Interactive App-Based Games for Bilingual Education: Advancing English Proficiency and Promoting Digital Sustainability in Physical Education
Bibliographic record
Abstract
This study presents a dataset and analysis on the integration of interactive app-based games in bilingual Physical Education (PE) settings, aiming to enhance English proficiency and promote sustainable learning practices. By utilizing digital technologies, the research explores how these tools support language acquisition while fostering environmentally responsible education. Grounded in bilingual education theories and digital sustainability principles, the study emphasizes resource-efficient educational technologies. It also aligns with the FAIR Data Principles, ensuring that the generated data is findable, accessible, interoperable, and reusable, contributing to open science. A mixed-methods approach was used to collect quantitative data on English proficiency and PE conceptual understanding, complemented by qualitative data from observations, interviews, and focus groups. The study involved 120 elementary school students from grades 3 to 5 and six PE teachers from three different schools, focusing on the effectiveness of app-based games in bilingual education and their role in sustainable teaching and learning. The dataset indicates statistically significant improvements in English proficiency and PE conceptual understanding among participants. Additionally, the findings highlight the environmental and social implications of integrating digital technologies in bilingual PE settings. This study demonstrates how app-based games align with global educational goals by minimizing environmental footprints and fostering inclusive learning environments. The dataset offers insights into the dual academic and environmental benefits of digital tools in bilingual education, providing a scalable and adaptable model for integrating digital sustainability into curricula. By bridging bilingual education, sustainable learning, and digital innovation, this study contributes to discussions on the evolving role of technology in education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".