Students’ Perception on the Use of Humor in the Teaching of English as a Second Language in Nigeria
Bibliographic record
Abstract
Learners’ perceptions about foreign language or second language are becoming issue of attention from various scholars in applied linguistics in recent time (Ayman, 2012; Masoumeh, 2012). The effective teaching of English as Second Language (ESL) is one of the major concerns in Applied Linguistics. Hence, the paper was set out to investigate the perceptions of the use of humor in the teaching of English as L2 on the students of a Tertiary Institution in Nigeria. This approach unraveled the implication of humor both from the positive and negative sides. The paper reflected the relationship between culture and humor as reported by students. It was therefore suggested that the use of humor in the teaching of English as second language (ESL) should be done by teachers with care considering the fact that the linguistic environment is cross cultural, so that the purpose of effective teaching of ESL to learners will be achieved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".