Shifting Learning Atmosphere through Process Drama: Teaching English Parts-of-Speech (PoS) in Indian Classroom
Bibliographic record
Abstract
This paper investigates the effectiveness of process drama in teaching English parts-of-speech to middle school students in an eastern Indian school. For the application part, researchers developed process drama-based lesson plans following the structural approach and implemented them among the students of class VII studying English as a second language (L2). The study employed a quasi-experimental design, with a pretest-posttest approach to data collection. Additionally, the facilitator consistently took observational field notes to understand the utility and limitations of process drama in a second-language classroom. The study's major findings indicate a significant growth in the treatment group and showed seminal benefits over the traditional method of teaching parts-of-speech through the structural approach. Moreover, observation and field notes indicated the welcoming attitude of learners towards process drama-based language pedagogy. Also, observation and field notes showed assistance in understanding the utility and limitations of process drama as a pedagogical tool in an L2 classroom. Thus, findings of this study have implications for language educators, curriculum designers, and policymakers, offering valuable insights and practical recommendations for integrating process drama in L2 teaching methodologies in diverse educational settings. Additionally, this research contributes to the ongoing discourse on innovative language teaching techniques, catering to the needs of the diverse student populations in language classroom.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".