Exploring Indonesian Vocational Students’ Perspectives on Deep Learning in English Language Education
Bibliographic record
Abstract
The potential of deep learning in the context of English teaching is essential so as to increase student engagement and learning outcomes through some innovative learning models. However, few studies address the effect of deep learning in English classrooms from vocational high school students’ perspectives, mainly in Indonesian context. This study aims to disclose the students’ perceptions on the implementation of deep learning at vocational high schools, considering quantitative and qualitative data. The participants of this study are 191 students from four vocational high schools in Eastern Jakarta, Indonesia. The study employed a mixed-methods design, combining quantitative and qualitative data. The data were collected through an online questionnaire, comprising closed and open-ended questions. The data were then analyzed with descriptive statistics and thematic analysis. The results show that students have positive responses on their mindfulness, meaningfulness, and the enjoyment in English language teaching. Qualitatively, students found English very important for their career and considered the learning materials provided by English teachers relevant. Additionally, the students felt motivated to learn English for the enhancement of creative and critical thinking. This study recommends refining deep learning practices for effective and meaningful English learning environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".