English for Ecotourism and Its Sustainability with Augmented Reality Technology
Bibliographic record
Abstract
When it comes to traveling, more and more people are becoming interested in having profound experiences to the places they are visiting and the local inhabitants. However, there currently very few tour guides in Taiwan who are well equipped to promote preservation of the environment and sustainable development. In addition, Taiwanese students interested in the field have been found to rarely talk with foreigners about topics related to ecology or environmental protection even though they are popular issues around the world. This study centres on English for ecotourism that is supplemented by comprehensive project-based learning (CPBL) and augmented reality (AR) technology to explore how teaching such a course using AR technology impacts English for specific purposes (ESP) learning and sustainable development. Two classes of ninety-nine college students in total participated in the study. The research also involved a survey, comprised of three sets of questionnaires concerning student satisfaction with AR application, CPBL, and ESP learning. An independent t-test and an analysis of variance were completed to examine the variables of gender, class, and English proficiency level to understand the significance of student satisfaction. The results found that more tourists chose the AR versions of the brochures than the general versions. Satisfaction between both foreign language classes regarding CPBL and ESP learning was significant. Across the three different English proficiency levels, the mean level of student satisfaction for all three variables was highest in the high proficiency group. This study reveals that adopting an AR approach for CPBL and ESP learning could better achieve the goals of ESP teaching and sustainable development than the traditional English teaching model based on in-class lectures.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".