Implementation of Local Wisdom-Based Indonesian Learning to Strengthen Pancasila Student Profiles (P5): Case Studies in Vocational High Schools
Bibliographic record
Abstract
The importance of strengthening Pancasila values in vocational students necessitates a holistic learning approach; one such effort is to integrate Indonesian language learning with local wisdom to instil national identity in students and prepare the younger generation to face global challenges. As a result, the purpose of this research was to describe the implementation of Indonesian language learning based on local wisdom in Vocational High Schools to raise the profile of Pancasila students. This study is part of a qualitative research design using the case study method. Residents of vocational high schools in Central Java made up the research respondents. Data was gathered by interview and observation techniques, then interactively and descriptively analysed. The findings of the study revealed that school management, from the principal to the teachers, was completely dedicated to adopting the Merdeka curriculum as a pilot project by incorporating local wisdom-based Indonesian language learning processes. Even though there are some flaws, the learning process based on local wisdom that has been applied thus far appears to be functioning almost perfectly. Furthermore, students benefit from local wisdom-based learning since it is seen as an adaptive model for accommodating competencies that strengthen the student profile of Pancasila as the foundation and identity of the Indonesian nation. However, improvements and optimisation are still required to implement this Merdeka curriculum to ensure the results can be even better in assisting students in mastering subject matter, particularly the Indonesian language subject matter, and providing a foundation and knowledge of Indonesian wisdom culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".