Drama as Dialogue: Reflective Practitioner Insights on Teaching English-Language Plays in Saudi Higher Education
Bibliographic record
Abstract
This paper explores the pedagogical possibilities and challenges of teaching English-language drama in Saudi Arabian universities, where English functions as the medium of instruction in literature courses but is not students’ first language. While drama is often included in English literature curricula, it receives limited attention as a distinct mode of literary engagement with unique educational affordances. Drawing on theoretical perspectives from literature education and language pedagogy, as well as reflective practitioner insights drawn from the author’s classroom experience teaching plays such as Ibsen’s A Doll’s House and Shaw’s Pygmalion, Arms and the Man, and Major Barbara, this paper argues that drama offers valuable opportunities for oracy development, interpretive reading, and dialogic learning—especially in multilingual contexts. The discussion highlights how the performative and collaborative nature of drama can support deeper student engagement with character, voice, and theme. At the same time, it considers the linguistic, cultural, and institutional constraints that shape drama instruction in Saudi higher education, including the sociolinguistic environment, assessment practices, and classroom norms. By reflecting on drama as both text and practice, the paper invites a reimagining of how literature is taught in English-medium classrooms, proposing drama as a dynamic space for critical inquiry, linguistic expression, and cultural dialogue. The Saudi context provides a lens through which broader questions about literary pedagogy, language policy, and multilingual education in the university setting can be reconsidered.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".