Gauging the Interactive Language Learning to improve English Communication Skills Among Vocational High School Students
Bibliographic record
Abstract
Enhancing English language skills in Vocational High Schools (SMK) is crucial for preparing students to meet the demands of globalization and the workforce. This study aims to evaluate the effectiveness of communication-based learning methods in comparison to lecture, project, and game-based methods in improving students' scores on the English Proficiency Test (EPT). The research employs a quasi-experimental design with a post-test only approach, involving 53 students divided into four groups according to the learning methods applied. Data were collected through the EPT, and data analysis was conducted using One-Way ANOVA and Tukey's Post Hoc test. The findings indicate that the communication-based learning method is significantly more effective in enhancing EPT scores than the other methods, achieving the highest average score of 389.29. Additionally, both project-based and game-based methods also demonstrated significant improvements compared to the lecture method. The conclusion of this study underscores the importance of innovation in education, advocating for the integration of methods that combine communication, play, and technology. A technology-assisted edutainment Co-Trainers program is recommended as an innovative solution to create an interactive and effective learning environment, equipping students to face an increasingly competitive job market.
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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 | 0.004 |
| 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".