The Impact of Using Sitcoms in English Language Teaching: A Literature Review
Bibliographic record
Abstract
Sitcoms, also known as Situation Comedies, are a genre of comedy that has been broadcast on television for many years. Sitcoms have not only been used to entertain viewers but have also become an effective strategy for teaching English as a foreign language. This article presents a literature review focused on the impact of using sitcoms (situation comedies) on the development of communication skills and intercultural competencies in students. The objective was to critically analyze 18 academic articles related to this topic in 11 countries. To collect information, an exhaustive search was conducted in academic databases, and studies addressing the effective use of sitcoms in the classroom were selected. The results indicated that the strategic integration of sitcoms significantly improves speaking and listening skills, as well as cultural understanding and vocabulary learning. The beneficial effects of sitcoms are mainly highlighted in students’ speaking skills development, as they enhance stress, intonation, vocabulary, and the enrichment of new expressions. Regarding the development of intercultural competencies, students gain a better understanding of cultural differences and avoid stereotypes. Sitcom exposure also improves listening comprehension, which helps with language acquisition in general. It was concluded that sitcoms can have a positive role in EFL classes. With their comfortable characters and humorous situations, these TV comedies are more than just entertaining. They are effective teaching tools that enhance language skills at all levels. The suggestion is that teachers investigate, modify, and use this genre of comedy to strengthen their students' EFL learning experiences. These strategies, which have been successful in other European and Asian countries, have not yet been implemented in Colombia.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".