilitating role invocabulary acquisition. Quizlet, an online teacher-/student-friendly tool, is one of the leading applications invocabulary acquisition. Along with the effectiveness of visualization in acquiring vocabulary, humor has been alsoextensively indicated to carry a significant role in language learning. With all its facilitating features, the integrationof technology, humor, and vocabulary can be achieved via Quizlet. In this study, the visual integration of humoraccompanying vocabulary on Quizlet was taken into scrutiny to see to what extent humor-integrated pictures onQuizlet account for the retention of vocabulary acquisition. With this purpose, this study examined the effect ofhumor-integrated pictures on vocabulary acquisition of 45 intermediate English as a foreign language (EFL) learnerson Quizlet. In so doing, the experimental group received a series of unknown vocabulary items for which theintegrated pictures were humorous, while the vocabulary items assigned for the control group were identical, but innon-humorous contexts. At the end, an independent samples t-test applied on the scores achieved from a posttestindicated a significant difference in scores of the control group and that of the experimental group. In fact, thelearners in the experimental group significantly outperformed their counterparts in the control group. The resultsindicated that linking vocabulary items with humorous pictures is more effective than using non-humorous context inlearning vocabulary. Apparently, as the results indicate, the significant effectiveness of technology in vocabularylearning can be boosted with the help of humorous context. The findings shed light on the importance of technologyin language learning and its linking with humor in vocabulary 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".