A Study of Problem-Based Flipped Learning of Indonesian Vocational High School Students
Bibliographic record
Abstract
The integration of innovative learning models is essential for preparing vocational high school (VHS) students with 21st-century skills. This study aimed to explore the need for a Problem-Based Flipped Learning (PBFL) model with a Science Technology Engineering Mathematics (STEM) approach tailored to the needs of vocational high school students. Employing a qualitative descriptive method, the study involved 33 vocational high-school students and three mathematics teachers. Data was collected through interviews, observations, and questionnaires, with validity using triangulation. The results indicated that 87.9% of students endorsed the incorporation of PBFL-STEM, highlighting its efficacy in promoting collaboration and critical thinking. Educators emphasized its capacity to bridge theory and practice for enhanced industry alignment. Nonetheless, insufficient technology infrastructure and inadequate teacher preparation have been recognized as impediments to effective adoption. To tackle these issues, this study employed adaptive solutions, including the utilization of readily available technology (e.g., smartphones) and the provision of continuous teacher mentorship for better implementation. This research identified that the PBFL-STEM approach is extremely pertinent for vocational high schools, providing significant possibilities to cultivate students' 21st-century talents. This study showed that vocational schools can quickly fix these problems by encouraging teachers to improve their skills and ensuring that they understand the importance of new ways of learning for giving students a good 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".