Integrating Storynomics into Language Education to Enhance Speaking Skills of Tourism Students in Bali, Indonesia
Bibliographic record
Abstract
Speaking is a highly prioritized skill in English language learning, particularly in tourism. Storynomics is an approach in tourism that emphasizes storytelling, creative content, and living culture, utilizing culture's power as a destination's fundamental essence. This study aims to examine how the storynomics strategy can promote the’ speaking competence of tourism students in Bali. The study employed Research and Development (R&D) with an Exploratory Mixed Method design. Quasi-experimental research was applied to determine the effectiveness of the material design for students in the Tourism Management Department. The students were divided into two groups of 30 students in each group. The data were obtained through pretest and posttest speaking tests after the treatment was given. The result of the test indicated there is a significant difference between the two groups. The experimental group taught the storynomics strategy throughout the study to promote speaking ability, while the control group continued with conventional methods. At the end of treatment, a posttest was conducted in two groups, and the test results were compared. Statistical analysis showed that the experimental group obtained a higher average score in the posttest with an N-gain score of 0.58. These findings emphasize the positive impact of implementing a storynomics strategy in learning English as a foreign language, especially for describing local culture. It is expected that further development can be made in designing other teaching materials using a cultural approach and more diverse communication strategies with other theories or approaches. In addition, a deeper analysis is needed regarding the linguistic aspects of English language learning in the field of tour guiding.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".